The DOI matches its Crossref record; 75 of the 76 references it cites were found in a registry or an index; none of the 80 sources its claims cite is checked for support yet.
Radiomics: Images Are More than Pictures, They Are Data
Open accessAbstract
In the past decade, the field of medical image analysis has grown exponentially, with an increased number of pattern recognition tools and an increase in data set sizes. These advances have facilitated the development of processes for high-throughput extraction of quantitative features that result in the conversion of images into mineable data and the subsequent analysis of these data for decision support; this practice is termed radiomics. This is in contrast to the traditional practice of treating medical images as pictures intended solely for visual interpretation. Radiomic data contain first-, second-, and higher-order statistics. These data are combined with other patient data and are mined with sophisticated bioinformatics tools to develop models that may potentially improve diagnostic, prognostic, and predictive accuracy. Because radiomics analyses are intended to be conducted with standard of care images, it is conceivable that conversion of digital images to mineable data will eventually become routine practice. This report describes the process of radiomics, its challenges, and its potential power to facilitate better clinical decision making, particularly in the care of patients with cancer.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
Radiomics converts digital medical images into high-dimensional, mineable quantitative data and mines those data, often with clinical and genomic covariates, to support diagnosis, prognosis, and treatment decisions. This review describes the radiomics workflow, highlights oncology examples, and outlines major challenges including reproducibility, standardization, and data sharing.
Key findings
- Radiomics is defined as the conversion of images to higher-dimensional data and subsequent mining for decision support; it differs from CAD systems by extracting many quantitative features rather than delivering a single detection/diagnosis answer.
- Feature extraction has expanded from 182 texture plus 22 semantic features for lung cancer CT to sets of 442, 662, and 522 features; a covariance matrix example used 219 features extracted from non–small cell lung cancer tumors in 235 patients.
- Selected clinical examples report that Haralick texture analysis in 147 men with biopsy-proven prostate cancer differentiated benign from cancerous tissue, and follow-up work distinguished Gleason score 6 from 7 or more with 93% accuracy and 4+3 from 3+4 with 92% accuracy; in 49 prostate cancer patients, texture analysis improved AUC from 0.82–0.85 to 0.92–0.96.
- Curation of a 143-patient non–small cell lung cancer dataset required an initial 285 candidates (approximately 50% attrition) and 430 hours of processing, or approximately 3 hours per patient; the paper also notes histopathology error rates can be as high as 23%.
Methods
- Narrative review/report in *Radiology*; it describes the radiomics process—image acquisition, volume-of-interest identification, segmentation, feature extraction/qualification, database building, and classifier modeling—rather than presenting a primary study.
- Evidence is drawn from selected published radiomics and radiogenomics studies, mostly in oncology, using CT, MR imaging, and PET; examples include prostate, lung, glioma, hepatocellular carcinoma, breast, and bladder cancer.
- Main analysis is conceptual synthesis and illustrative reporting of selected quantitative results; no systematic search strategy, meta-analysis, or formal risk-of-bias assessment is described.
Limitations
- Stated in the paper: reproducibility problems, overfitting, poor study design (especially mixing hypothesis generation with hypothesis testing), lack of validation standards, incomplete reporting, and unrecognized confounding variables, particularly in retrospective databases; independent validation and prospective multicenter trials are needed.
- Stated in the paper: segmentation is the most critical, challenging, and contentious component, with high interoperator variability in manual contours; data sharing faces cultural, administrative, regulatory, personal, and infrastructure barriers.
- Evident from the text: as a narrative review, the cited examples are selected rather than systematically identified, and the paper does not report a formal review protocol or quantitative synthesis.
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Claims the paper makes
60 Not checked yet · 42 No basis given · 39 Own interpretation · 1 Own method · 1 Own result
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Notably, when this is performed, the combinations reside in spatially explicit regions of the tumors (Fig 2).”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Volume of Interest Identification
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Genomic heterogeneity within tumors and across metastatic tumor sites in the same patient is the major cause of treatment failure and emergence of therapy resistance (19).”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
What the sources say
- Not checked yetcites [19] source
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In the paper
“This is important, as it is estimated that the error rate of cancer histopathology can be as high as 23% (20–23).”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
What the sources say
hedged- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It also could provide confirmatory information to support histopathologic findings.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The combination of this subset of radiomic features with genomic data may increase diagnostic, prognostic, and predictive power.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“In the evaluation of lung cancer and in the evaluation of glioblastoma multiforme, radiomics has been shown to be a tool with which to assess patient prognosis (7).”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
What the sources say
- Not checked yetcites [7] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“First, a subset of the radiomic data can be used to suggest gene expression or mutation status that potentially warrants further testing.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
- Rests on an assumption: Virtually all patients with cancer undergo imaging at some point and often multiple times during their care.
- Rests on an assumption: Not all patients with cancer have their disease genomically profiled.
- Rests on an assumption: When genomic profiling is performed, it is done one time at one location and is susceptible to sampling error.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Radiomics appears to offer a nearly limitless supply of imaging biomarkers that could potentially aid cancer detection, diagnosis, assessment of prognosis, prediction of response to treatment, and monitoring of disease status.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is important because the radiomic data are derived from the entire tumor (or tumors) rather than from just a sample.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plain- Asserted: passes — The authors put it forward as true.
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The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In radiomics and elsewhere, the power of the predictive classifier model is dependent on having sufficient data.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Building Databases: Numbers Are King, Quality Is Queen
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The power of the predictive classifier model is dependent on having sufficient data.
- It may state two separate claims as one. 48
- Probably not a substantial claim. 63
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Thus, the curation of a data set of 143 patients required an initial cohort of 285 patients (approximately 50% attrition) and required 430 hours of processing, or approximately 3 hours of processing per patient.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Building Databases: Numbers Are King, Quality Is Queen
plainn = 143- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Figure 3 is a covariance matrix of 219 features extracted from CT scans in 143 patients with non–small cell lung cancer.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
plainn = 143- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“For example, research has already shown the capacity of radiomics analyses to help distinguish prostate cancer from benign prostate tissue or add information about prostate cancer aggressiveness (6).”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
What the sources say
- Not checked yetcites [6] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Second, a subset of radiomic features is not significantly related to gene expression or mutational data and, hence, has the potential to provide additional, independent information.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, precision medicine requires not only in vitro biomarkers and companion diagnostics but also spatially and temporally resolved in vivo biomarkers of tumor biology.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
In the paper
“CADcomputer-aided diagnosis and detection systems are usually standalone systems that are designated by the Food and Drug Administration for use in either the detection or diagnosis of disease (1).”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
What the sources say
- Not checked yetcites [1] Institute of Medicine I, 2012
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Unlike CADcomputer-aided diagnosis and detection systems, which are directed toward delivering a single answer (ie, presence of a lesion or cancer), radiomics is explicitly a process designed to extract a large number of quantitative features from digital images, place these data in shared databases, and subsequently mine the data for hypothesis generation, testing, or both.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plain- Asserted: passes — The authors put it forward as true.
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- Load-bearing: passes — The paper’s conclusions depend on it.
