Radiomics: the process and the challenges

Kumar, Virendra; Gu, Yuhua; Basu, Satrajit; Berglund, Anders; Eschrich, Steven A.; Schabath, Matthew B.; Forster, Kenneth; Aerts, Hugo J.W.L.; Dekker, Andre; Fenstermacher, David; Goldgof, Dmitry B.; Hall, Lawrence O.; Lambin, Philippe; Balagurunathan, Yoganand; Gatenby, Robert A.; Gillies, Robert J.

doi:10.1016/j.mri.2012.06.010

Cited by 2,258Radiomics and Machine Learning in Medical ImagingImage Retrieval and Classification TechniquesMedical Imaging Techniques and Applications

Radiomics: the process and the challenges

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Kumar, Virendra, Gu, Yuhua, Basu, Satrajit, Berglund, Anders, Eschrich, Steven A., Schabath, Matthew B., Forster, Kenneth, Aerts, Hugo J.W.L. +8 more·2012·Magnetic Resonance Imaging

DOI 10.1016/j.mri.2012.06.010PMC PMC3563280

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Abstract

"Radiomics" refers to the extraction and analysis of large amounts of advanced quantitative imaging features with high throughput from medical images obtained with computed tomography, positron emission tomography or magnetic resonance imaging. Importantly, these data are designed to be extracted from standard-of-care images, leading to a very large potential subject pool. Radiomics data are in a mineable form that can be used to build descriptive and predictive models relating image features to phenotypes or gene-protein signatures. The core hypothesis of radiomics is that these models, which can include biological or medical data, can provide valuable diagnostic, prognostic or predictive information. The radiomics enterprise can be divided into distinct processes, each with its own challenges that need to be overcome: (a) image acquisition and reconstruction, (b) image segmentation and rendering, (c) feature extraction and feature qualification and (d) databases and data sharing for eventual (e) ad hoc informatics analyses. Each of these individual processes poses unique challenges. For example, optimum protocols for image acquisition and reconstruction have to be identified and harmonized. Also, segmentations have to be robust and involve minimal operator input. Features have to be generated that robustly reflect the complexity of the individual volumes, but cannot be overly complex or redundant. Furthermore, informatics databases that allow incorporation of image features and image annotations, along with medical and genetic data, have to be generated. Finally, the statistical approaches to analyze these data have to be optimized, as radiomics is not a mature field of study. Each of these processes will be discussed in turn, as well as some of their unique challenges and proposed approaches to solve them. The focus of this article will be on images of non-small-cell lung cancer.

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The first study to identify, define, and extract more agnostic features used 182 texture features in combination with 22 semantic features to describe CT images of lung cancer.
“The first such study used 182 texture features in combination with 22 semantic features to describe CT images of lung cancer (24).”
From Radiomics: Images Are More than Pictures, They Are Data (2016) · cites this paper as [24]

