Cited by 8,421Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification TechniquesMedical Imaging Techniques and Applications

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 access

Abstract

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.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.1148/radiol.2015151169

Cites

75 of 75 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Verification

Claims the paper makes

60 Not checked yet · 42 No basis given · 39 Own interpretation · 1 Own method · 1 Own result

Verify all 80 references — free account

Show all 58 claims

  • It may state two separate claims as one. 48
  • Probably not a substantial claim. 63
85 of 85
  • 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.”
17 of 17

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?

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.

1 of 1

Citations

Cites

Show all 76 references

Research works printed but not linked: 7

  1. Office of the Press Secretary, The White House Web site
  2. Shankar LK, 2006, J Nucl Med, 47, 1059
  3. Kinahan P, 2014, J Nucl Med, 55, 1520
  4. Grossmann P, Sci Transl Med
  5. Pickup L, 2015, Radiological Society of North America Scientific Assembly and Annual Meeting Program, SSM06
  6. Fehr D, Proc Natl Acad Sci USA
  7. 2014, Nature, 515, 7

Cited by

Sorted by citations

Items 901–1,000 of 1,034

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
OpenAlex
W2174661749
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
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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

A language model drew the claims from the paper on 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).

A second model labelled each claim on 2026-10-09 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review