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Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, correlation of radiomic data with genomic or other -omic data could inform not only the decision about whether to test for certain gene alterations in biopsy samples but also the choice of biopsy sites.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“These features, in conjunction with the other information, can be correlated with clinical outcomes data and used for evidence-based clinical decision support (Fig 1).”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
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- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The conversion of digital medical images into mineable high-dimensional data, a process that is known as radiomics, is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology and that these relationships can be revealed via quantitative image analyses.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence.
- The authors state this as an assumption: Biomedical images contain information that reflects underlying pathophysiology and these relationships can be revealed via quantitative image analyses.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Thus, radiomics can provide important information regarding the sample genomics and can be used for cross-validation.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In this emerging era of targeted therapies, it is notable that most responses are not durable and that benefit is generally measured in months, not years.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Radiomics is designed to develop decision support tools; therefore, it involves combining radiomic data with other patient characteristics, as available, to increase the power of the decision support models.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“It is now appreciated that most clinically relevant solid tumors are highly heterogeneous at the phenotypic, physiologic, and genomic levels (13–15) and that they continue to evolve over time.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
What the sources say
plain- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Early successes of CADcomputer-aided diagnosis and detection have been greatest in breast cancer imaging (2,3).”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Quantitative image features based on intensity, shape, size or volume, and texture offer information on tumor phenotype and microenvironment (or habitat) that is distinct from that provided by clinical reports, laboratory test results, and genomic or proteomic assays.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It should be emphasized that radiomic and radiogenomic analyses can be used to identify correlations, not causes; thus, they are not expected to enable definitive assessment of genetic or other contents of tissue through imaging alone.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, these habitats change after treatment (eg, treatment with radiation and temazolamide), and the pattern of change has been observed to be predictive of response.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Volume of Interest Identification
plaincorrelational- Asserted: passes — The authors put it forward as true.
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A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This is driven by the knowledge that attempting to filter the data at input would be inefficient and would presuppose knowledge regarding the value of the features in classifier models before they were tested.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Volume of Interest Identification
hedgeddirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This approach continues to have high value, and there is a movement to capture such semantic data with the aid of computers to achieve higher interreader agreement, faster throughput, and lower variance.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Segmentation of normal structures, such as skeletal elements and organs, can now be achieved with full automation.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Segmentation
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Identification of tissue volumes of prognostic value is the core of the practice of radiology in oncology.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Volume of Interest Identification
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Ideally, robust models accommodate patient features beyond imaging.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
hedged- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The most systematic approach is to first identify features that may be redundant (ie, those that are highly correlated with one another).”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
plainn = 143- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the future, capturing images and other data prospectively and with higher quality and standards should reduce data attrition and make the process more efficient.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Building Databases: Numbers Are King, Quality Is Queen
hedgedconditionaln = 143- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“More recently, this has been expanded to 662 features that also contain Laplace transforms of Gaussian fits (46) and 522 features that include texture and fractal dimension features (48).”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
What the sources say
- Not checked yetcites [46] Grossmann P, Sci Transl Med
- Not checked yetcites [48] Pickup L, 2015
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Hence, there is a pressing need to capture such data and to share data across institutions to accumulate sufficient numbers for statistical power.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
boosted- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although these approaches use a priori knowledge through training sets, they are agnostic in that they make no assumptions about the meaning of the individual features.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Furthermore, the best models are those that can accommodate additional clinical or genomic covariates, and this increases the need for large high-quality data sets.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Building Databases: Numbers Are King, Quality Is Queen
plaindirect- Asserted: passes — The authors put it forward as true.
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It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, any disease, especially cancer, requires operator input because of inter- and intrasubject morphologic and contrast heterogeneity at the initial examination.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Segmentation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Any disease, especially cancer, requires operator input because of inter- and intrasubject morphologic and contrast heterogeneity at the initial examination.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“In practice, “semantic” and “agnostic” features are the two types of features extracted in radiomics.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Clusters of highly correlated features can be collapsed into one representative feature, usually the one with the largest intersubject variability or highest dynamic range.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
plaindirectn = 143- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The heart of radiomics is the extraction of high-dimension feature data to quantitatively describe attributes of volumes of interest.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“A further level of prioritization, described by Aerts et al (47), is to rank order features within separate categories representing different agnostic and semantic classes of features (eg, size, shape, and first-, second-, and higher-order textures).”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
What the sources say
- Not checked yetcites [47] Aerts HJ, 2014, Nat Commun source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, overall survival is a common endpoint for many studies, but this includes death from all causes, which may not be related to the disease being studied.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Overall survival includes death from all causes, which may not be related to the disease being studied.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It has been our experience that a reasonable rule of thumb is that 10 samples (patients) are needed for each feature in a model based on binary classifiers.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Building Databases: Numbers Are King, Quality Is Queen
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
- Rests on an assumption: A reasonable rule of thumb is that 10 samples (patients) are needed for each feature in a model based on binary classifiers.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is challenging because many tumors have indistinct borders.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Segmentation
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“As mentioned previously, the power of the model is entirely dependent on the size and quality of the data within the database.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“More exact endpoints include progression-free survival and disease-free survival or recurrence; however, these data are not readily available and require a dedicated abstraction effort with chart review.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Texture analysis of images was first introduced in 1973 by Haralick et al (41).”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
What the sources say
- Not checked yetcites [41] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In other words, it is not sufficient to report a number or a set of numbers derived from images; instead, we must also be able to provide error bars, as is done with every other credible laboratory measurement.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Image Acquisition
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Modern CT, MR imaging, and combined PET/CT units allow for wide variations in acquisition and image reconstruction protocols, and standardization of these protocols across medical imaging centers is typically lacking.
- Rests on an assumption: Variations in acquisition and image reconstruction parameters can introduce changes that are not due to underlying biologic effects.
None of the sources cited for this claim have been checked yet.
In the paper
“With such large complexity, there is a danger of overfitting analyses, and hence, dimensionality must be reduced by prioritizing the features (49,50).”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
What the sources say
- Not checked yetcites [49]
- Not checked yetcites [50] source
plainconditionaln = 143- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The basic philosophy, which has its foundation in process engineering, is to capture as much data as possible at the front end and use downstream database mining to identify the features with the highest prognostic value.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Volume of Interest Identification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In the final analysis, the value of feature sets is determined by their contribution to classifier models created through database mining.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While both of these approaches have merit, the best models are those that are tailored to a specific medical context and, hence, start out with a well-defined endpoint.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Errors in histopathology are due to both sampling errors and observer variability; thus, there is a great need for additional quantitative diagnostic information.”
§ Radiomics: Images Are More than Pictures, They Are Data > Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Radiomics can be performed with as few as 100 patients, although larger data sets provide more power.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Building Databases: Numbers Are King, Quality Is Queen
plaindirectn = 100- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Rests on an assumption: Radiomics can be performed with as few as 100 patients.
In the paper
“This was followed by a 442-member feature set that also contained wavelets (47).”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
What the sources say
- Not checked yetcites [47] Aerts HJ, 2014, Nat Commun source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“As these patients were not filtered for medical or demographic issues, there was no selection bias.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Building Databases: Numbers Are King, Quality Is Queen
plainn = 143- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In practice, not all information is available for all patients; hence, models should also be designed to accommodate sparse data.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“It is well known in the radiology community that contrast enhancement at MR imaging is often heterogeneous, with complex patterns.”