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Method
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Background
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Method
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Method
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MethodSelf-citation
A review of methods of analysis in contouring studies for radiation oncology
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Method
Quantifying spatial heterogeneity in dynamic contrast‐enhanced MRI parameter maps
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Background
Image segmentation feature selection and pattern classification for mammographic microcalcifications
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Method
Quantifying Tumor Vascular Heterogeneity with Dynamic Contrast‐Enhanced Magnetic Resonance Imaging: A Review
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Method
Computer‐aided measurement of liver volumes in CT by means of geodesic active contour segmentation coupled with level‐set algorithms
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Method
Graph cuts segmentation using an elliptical shape prior
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Method
Variability of target volume delineation in cervical esophageal cancer11Awarded Research Trainee Prize from the Radiological Society of North America (RSNA).
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Background
Feasibility of Pathology-Correlated Lung Imaging for Accurate Target Definition of Lung Tumors
Stroom, J. C., Blaauwgeers, H., van Baardwijk, A. A. W., Boersma, L. J., Lebesque, J. V., Theuws, J. C. M., van Suylen, R. J., Klomp, H. M., Liesker, K. R., van Pel, R., et al. (2007) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2007.04.065 · Cited by 97 · cited 1× · see citing works
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Method
Image Data Sharing for Biomedical Research—Meeting HIPAA Requirements for De-identification
Freymann, J., Kirby, J., Perry, J. H., Clunie, D., & Jaffe, C. C. (2011) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-011-9422-x · Cited by 95 · cited 2× · see citing works
“One of the largest online CT image repositories is the National Biomedical Image Archive (NBIA) hosted by the National Cancer Institute.”
Method
Comparison of model‐based arterial input functions for dynamic contrast‐enhanced MRI in tumor bearing rats
McGrath, D. M., Bradley, D. P., Tessier, J., Lacey, T., Taylor, C., & Parker, G. J. M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21959 · Cited by 93 · cited 1× · see citing works
“Different investigators use different methods to convert DCE MRI signal intensities to contrast agent concentration [23–25].”
Method
The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change-Analysis Software
Armato, S. G., Meyer, C., McNitt‐Gray, M. F., McLennan, G., Reeves, A. P., Croft, B. Y., & Clarke, L. (2008) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2008.161 · Cited by 92 · cited 1× · see citing works
“Currently available clinical image data which may be used for radiomics study includes the Lung Image Database Consortium, the Reference Image Database to Evaluate Response to therapy in lung cancer and others [27,28].”
Method
Is cross-validation better than resubstitution for ranking genes?
Braga-Neto, U., Hashimoto, R. F., Dougherty, E. R., Nguyen, D. V., & Carroll, R. J. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg399 · Cited by 91 · cited 1× · see citing works
“In addition, most popular error estimates has been studied in context of small sample classification [97].”
Background
Object segmentation using graph cuts based active contours
Xu, N., Bansal, R., & Ahuja, N. (2005) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211451 · Cited by 90 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Strong Feature Sets from Small Samples
Kim, S., Dougherty, E. R., Barrera, J., Chen, Y., Bittner, M. L., & Trent, J. M. (2002) · Journal of Computational Biology · doi:10.1089/10665270252833226 · Cited by 89 · cited 1× · see citing works
“There has been extensive studies to make unbiased inference in small samples, one approach was to create synthetic samples following the distribution of the sample groups and report errors of the newly formed population [96].”
Background
Medical Imaging on the Semantic Web: Annotation and Image Markup.
Rubin, D. L., Mongkolwat, P., Kleper, V., Supekar, K., & Channin, D. S. (2008) · Cited by 73 · see citing works
Incorporation of tumor shape into an assessment of spatial heterogeneity for human sarcomas imaged with FDG-PET
O’Sullivan, F. (2005) · Biostatistics · doi:10.1093/biostatistics/kxi010 · Cited by 70 · cited 1× · see citing works
“Quantitative features describing the geometric shape of a tumor can also be extracted from the 3D surface of the rendered volumes [62].”
Background
An evaluation of the variability of tumor-shape definition derived by experienced observers from CT images of supraglottic carcinomas (ACRIN protocol 6658)
Cooper, J. S., Mukherji, S. K., Toledano, A. Y., Beldon, C., Schmalfuss, I. M., Amdur, R. J., Sailer, S. L., Loevner, L. A., Kousouboris, P., Ang, K. K., et al. (2007) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2006.10.029 · Cited by 66 · cited 1× · see citing works
“In the majority of cases, manual segmentation tends to overestimate the lesion volume to ensure the entire lesion is identified [54], and the process is highly variable [55,56].”