§ Radiomics: Images Are More than Pictures, They Are Data > Examples of Radiomics Results > Tumor Prognosis
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Such data sharing is a major initiative of the QINQuantitative Imaging Network, whose members are committed to depositing well-curated data sets into The Cancer Imaging Archive for public and private data mining efforts.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Classifier Modeling and Data Sharing
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Although this study may have been underpowered”
§ Radiomics: Images Are More than Pictures, They Are Data > Examples of Radiomics Results > Tumor Prognosis
What the sources say
- Not checked yetcites [58] source
hedgedn = 49- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“They found that combinations of 28 imaging traits could be used to reconstruct 78% of the global gene expression profiles, which in turn were linked to cell proliferation, liver synthetic function, and patient prognosis (40).”
§ Radiomics: Images Are More than Pictures, They Are Data > Examples of Radiomics Results > Tumor Prognosis
What the sources say
- Not checked yetcites [40] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In the peripheral zone of the prostate, all five features assessed (entropy, inertia, energy, correlation, and homogeneity) differed significantly between benign and cancerous tissue on both types of images; however, in the transition zone, significant differences were found for all five features on ADCapparent diffusion coefficient maps and for two features (inertia and correlation) on T2-weight”
§ Radiomics: Images Are More than Pictures, They Are Data > Examples of Radiomics Results > Enabling Diagnosis
What the sources say
- Not checked yetcites [6] source
plaincorrelationaln = 147- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The feature set was then filtered for reproducibility, yielding 18 features that were assessed in three distinct habitats.”
§ Radiomics: Images Are More than Pictures, They Are Data > Examples of Radiomics Results > Tumor Prognosis
What the sources say
- Not checked yetcites [57] source
plaindirectn = 55- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In a follow-up study, these features were used to automatically compute Gleason grade and were found to enable discrimination between cancers with a Gleason score of 6 (3+3) and those with a Gleason score of 7 of more with 93% accuracy.”
§ Radiomics: Images Are More than Pictures, They Are Data > Examples of Radiomics Results > Enabling Diagnosis
What the sources say
- Not checked yetcites [53] Fehr D, Proc Natl Acad Sci USA
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Testing of the core technology of texture analysis also is among the areas in which standards are lacking.”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Standards
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While there are guidelines for reporting, none yet exist for the reporting of quantitative imaging results, let alone for the reporting of much more complex radiomics results.”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Standards
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, the most rigorous biomarker qualification requires a prospective multicenter trial wherein the biomarker is one of the primary endpoints (62,63).”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Reproducibility
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“The standards have been adopted by the National Institutes of Health (8,70).”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Reproducibility
What the sources say
- Not checked yetcites [8]
- Not checked yetcites [70] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Collection of high-quality image data requires sophisticated content expertise to identity and circumscribe (with computer assistance) and annotate (with a standardized and mineable lexicon) the volumes of interest.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Curation of High-Quality Data by Radiologists
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Radiomics has been initiated in oncology studies, but it is potentially applicable to all diseases.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Advances in Knowledge
hedged- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although relationships between radiomics and outcome are defined with populations, they can be applied to individual patients.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Implications for Patient Care
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Notably, communities like the Children’s Oncology Group have established a history and culture of data sharing (73) and therefore are in a prime position to expand their efforts to include radiomic image analysis.”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Data Sharing
What the sources say
- Not checked yetcites [73] source
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“For example, while the value of test-retest subject or patient image studies is well recognized, many of the published studies have small sample sizes.”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Standards
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Radiomic analysis promises to increase precision in diagnosis, assessment of prognosis, and prediction of therapy response.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Implications for Patient Care
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Furthermore, once images are archived, they are rarely reaccessed.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Curation of High-Quality Data by Radiologists
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Multisite trials are required to interrogate separate cohorts of patients and to create databases with sufficient size for statistical power.”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Data Sharing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This is well recognized, and most large medical centers are now investing in appropriate electronic medical record systems to make patient data accessible in a mineable form.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Health Informatics
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“They are beginning to be solved through sharing of oncology metadata (www.oriencancer.org or the Children’s Oncology Group), and some sharing of image files has been enabled by The Cancer Imaging Archive (77).”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Data Sharing
What the sources say
- Not checked yetcites [77] source
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Radiomics is a new field, and there are substantial challenges to its implementation in a clinical setting.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Advances in Knowledge
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Such capabilities are nearly at hand, as most picture archiving and communication systems have the capability to coregister current images with prior images and perform user-interactive segmentation.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > The Radiology Reading Room of the Future
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“These challenges have been met and solved in the basic sciences of gene expression, sequencing, and protein structure databases.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Data Sharing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“In current general practice, radiologic examinations are qualitatively evaluated, and the generated reports often do not use a standard lexicon, despite a number of efforts to embrace a uniform lexicon, such as RadLex® (74).”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Curation of High-Quality Data by Radiologists
What the sources say
- Not checked yetcites [74] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Superb reporting guidelines for clinical studies have been developed by many organizations. An excellent overview is provided by the Equator network, which promotes the quality and transparency of health research (65).”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Reproducibility
What the sources say
- Not checked yetcites [65] source
boosted- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Data sharing is a common challenge in all biomedical research, and it must overcome cultural, administrative, regulatory, and personal issues (72).”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Data Sharing
What the sources say
- Not checked yetcites [72] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although massive image repositories exist, they are virtually inaccessible for curation because of the limitations described previously.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Curation of High-Quality Data by Radiologists
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Radiomics can be performed with tomographic images from CT, MR imaging, and PET studies.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Advances in Knowledge
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Differences in image acquisition and reconstruction are covariates that must be incorporated in the mining of quantitative image data and therefore increase the amount of data required for model building by an order of magnitude.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Data Sharing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
- Rests on an assumption: Differences in image acquisition and reconstruction are covariates that must be incorporated in mining quantitative image data.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“While standards exist or are being developed in many of the areas already mentioned in this article, there are still gaps.”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Standards
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Currently, radiomic data are typically not incorporated as part of this data stream; however, this is changing with the adoption of structured radiology reporting.”
§ Radiomics: Images Are More than Pictures, They Are Data > Radiomics: The Next Frontier in Clinical Decision Making > Health Informatics
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“These issues have become serious enough that editors from more than 30 high-impact-factor biomedical journals have united to impose common standards for statistical testing and to improve access to raw data (68,69).”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Reproducibility
What the sources say
- Not checked yetcites [68] 2014, Nature
- Not checked yetcites [69] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Their results suggested that radiogenomic analyses could be used to guide the selection of therapy for individual tumors.”
§ Radiomics: Images Are More than Pictures, They Are Data > Examples of Radiomics Results > Treatment Selection
What the sources say
- Not checked yetcites [55] source
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Data sharing in radiomics is especially daunting because shared data must include images and sharing must be in compliance with the Health Insurance Portability and Accountability Act, as a substantial amount of personal health information is needed to build models of sufficient complexity.”
§ Radiomics: Images Are More than Pictures, They Are Data > Challenges for Radiomics > Data Sharing
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
The authors state this as an assumption.
Biomedical images contain information that reflects underlying pathophysiology and these relationships can be revealed via quantitative image analyses.
“The conversion of digital medical images into mineable high-dimensional data, a process that is known as radiomics, is motivated by the concept that biomedical images contain information that reflects underlying pathophysiology and that these relationships can be revealed via quantitative image analyses.”
The paper relies on this without support.
Virtually all patients with cancer undergo imaging at some point and often multiple times during their care.
“The value of radiogenomics stems from the fact that while virtually all patients with cancer undergo imaging at some point and often multiple times during their care, not all of them have their disease genomically profiled.”
The paper relies on this without support.
Not all patients with cancer have their disease genomically profiled.
“The value of radiogenomics stems from the fact that while virtually all patients with cancer undergo imaging at some point and often multiple times during their care, not all of them have their disease genomically profiled.”
The paper relies on this without support.
When genomic profiling is performed, it is done one time at one location and is susceptible to sampling error.
“Furthermore, when genomic profiling is performed, it is done one time at one location and is susceptible to sampling error.”
The paper relies on this without support.
Digital radiologic images are obtained for almost every patient with cancer.
“A major strength of a radiomics approach for cancer is that digital radiologic images are obtained for almost every patient with cancer, and all of these images are potential sources for radiomics databases (Table 1).”
The paper relies on this without support.
Most patients with cancer will undergo multiple CT, MR imaging, and PET examinations.
“Most of these patients will undergo multiple CT, MR imaging, and PET examinations.”
The paper relies on this without support.
Modern CT, MR imaging, and combined PET/CT units allow for wide variations in acquisition and image reconstruction protocols, and standardization of these protocols across medical imaging centers is typically lacking.
“Modern CT, MR imaging, and combined PET/CT units allow for wide variations in acquisition and image reconstruction protocols, and standardization of these protocols across medical imaging centers is typically lacking.”
The paper relies on this without support.
Variations in acquisition and image reconstruction parameters can introduce changes that are not due to underlying biologic effects.
“However, when images are analyzed numerically to extract meaningful data, variations in acquisition and image reconstruction parameters can introduce changes that are not due to underlying biologic effects.”
The paper relies on this without support.
A consensus is emerging that truth is elusive and that optimum reproducible segmentation is achievable with computer-aided edge detection followed by manual curation.
“However, a consensus is emerging that truth is elusive and that optimum reproducible segmentation is achievable with computer-aided edge detection followed by manual curation.”
The paper relies on this without support.
Any disease, especially cancer, requires operator input because of inter- and intrasubject morphologic and contrast heterogeneity at the initial examination.
“However, any disease, especially cancer, requires operator input because of inter- and intrasubject morphologic and contrast heterogeneity at the initial examination.”
The paper relies on this without support.
The power of the predictive classifier model is dependent on having sufficient data.
“In radiomics and elsewhere, the power of the predictive classifier model is dependent on having sufficient data.”
The paper relies on this without support.
A reasonable rule of thumb is that 10 samples (patients) are needed for each feature in a model based on binary classifiers.
“It has been our experience that a reasonable rule of thumb is that 10 samples (patients) are needed for each feature in a model based on binary classifiers.”
The paper relies on this without support.
Radiomics can be performed with as few as 100 patients.
“Radiomics can be performed with as few as 100 patients, although larger data sets provide more power.”
The paper relies on this without support.
Overall survival includes death from all causes, which may not be related to the disease being studied.
“For example, overall survival is a common endpoint for many studies, but this includes death from all causes, which may not be related to the disease being studied.”
The paper relies on this without support.
The slow progress of molecular biology–based systemic diagnostic techniques and therapies is attributable to technical complexity, poor study design, overfitting, lack of standards, incomplete reporting, and unrecognized confounding.
“That slow progress can be attributed to a number of causes, including technical complexity, poor study design (in particular, mixing hypothesis generation with hypothesis testing) and overfitting of data, lack of standards for validating results, incomplete reporting of results, and unrecognized confounding variables in the databases used, particularly if data are derived retrospectively.”
The paper relies on this without support.
The quality of classifier models is limited by the size of the data sets used to create them.
“As discussed previously, the quality of classifier models is limited by the size of the data sets used to create them.”
The paper relies on this without support.
Differences in image acquisition and reconstruction are covariates that must be incorporated in mining quantitative image data.
“Differences in image acquisition and reconstruction are covariates that must be incorporated in the mining of quantitative image data and therefore increase the amount of data required for model building by an order of magnitude.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Those changes have been especially prominent in oncology, showing that the information provided by imaging studies can go far beyond determining whether a lesion is benign or malignant, now being able to indicate the histological type of the tumor, staging, presence of mutations, chance of treatment response, risk of recurrence, and expected survival(7-9).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [7]
In the citing paper
“Radiomics has been described as an extension of CAD that associates the quantitative characteristics (features) of images with patient data and clinical outcomes, not only allowing the diagnosis to be made but also providing information regarding the prognosis and treatment response(7,25).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [7]
In the citing paper
“In the medical literature, there are already many studies using radiomics in different types of imaging studies to evaluate different diseases. Most of those studies are related to oncology, such as the study of lung and kidney neoplasms on CT and positron-emission tomography/CT scans, as well as prostate cancer, breast cancer, glioma, and hepatocellular carcinoma on MRI(7,14,36).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [7]
In the citing paper
“Tools of CAD were initially developed to aid in the interpretation of radiological findings and early identification of diseases, especially breast and lung cancer(7,10).”
From Artificial intelligence, machine learning, computer-aided diagnosis, and radiomics: advances in imaging towards to precision medicine (2019) · cites this paper as [7]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
“Figure 3 is a covariance matrix of 219 features extracted from CT scans in 143 patients with non–small cell lung cancer.”
§ Radiomics: Images Are More than Pictures, They Are Data > Process of Radiomics > Feature Extraction and Qualification
“A total of 219 features were extracted from each non–small cell lung cancer tumor in 235 patients.”
Citations
Cites
Textural Features for Image Classification
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973) · IEEE Transactions on Systems, Man, and Cybernetics · doi:10.1109/tsmc.1973.4309314 · Cited by 22,886
Siegel, R., Ma, J., Zou, Z., & Jemal, A. (2014) · CA: A Cancer Journal for Clinicians · doi:10.3322/caac.21208 · Cited by 11,499
A concordance correlation coefficient to evaluate reproducibility.