Background
The integration of PET-CT scans from different hospitals into radiotherapy treatment planning
Öllers, M., Bosmans, G., van Baardwijk, A. A. W., Dekker, A. L. A. J., Lambin, P., Teule, J., Thimister, W., Rhami, A., & Ruysscher, D. D. (2008) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2007.12.025 · Cited by 65 · cited 1× · see citing works
“From a technical viewpoint, the main challenges are the dose calibration and the metabolic volume or volume-of-interest (VOI) reconstruction that depends heavily on the scan protocol and source-to-background ratio [16].”
BackgroundSelf-citation
Developing a quality control protocol for diffusion imaging on a clinical MRI system
Delakis, I., Moore, E., Leach, M. O., & Wilde, J. P. D. (2004) · Physics in Medicine and Biology · doi:10.1088/0031-9155/49/8/003 · Cited by 65 · cited 1× · see citing works
“DWI has been proposed as a cancer imaging biomarker, and there are efforts to develop quality control protocols [18,19].”
Background
Automatic Graph Cut Segmentation of Lesions in CT Using Mean Shift Superpixels
Ye, X., Beddoe, G., & Slabaugh, G. (2010) · International Journal of Biomedical Imaging · doi:10.1155/2010/983963 · Cited by 55 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
The Annotation and Image Mark-up Project
Channin, D. S., Mongkolwat, P., Kleper, V., & Rubin, D. L. (2009) · Radiology · doi:10.1148/radiol.2533090135 · Cited by 55 · cited 1× · see citing works
“There are efforts to develop a standardized lexicon for the description of such lesions [4,5] and to include these descriptors via annotated image markup into quantitative, mineable data [6,7].”
Background
Predicting Radiological Panel Opinions Using a Panel of Machine Learning Classifiers
Zinovev, D., Raicu, D. S., Furst, J. D., & Armato, S. G. (2009) · Algorithms · doi:10.3390/a2041473 · Cited by 53 · see citing works
Developing a classifier model for lung tumors in CT-scan images
Basu, S., Hall, L., Goldgof, D. B., Gu, Y., Kumar, V., Choi, J. W., Gillies, R. J., & Gatenby, R. A. (2011) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.2011.6083840 · Cited by 51 · cited 2× · see citing works
“These selected features can also be used to develop classifier models based on machine learning algorithms to improve the performance [29].”
BackgroundSelf-citation
Non-rigid image registration of brain magnetic resonance images using graph-cuts
So, R. W. K., Tang, T. W. H., & Chung, A. C. S. (2011) · Pattern Recognition · doi:10.1016/j.patcog.2011.04.008 · Cited by 50 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Dynamic contrast-enhanced MRI in ovarian cancer: Initial experience at 3 tesla in primary and metastatic disease
Priest, A. N., Gill, A. B., Kataoka, M., McLean, M. A., Joubert, I., Graves, M. J., Griffiths, J. R., Crawford, R., Earl, H. M., Brenton, J. D., et al. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22291 · Cited by 49 · cited 1× · see citing works
“Different investigators use different methods to convert DCE MRI signal intensities to contrast agent concentration [23–25].”
Method
Texture feature extraction using gray level gradient based co-occurence matrices
Lam, S. W.-C. (2002) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/icsmc.1996.569778 · Cited by 47 · see citing works
A level set method based on the Bayesian risk for medical image segmentation
Chen, Y.-T. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.05.027 · Cited by 43 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Quantitative histogram analysis of images
Holub, O., & Ferreira, S. T. (2006) · Computer Physics Communications · doi:10.1016/j.cpc.2006.06.014 · Cited by 42 · cited 1× · see citing works
“, mean, median, min, max, range, skewness, kurtosis), but also more complex values, such as metabolic volume above an absolute SUV of 5 or the fraction of highdensity tissue measured with CT [60,61].”
Background
Interactive segmentation based on the live wire for 3D CT chest image analysis
Lu, K., & Higgins, W. E. (2007) · International Journal of Computer Assisted Radiology and Surgery · doi:10.1007/s11548-007-0129-x · Cited by 41 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Graph cut with ordering constraints on labels and its applications
Liu, X., Veksler, O., & Samarabandu, J. K. (2008) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2008.4587470 · Cited by 37 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Comparison of quantitative parameters in cervix cancer measured by dynamic contrast–enhanced MRI and CT
Yang, C., Stadler, W. M., Karczmar, G. S., Milosevic, M., Yeung, I. W. T., & Haider, M. A. (2010) · Magnetic Resonance in Medicine · doi:10.1002/mrm.22371 · Cited by 36 · cited 1× · see citing works
“Different investigators use different methods to convert DCE MRI signal intensities to contrast agent concentration [23–25].”
Method
Quantification of perfusion and permeability in breast tumors with a deconvolution‐based analysis of second‐bolus T1‐DCE data
Makkat, S., Luypaert, R., Sourbron, S. P., Stadnik, T. W., & Mey, J. D. (2007) · Journal of Magnetic Resonance Imaging · doi:10.1002/jmri.20937 · Cited by 35 · cited 1× · see citing works
“Results of the DCE MRI depend on the contrast agent dose, method of administration, pulse sequence used, field strength of the scanner and the analysis method used [20–22].”