Lin, L. I.-K. (1989) · Biometrics · doi:10.2307/2532051 · Cited by 8,785
Intratumor Heterogeneity and Branched Evolution Revealed by Multiregion Sequencing
Gerlinger, M., Rowan, A. J., Horswell, S., Larkin, J., Endesfelder, D., Gronroos, E., Martinez, P., Matthews, N., Stewart, A., Tarpey, P., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1113205 · Cited by 7,774
Radiomics: extracting more information from medical images using advanced feature analysis
Lambin, P., Rios-Velazquez, E., Leijenaar, R., Carvalho, S., van Stiphout, R. G. P. M., Granton, P., Zegers, C. M. L., Gillies, R., Boellard, R., Dekker, A., et al. (2012) · European Journal of Cancer · doi:10.1016/j.ejca.2011.11.036 · Cited by 6,084
Self-citationSignet-Ring Cell Carcinoma of the Colon: A Case Report and Review of the Literature
Park, P. Y., Goldin, T. A., Chang, J. C. S., Markman, M. M., & Kundranda, M. N. (2015) · Case Reports in Oncology · doi:10.1159/000441772 · Cited by 5,273
A New Initiative on Precision Medicine
Collins, F. S., & Varmus, H. (2015) · New England Journal of Medicine · doi:10.1056/nejmp1500523 · Cited by 5,202
Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach
Aerts, H. J. W. L., Velazquez, E. R., Leijenaar, R. T. H., Parmar, C., Grossmann, P., Carvalho, S., Bussink, J., Monshouwer, R., Haibe-Kains, B., Rietveld, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms5006 · Cited by 5,132
Self-citationThe Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599
Genotypic and Histological Evolution of Lung Cancers Acquiring Resistance to EGFR Inhibitors
Sequist, L. V., Waltman, B. A., Dias-Santagata, D., Digumarthy, S., Turke, A. B., Fidias, P., Bergethon, K., Shaw, A. T., Gettinger, S., Cosper, A. K., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002003 · Cited by 3,499
Research works printed but not linked: 7
- Office of the Press Secretary, The White House Web site
- Shankar LK, 2006, J Nucl Med, 47, 1059
- Kinahan P, 2014, J Nucl Med, 55, 1520
- Grossmann P, Sci Transl Med
- Pickup L, 2015, Radiological Society of North America Scientific Assembly and Annual Meeting Program, SSM06
- Fehr D, Proc Natl Acad Sci USA
- 2014, Nature, 515, 7
Cited by
Sorted by citations
Items 101–200 of 1,034
Meyer, M., Ronald, J., Vernuccio, F., Nelson, R. C., Ramirez-Giraldo, J. C., Solomon, J., Patel, B. N., Samei, E., & Marin, D. (2019) · Radiology · doi:10.1148/radiol.2019190928 · Cited by 258
Deep learning classification of lung cancer histology using CT images
Chaunzwa, T. L., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M. D., Christiani, D. C., Mak, R. H., & Aerts, H. J. W. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-84630-x · Cited by 269
He, L., Huang, Y., Ma, Z., Liang, C., Liang, C., & Liu, Z. (2016) · Scientific Reports · doi:10.1038/srep34921 · Cited by 253
Big data in basic and translational cancer research
Jiang, P., Sinha, S., Aldape, K., Hannenhalli, S., Sahinalp, C., & Ruppin, E. (2022) · Nature Reviews Cancer · doi:10.1038/s41568-022-00502-0 · Cited by 254
Cui, Y., Yang, X., Shi, Z., Yang, Z., Du, X., Zhao, Z., & Cheng, X. (2018) · European Radiology · doi:10.1007/s00330-018-5683-9 · Cited by 251
Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score
Sanduleanu, S., Woodruff, H. C., de Jong, E. E. C., van Timmeren, J. E., Jochems, A., Dubois, L., & Lambin, P. (2018) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2018.03.033 · Cited by 242
Machine Learning in Radiology: Applications Beyond Image Interpretation
Lakhani, P. C., Prater, A. B., Hutson, R. K., Andriole, K. P., Dreyer, K. J., Morey, J., Prevedello, L. M. S., Clark, T. J., Geis, J. R., Itri, J. N., et al. (2017) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.09.044 · Cited by 270
Sollini, M., Antunovic, L., Chiti, A., & Kirienko, M. (2019) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-019-04372-x · Cited by 227
Radiomic MRI Phenotyping of Glioblastoma: Improving Survival Prediction
Bae, S., Choi, Y. S., Ahn, S. S., Chang, J. H., Kang, S., Kim, E. H., Kim, S. H., & Lee, S. (2018) · Radiology · doi:10.1148/radiol.2018180200 · Cited by 249
Shin, J., Seo, N., Baek, S., Son, N., Lim, J. S., Kim, N. K., Koom, W. S., & Kim, S. (2022) · Radiology · doi:10.1148/radiol.211986 · Cited by 247
Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma
Zhang, B., He, X. J., Ouyang, F., Gu, D., Dong, Y., Zhang, L., Mo, X., Huang, W., Tian, J., & Zhang, S. (2017) · Cancer Letters · doi:10.1016/j.canlet.2017.06.004 · Cited by 246
Whybra, P., Zwanenburg, A., Andrearczyk, V., Schaer, R., Apte, A., Ayotte, A., Baheti, B., Bakas, S., Bettinelli, A., Boellaard, R., et al. (2024) · Radiology · doi:10.1148/radiol.231319 · Cited by 246
Liu, X., Li, Y., Qian, Z., Sun, Z., Xu, K., Wang, K., Liu, S., Fan, X., Li, S., Zhang, Z., et al. (2018) · NeuroImage Clinical · doi:10.1016/j.nicl.2018.10.014 · Cited by 243
Texture Analysis of Imaging: What Radiologists Need to Know
Varghese, B. A., Cen, S., Hwang, D., & Duddalwar, V. A. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20624 · Cited by 253
Park, H., Lim, Y., Ko, E. S., Cho, H.-H., Lee, J. E., Han, B., Ko, E. Y., Choi, J. S., & Park, K. W. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3783 · Cited by 243
Emerging Applications of Artificial Intelligence in Neuro-Oncology
Rudie, J. D., Rauschecker, A. M., Bryan, R. N., Davatzikos, C. A., & Mohan, S. (2019) · Radiology · doi:10.1148/radiol.2018181928 · Cited by 247
Yue, H., Yu, Q., Liu, C., Huang, Y., Jiang, Z., Shao, C., Zhang, H., Ma, B., Wang, Y., Xie, G., et al. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-3026 · Cited by 241
Dong, Y., Feng, Q., Yang, W., Lu, Z., Deng, C., Zhang, L., Lian, Z., Liu, J., Luo, X., Pei, S., et al. (2017) · European Radiology · doi:10.1007/s00330-017-5005-7 · Cited by 239
Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?
Yang, L., Dong, D., Fang, M., Zhu, Y., Zang, Y., Liu, Z., Zhang, H., Ying, J., Zhao, X., & Tian, J. (2018) · European Radiology · doi:10.1007/s00330-017-5146-8 · Cited by 237
Lymph Node Imaging in Patients with Primary Breast Cancer: Concurrent Diagnostic Tools
Marino, M. A., Avendaño, D., Zapata, P., Riedl, C. C., & Pinker, K. (2019) · The Oncologist · doi:10.1634/theoncologist.2019-0427 · Cited by 238
Radiomics of Brain MRI: Utility in Prediction of Metastatic Tumor Type
Kniep, H. C., Madesta, F., Schneider, T., Hanning, U. M. P., Schönfeld, M. H., Schön, G., Fiehler, J., Gauer, T., Werner, R., & Gellißen, S. (2018) · Radiology · doi:10.1148/radiol.2018180946 · Cited by 234
Kolossváry, M., Karády, J., Szilveszter, B., Kitslaar, P. H., Hoffmann, U., Merkely, B. P., & Maurovich‐Horvat, P. (2017) · Circulation Cardiovascular Imaging · doi:10.1161/circimaging.117.006843 · Cited by 239
Criteria for the translation of radiomics into clinically useful tests
Huang, E. P., O’Connor, J. P. B., McShane, L. M., Giger, M. L., Lambin, P., Kinahan, P. E., Siegel, E. L., & Shankar, L. K. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-022-00707-0 · Cited by 246
Self-citationYang, J., Ju, J., Guo, L., Ji, B., Shi, S., Yang, Z., Gao, S., Yuan, X., Tian, G., Liang, Y., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.12.028 · Cited by 233
Deep Learning for Screening COVID-19 using Chest X-Ray Images
Basu, S., Mitra, S., & Saha, N. (2020) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci47803.2020.9308571 · Cited by 237
Bibault, J., Giraud, P., Housset, M., Durdux, C., Taı̈eb, J., Berger, A., Coriat, R., Chaussade, S., Dousset, B. E., Nordlinger, B., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-30657-6 · Cited by 229
Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats
Napel, S. A., Mu, W., Jardim‐Perassi, B. V., Aerts, H. J. W. L., & Gillies, R. J. (2018) · Cancer · doi:10.1002/cncr.31630 · Cited by 235
Self-citationRadiomic-Based Pathological Response Prediction from Primary Tumors and Lymph Nodes in NSCLC
Coroller, T. P., Agrawal, V., Huynh, E., Narayan, V., Lee, S. W., Mak, R. H., & Aerts, H. J. W. L. (2016) · Journal of Thoracic Oncology · doi:10.1016/j.jtho.2016.11.2226 · Cited by 228
The crucial role of multiomic approach in cancer research and clinically relevant outcomes
Lu, M., & Zhan, X. (2018) · The EPMA Journal · doi:10.1007/s13167-018-0128-8 · Cited by 235
Zhen, S., Cheng, M., Tao, Y.-B., Wang, Y., Juengpanich, S., Jiang, Z.-Y., Jiang, Y., Yan, Y.-Y., Lu, W., LÜ, J.-M., et al. (2020) · Frontiers in Oncology · doi:10.3389/fonc.2020.00680 · Cited by 238
Zhao, W., Yang, J., Sun, Y., Li, C., Wu, W., Jin, L., Yang, Z., Ni, B., Gao, P., Wang, P., et al. (2018) · Cancer Research · doi:10.1158/0008-5472.can-18-0696 · Cited by 226
Predictive markers for Parkinson's disease using deep neural nets on neuromelanin sensitive MRI
Shinde, S., Prasad, S., Saboo, Y., Kaushick, R., Saini, J. S., Pal, P. K., & Ingalhalikar, M. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101748 · Cited by 230
Ma, X., Wei, J., Gu, D., Zhu, Y., Feng, B., Liang, M., Wang, S., Zhao, X., & Tian, J. (2019) · European Radiology · doi:10.1007/s00330-018-5985-y · Cited by 220
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Sheth, D., & Giger, M. L. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26878 · Cited by 225
Kickingereder, P., Neuberger, U., Bonekamp, D., Piechotta, P. L., GOETZ, M. A., Wick, A., Sill, M., Kratz, A., Shinohara, R. T., Jones, D., et al. (2017) · Neuro-Oncology · doi:10.1093/neuonc/nox188 · Cited by 219
Wu, J., Cao, G., Sun, X., Lee, J., Rubin, D. L., Napel, S. A., Kurian, A. W., Daniel, B. L., & Li, R. (2018) · Radiology · doi:10.1148/radiol.2018172462 · Cited by 216
Zhou, J., Zhang, Y., Chang, K., Lee, K. E., Wang, O., Li, J., Lin, Y., Pan, Z., Chang, P., Chow, D. S., et al. (2019) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26981 · Cited by 220
Radiomics strategy for glioma grading using texture features from multiparametric MRI
Tian, Q., Yan, L., Zhang, X., Zhang, X., Hu, Y., Han, Y., Liu, Z., Nan, H., Sun, Q., Sun, Y., et al. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26010 · Cited by 221
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Shafiq-ul-Hassan, M., Latifi, K., Zhang, G. G., Ullah, G., Gillies, R. J., & Moros, E. G. (2018) · Scientific Reports · doi:10.1038/s41598-018-28895-9 · Cited by 215
Self-citationSantos, M. K., Ferreira Júnior, J. R., Wada, D. T., Tenório, A. P. M., Nogueira-Barbosa, M. H., & Marques, P. M. D. A. (2019) · Radiologia Brasileira · doi:10.1590/0100-3984.2019.0049 · Cited by 214
The future of personalised radiotherapy for head and neck cancer
Caudell, J. J., Torres–Roca, J. F., Gillies, R. J., Enderling, H., Kim, S., Rishi, A., Moros, E. G., & Harrison, L. B. (2017) · The Lancet Oncology · doi:10.1016/s1470-2045(17)30252-8 · Cited by 214
Self-citationRadiomics and radiogenomics in gliomas: a contemporary update
Singh, G., Manjila, S. V., Sakla, N. M., True, A., Wardeh, A. H., Beig, N. G., Vaysberg, A., Matthews, J., Prasanna, P., & Spektor, V. (2021) · British Journal of Cancer · doi:10.1038/s41416-021-01387-w · Cited by 226
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Yu, J., Shi, Z., Lian, Y., Li, Z., Liu, T., Gao, Y., Wang, Y., Chen, L., & Mao, Y. Q. (2016) · European Radiology · doi:10.1007/s00330-016-4653-3 · Cited by 216
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Han, Y. K., Ma, Y., Wu, Z., Zhang, F., Zheng, D., Liu, X., Tao, L., Liang, Z., Yang, Z., Li, X., et al. (2020) · European Journal of Nuclear Medicine and Molecular Imaging · doi:10.1007/s00259-020-04771-5 · Cited by 218
Zhu, X., Dong, D., Chen, Z., Fang, M., Zhang, L., Song, J., Yu, D., Zang, Y., Liu, Z., Shi, J., et al. (2018) · European Radiology · doi:10.1007/s00330-017-5221-1 · Cited by 211
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Cuocolo, R., Caruso, M., Perillo, T., Ugga, L., & Petretta, M. (2020) · Cancer Letters · doi:10.1016/j.canlet.2020.03.032 · Cited by 221