Method
An open-source toolkit for the volumetric measurement of CT lung lesions
Krishnan, K., Ibanez, L., Turner, W. D., Jomier, J., & Avila, R. S. (2010) · Optics Express · doi:10.1364/oe.18.015256 · Cited by 34 · see citing works
Constraint factor graph cut–based active contour method for automated cellular image segmentation in RNAi screening
CHEN, C., Li, H., Zhou, X., & Wong, S. T. C. (2008) · Journal of Microscopy · doi:10.1111/j.1365-2818.2008.01974.x · Cited by 33 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Mapping LIDC, RadLex™, and Lung Nodule Image Features
Opulencia, P., Channin, D. S., Raicu, D. S., & Furst, J. D. (2010) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-010-9285-6 · Cited by 30 · cited 1× · see citing works
“There are efforts to develop a standardized lexicon for the description of such lesions [4,5] and to include these descriptors via annotated image markup into quantitative, mineable data [6,7].”
Background
Segmentation of elastographic images using a coarse-to-fine active contour model
Liu, W., Zagzebski, J. A., Varghese, T., Dyer, C. R., Techavipoo, U., & Hall, T. J. (2006) · Ultrasound in Medicine & Biology · doi:10.1016/j.ultrasmedbio.2005.11.011 · Cited by 29 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
A Context‐Sensitive Active Contour for 2D Corpus Callosum Segmentation
He, Q., Duan, Y., Miles, J. H., & Takahashi, N. (2007) · International Journal of Biomedical Imaging · doi:10.1155/2007/24826 · Cited by 26 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Toward a fully de-identified biomedical information warehouse.
Liu, J., Erdal, S., Silvey, S. A., Ding, J., Riedel, J. D., Marsh, C. B., & Kamal, J. (2009) · PubMed Central · Cited by 25 · see citing works
Characterization of image heterogeneity using 2D Minkowski functionals increases the sensitivity of detection of a targeted MRI contrast agent
Canuto, H. C., McLachlan, C., Kettunen, M. I., Velić, M., Krishnan, A. S., Neves, A., de Backer, M., Hu, D., Hobson, M., & Brindle, K. M. (2009) · Magnetic Resonance in Medicine · doi:10.1002/mrm.21946 · Cited by 21 · see citing works
Blood glucose level normalization and accurate timing improves the accuracy of PET-based treatment response predictions in rectal cancer
Janssen, M. H. M., Öllers, M., van Stiphout, R. G. P. M., Riedl, R. G., van den Bogaard, J., Buijsen, J., Lambin, P., & Lammering, G. (2010) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2010.01.021 · Cited by 18 · cited 1× · see citing works
“From a patient protocol perspective, administration issues (residual activity in syringe, paravenous administration), blood glucose level [17], uptake period, breathing, patient comfort and inflammation all influence the quantitation of the standardized uptake value (SUV) of 18-FDG.”
BackgroundSelf-citation
Segmentation of the central-chest lymph nodes in 3D MDCT images
Lu, K., & Higgins, W. E. (2011) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2011.06.014 · Cited by 17 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
RECIST: right time to renovate?
Burton, A. (2007) · The Lancet Oncology · doi:10.1016/s1470-2045(07)70155-9 · Cited by 16 · cited 1× · see citing works
“These measures do not reflect the complexity of tumor morphology or behavior, nor, in many cases, are changes in these measures predictive of therapeutic benefit [3].”
Background
Iterative live wire and live snake: new user-steered 3D image segmentation paradigms
Souza, A., Udupa, J. K., Grevera, G. J., Sun, Y., Odhner, D., Suri, N., & Schnall, M. D. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.651333 · Cited by 16 · cited 1× · see citing works
“Many popular segmentation algorithms have been applied in medical imaging studies within the last 20 years; the most popular ones include region-growing methods [31,32], level set methods [33–38], graph cut methods [39–44], active contours (snake) algorithms [45–49] and semiautomatic segmentations such as livewires [5…”
Method
Evaluation of accuracy in MS lesion volumetry using realistic lesion phantoms
Rexilius, J., Hahn, H. K., Schlüter, M., Bourquain, H., & Peitgen, H. (2005) · Academic Radiology · doi:10.1016/j.acra.2004.10.059 · Cited by 13 · cited 1× · see citing works
“In the majority of cases, manual segmentation tends to overestimate the lesion volume to ensure the entire lesion is identified [54], and the process is highly variable [55,56].”
Background
ReliefF-Based Feature Selection for Automatic Tumor Classification of Mammogram Images
Heshmati, A., Amjadifard, R., & Shanbehzadeh, J. (2011) · Iranian Conference on Machine Vision and Image Processing · doi:10.1109/iranianmvip.2011.6121616 · Cited by 8 · see citing works
Increased classification accuracy and speedup through pair-wise feature selection for support vector machines
Kramer, K., Goldgof, D. B., Hall, L., & Remsen, A. (2011) · IEEE Symposium on Computational Intelligence and Data Mining (CIDM) · doi:10.1109/cidm.2011.5949457 · Cited by 8 · see citing works
Self-citation
Optimum compactness structures derived from the regular octahedron
Suárez, J., Gancedo, E., Álvarez, J. M., & Morán, A. (2008) · Engineering Structures · doi:10.1016/j.engstruct.2008.04.030 · Cited by 6 · cited 1× · see citing works
“Second-order statistics or co-occurrence matrix features can be used for texture classification [64–66] and are widely applied in medical pattern recognition tasks [67–71].”
Method