Mali, S. A., Ibrahim, A., Woodruff, H. C., Andrearczyk, V., Müller, H., Primakov, S. P., Salahuddin, Z., Chatterjee, A., & Lambin, P. (2021) · Journal of Personalized Medicine · doi:10.3390/jpm11090842 · Cited by 228
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Oreiller, V., Andrearczyk, V., Jreige, M., Boughdad, S., Elhalawani, H. M., Castelli, J., Vallières, M., Zhu, S., Xie, J., Peng, Y., et al. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102336 · Cited by 223
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Kenny, H., Hadjiiski, L. M., Chan, H., Weizer, A. Z., Alva, A. S., Cohan, R. H., Caoili, E. M., Paramagul, C., & Samala, R. K. (2017) · Scientific Reports · doi:10.1038/s41598-017-09315-w · Cited by 208
Optimizing Cancer Treatment Using Game Theory
Staňková, K., Brown, J. S., Dalton, W. S., & Gatenby, R. A. (2018) · JAMA Oncology · doi:10.1001/jamaoncol.2018.3395 · Cited by 202
Kim, S., Shin, J., Kim, D. Y., Choi, G. H., Kim, M., & Choi, J. (2019) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-18-2861 · Cited by 207
Da‐ano, R., Masson, I., Lucia, F., Dore, M., Robin, P., Alfieri, J., Rousseau, C., Mervoyer, A., Reinhold, C., Castelli, J., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-66110-w · Cited by 211
Big Data’s Role in Precision Public Health
Dolley, S. (2018) · Frontiers in Public Health · doi:10.3389/fpubh.2018.00068 · Cited by 216
Artificial intelligence applications for thoracic imaging
Chassagnon, G., Vakalopoulou, M., Nikos Paragios, A., & Revel, M. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108774 · Cited by 214
Laukamp, K. R., Thiele, F., Shakirin, G., Zopfs, D., Faymonville, A., Timmer, M., Maintz, D., Perkuhn, M., & Borggrefe, J. (2019) · European Radiology · doi:10.1007/s00330-018-5595-8 · Cited by 205
Radiomics of pulmonary nodules and lung cancer
Wilson, R., & Devaraj, A. (2017) · Translational Lung Cancer Research · doi:10.21037/tlcr.2017.01.04 · Cited by 202
Baeßler, B., Mannil, M., Oebel, S., Maintz, D. C., Alkadhi, H., & Manka, R. (2017) · Radiology · doi:10.1148/radiol.2017170213 · Cited by 202
Deep learning in head & neck cancer outcome prediction
Diamant, A., Chatterjee, A., Vallières, M., Shenouda, G., & Seuntjens, J. P. (2019) · Scientific Reports · doi:10.1038/s41598-019-39206-1 · Cited by 211
Prasanna, P., Patel, J., Partovi, S., Madabhushi, A., & Tiwari, P. (2017) · European Radiology · doi:10.1007/s00330-017-4815-y · Cited by 202
Neisius, U., El-Rewaidy, H., Nakamori, S., Rodriguez, J., Manning, W. J., & Nezafat, R. (2019) · JACC. Cardiovascular imaging · doi:10.1016/j.jcmg.2018.11.024 · Cited by 196
Zhang, Y., Zhang, X., Cheng, Y., Li, B., Teng, X.-Z., Zhang, J., Lam, S., Zhou, T., Ma, Z., Sheng, J., et al. (2023) · Military Medical Research · doi:10.1186/s40779-023-00458-8 · Cited by 218
Feng, S., Jia, Y., Liao, B., Huang, B., Zhou, Q., Li, X., Wei, K., Chen, L., Li, B., Wang, W., et al. (2019) · European Radiology · doi:10.1007/s00330-018-5935-8 · Cited by 199
Fornacon-Wood, I., Mistry, H. B., Ackermann, C. J., Blackhall, F. H., McPartlin, A. J., Finn, C. F., Price, G. J., & O’Connor, J. P. B. (2020) · European Radiology · doi:10.1007/s00330-020-06957-9 · Cited by 198
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Chu, L. C., Park, S., Kawamoto, S., Fouladi, D. F., Shayesteh, S., Zinreich, E. S., Graves, J. S., Horton, K. M., Hruban, R. H., Yuille, A. L., et al. (2019) · American Journal of Roentgenology · doi:10.2214/ajr.18.20901 · Cited by 193
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Vallières, M., Zwanenburg, A., Badic, B., Rest, C. C. L., Visvikis, D., & Hatt, M. (2017) · Journal of Nuclear Medicine · doi:10.2967/jnumed.117.200501 · Cited by 200
Genomic and immune heterogeneity are associated with differential responses to therapy in melanoma
Reuben, A., Spencer, C. N., Prieto, P. A., Gopalakrishnan, V., Reddy, S. M., Miller, J. P., Mao, X., De Macedo, M. P., Chen, J., Song, X., et al. (2017) · npj Genomic Medicine · doi:10.1038/s41525-017-0013-8 · Cited by 166
Hu, H.-T., Wang, Z., Huang, X.-W., Chen, S., Zheng, X., Ruan, S., Xie, X., Lu, M., Yu, J., Tian, J., et al. (2018) · European Radiology · doi:10.1007/s00330-018-5797-0 · Cited by 190
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Cuocolo, R., Cipullo, M. B., Stanzione, A., Ugga, L., Romeo, V., Radice, L., Brunetti, A., & Imbriaco, M. (2019) · European Radiology Experimental · doi:10.1186/s41747-019-0109-2 · Cited by 199
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van der Voort, S. R., Incekara, F., Wijnenga, M. M. J., Kapsas, G., Gahrmann, R., Schouten, J. W., Tewarie, R. N., Nijeholt, G. J. L. À., Hamer, P. C. D. W., Eijgelaar, R. S., et al. (2022) · Neuro-Oncology · doi:10.1093/neuonc/noac166 · Cited by 191
Radiomic nomogram for prediction of axillary lymph node metastasis in breast cancer
Han, L., Zhu, Y., Liu, Z., Yu, T., He, C., Jiang, W., Kan, Y., Dong, D., Tian, J., & Luo, Y. (2019) · European Radiology · doi:10.1007/s00330-018-5981-2 · Cited by 190
Gray-level discretization impacts reproducible MRI radiomics texture features
Duron, L., Balvay, D., Perre, S. V., Bouchouicha, A., Savatovsky, J., Sadik, J., Thomassin‐Naggara, I., Fournier, L. S., & Lecler, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0213459 · Cited by 189
Liu, C., Ding, J., Spuhler, K., Gao, Y., Serrano‐Sosa, M., Moriarty, M., Hussain, S., He, X., Liang, C., & Huang, C. (2018) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.26224 · Cited by 187
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Huang, R. Y., Bi, W. L., Griffith, B. D., Kaufmann, T. J., la Fougère, C. J., Schmidt, N. O., Tonn, J. C., Vogelbaum, M. A., Wen, P. Y., Aldape, K., et al. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy143 · Cited by 196
Deep learning workflow in radiology: a primer
Montagnon, E., Cerny, M., Cadrin-Chênevert, A., Hamilton, V., Derennes, T., Ilinca, A. P., Vandenbroucke‐Menu, F., Turcotte, S., Kadoury, S., & Tang, A. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0832-5 · Cited by 189
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How to develop a meaningful radiomic signature for clinical use in oncologic patients
Papanikolaou, N. K., Matos, C., & Koh, D. (2020) · Cancer Imaging · doi:10.1186/s40644-020-00311-4 · Cited by 186
Nasief, H., Zheng, C., Schott, D., Hall, W., Tsai, S., Erickson, B., & Allen Li, X. (2019) · npj Precision Oncology · doi:10.1038/s41698-019-0096-z · Cited by 181
van Timmeren, J. E., Leijenaar, R. T. H., van Elmpt, W., Reymen, B. J. T., Oberije, C., Monshouwer, R., Bussink, J., Brink, C., Hansen, O., & Lambin, P. (2017) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2017.04.016 · Cited by 185
Background, current role, and potential applications of radiogenomics
Pinker, K., Shitano, F., Sala, E., Do, R. K., Young, R. J., Wibmer, A. G., Hricak, H., Sutton, E. J., & Morris, E. A. (2017) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.25870 · Cited by 185
Self-citationVariability and Standardization of Quantitative Imaging
Hagiwara, A., Fujita, S., Ohno, Y., & Aoki, S. (2020) · Investigative Radiology · doi:10.1097/rli.0000000000000666 · Cited by 185
Agile convolutional neural network for pulmonary nodule classification using CT images
Zhao, X., Liu, L., Qi, S., Teng, Y., Li, J., & Qian, W. (2018) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-017-1696-0 · Cited by 190
Jiang, Y., Chen, C., Xie, J., Wang, W., Zha, X., Lv, W., Chen, H., Hu, Y., Li, T., Yu, J., et al. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.09.007 · Cited by 184
PET/MRI Radiomics in Patients With Brain Metastases
Lohmann, P., Köcher, M., Ruge, M. I., Visser-Vandewalle, V. E. R. M., Shah, N. J., Fink, G. R., Langen, K., & Galldiks, N. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00001 · Cited by 184
Hyun, S. H., Ahn, M. S., Koh, Y. W., & Lee, S. J. (2019) · Clinical Nuclear Medicine · doi:10.1097/rlu.0000000000002810 · Cited by 185
Combining molecular and imaging metrics in cancer: radiogenomics
Gullo, R. L., Naranjo, I. D., Morris, E. A., & Pinker, K. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0795-6 · Cited by 183
Peng, J., Zhang, J., Zhang, Q., Xu, Y., Zhou, J., & Liu, L. (2018) · Diagnostic and Interventional Radiology · doi:10.5152/dir.2018.17467 · Cited by 179
Liang, B., Yan, H., Tian, Y., Chen, X., Yan, L., Zhang, T., Zhou, Z., Wang, L., & Dai, J. (2019) · Frontiers in Oncology · doi:10.3389/fonc.2019.00269 · Cited by 178
Radiomics feature reliability assessed by intraclass correlation coefficient: a systematic review
Xue, C., Yuan, J., Lo, G. G., Chang, A. T. Y., Poon, D. M. C., Wong, O. L., Zhou, Y., & Chu, C. W. (2021) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-21-86 · Cited by 194
Wang, X., Zhao, X., Li, Q., Xia, W., Peng, Z., Zhang, R., Li, Q., Jian, J., Wang, W., Tang, Y., et al. (2019) · European Radiology · doi:10.1007/s00330-019-06084-0 · Cited by 176
Xu, Y., Su, G.-H., Ma, D., Xiao, Y., Shao, Z.-M., & Jiang, Y.-Z. (2021) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-021-00729-7 · Cited by 163
“185 To the best of our knowledge, radiomics technology applied to tumor immunity is mainly used to identify biomarkers re fl ecting immune in fi ltration and predict the therapeutic response of ICB-treated patients (Fig.”
BackgroundManganaro, L., Lakhman, Y., Bharwani, N., Gui, B., Gigli, S., Vinci, V., Rizzo, S., Kido, A., Cunha, T. M., Sala, E., et al. (2021) · European Radiology · doi:10.1007/s00330-020-07632-9 · Cited by 176
Feng, Z., Li, H., Liu, Q., Duan, J., Zhou, W., Yu, X., Chen, Q., Liu, Z., Wang, W., & Rong, P. (2022) · Radiology · doi:10.1148/radiol.221291 · Cited by 176
Shan, Q., Hu, H.-T., Feng, S., Peng, Z., Chen, S., Zhou, Q., Li, X., Xie, X., Lu, M., Wang, W., et al. (2019) · Cancer Imaging · doi:10.1186/s40644-019-0197-5 · Cited by 175
Jia, T.-Y., Xiong, J.-F., Li, X., Yu, W. J., Xu, Z., Cai, X., Ma, J., Ren, Y., Larsson, R., Zhang, J., et al. (2019) · European Radiology · doi:10.1007/s00330-019-06024-y · Cited by 168
Gao, J. A., Jiang, Q., Zhou, B., & Chen, D. (2019) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2019326 · Cited by 180
Li, J., Dong, D., Fang, M., Wang, R., Tian, J., Li, H., & Gao, J. (2020) · European Radiology · doi:10.1007/s00330-019-06621-x · Cited by 172
Quantitative CT Analysis of Diffuse Lung Disease
Chen, A., Karwoski, R. A., Gierada, D. S., Bartholmai, B. J., & Koo, C. W. (2019) · Radiographics · doi:10.1148/rg.2020190099 · Cited by 176
Predicting distant metastasis and chemotherapy benefit in locally advanced rectal cancer
Liu, Z., Meng, X., Zhang, H., Li, Z., Liu, J., Sun, K., Meng, Y., Dai, W., Xie, P., Ding, Y., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18162-9 · Cited by 170
Wu, S., Zheng, J., Li, Y., Wu, Z., Shi, S., Huang, M. X., Yu, H., Dong, W., Huang, J., & Lin, T. (2018) · EBioMedicine · doi:10.1016/j.ebiom.2018.07.029 · Cited by 168
Metadata
- Authors
- Gillies, R. J., Kinahan, P. E., & Hricak, H.
- Affiliations
- Memorial Sloan Kettering Cancer Center (US)
- Corresponding author
- Gillies, Robert J.
- Venue
- Radiology
- Published
- 2016-02
- Volume
- 278
- Issue
- 2
- Pages
- 563-577
- ISSN
- 0033-8419 (ISSN-L)
- 1527-1315
- PMC
- PMC4734157
- OpenAlex
- W2174661749
- Semantic Scholar
- dcd99d49af33bd14e9e0750bcf854e7b306c808a
- Cited by
- 8,421
- References
- 76
- Citations by year
- 2026: 916 · 2025: 1020 · 2024: 1098 · 2023: 1089 · 2022: 1116 · 2021: 1084 · 2020: 837 · 2019: 587 · 2018: 400 · 2017: 199 · 2016: 62
- FWCI
- 179.12
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Radiology (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Cancer Institute (R01CA187532, R01CA190105, U54CA143970, U01CA143062, P30CA008748)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
- Keywords
- radiomics, medical image analysis, quantitative imaging features, higher-order statistics, clinical decision support, cancer diagnosis, cancer prognosis, predictive modeling, multimodal data integration, bioinformatics
- MeSH terms
- Algorithms, Biopsy, Decision Making, Diagnosis, Computer-Assisted, Diagnostic Imaging, Humans, Image Interpretation, Computer-Assisted, Medical Informatics, Neoplasms, Prognosis, Radiology Information Systems, Genomics, Data Mining
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