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Radiomics: Images Are More than Pictures, They Are Data
Gillies, R. J., Kinahan, P. E., & Hricak, H. (2016) · Radiology · doi:10.1148/radiol.2015151169 · Cited by 8,421 · see citing works
Self-citation
Radiomics: the bridge between medical imaging and personalized medicine
Lambin, P., Leijenaar, R. T. H., Deist, T. M., Peerlings, J., de Jong, E. E. C., van Timmeren, J., Sanduleanu, S., Larue, R. T. H. M., Even, A. J. G., Jochems, A., et al. (2017) · Nature Reviews Clinical Oncology · doi:10.1038/nrclinonc.2017.141 · Cited by 5,688 · see citing works
Self-citation
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 · see citing works
Self-citation
The Image Biomarker Standardization Initiative: Standardized Quantitative Radiomics for High-Throughput Image-based Phenotyping
Zwanenburg, A., Vallières, M., Abdalah, M. A., Aerts, H. J. W. L., Andrearczyk, V., Apte, A., Ashrafinia, S., Bakas, S., Beukinga, R. J., Boellaard, R., et al. (2020) · Radiology · doi:10.1148/radiol.2020191145 · Cited by 4,177 · see citing works
“5 iv Chapter 1 On this document While analysis of medical images has practically taken place since the ﬁrst image was recorded, high throughput analysis of medical images is a more recent phenomenon (Kumar et al., 2012; Lambin et al., 2012; Gillies et al., 2015). The aim of such a radiomics process is to provide decis…”
BackgroundSelf-citation
Artificial intelligence in radiology
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018) · Nature Reviews Cancer · doi:10.1038/s41568-018-0016-5 · Cited by 3,803 · see citing works
Self-citation
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
Bakas, S., Akbari, H., Sotiras, A., Bilello, M., Rozycki, M., Kirby, J. S., Freymann, J. B., Farahani, K., & Davatzikos, C. (2017) · Scientific Data · doi:10.1038/sdata.2017.117 · Cited by 3,049 · see citing works
“A subset of the pre-operative scans included in the generated data [Data Citation 3 and Data Citation 4] was also part of the BraTS ’ 15 dataset (Table 4 (available online only)), which were skull-stripped, co-registered to the same anatomical template and resampled to 1 mm 3 voxel resolution by the challenge organize…”
Influential
Introduction to Radiomics
Mayerhoefer, M. E., Materka, A., Langs, G., Häggström, I., Szczypiński, P., Gibbs, P., & Cook, G. (2020) · Journal of Nuclear Medicine · doi:10.2967/jnumed.118.222893 · Cited by 1,777 · see citing works
“, interpatient variability) are preferable (39).”
Radiomics: the facts and the challenges of image analysis
Rizzo, S., Botta, F., Raimondi, S., Origgi, D., Fanciullo, C., MORGANTI, A. G., & Bellomi, M. (2018) · European Radiology Experimental · doi:10.1186/s41747-018-0068-z · Cited by 1,048 · see citing works
“4) and not always feasible for radiomics ’ analysis, requiring very large data sets [46].”
Background
The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges
Liu, Z., Wang, S., Dong, D., Wei, J., Fang, C., Zhou, X., Sun, K., Li, L., Li, B., Wang, M., et al. (2019) · Theranostics · doi:10.7150/thno.30309 · Cited by 969 · see citing works
“The concept of radiomics was first proposed in 2012 [1, 2], and has since attracted the attention of researchers throughout the world (Figure 1).”
BackgroundMethod
Repeatability and Reproducibility of Radiomic Features: A Systematic Review
Traverso, A., Wee, L., Dekker, A. L. A. J., & Gillies, R. J. (2018) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2018.05.053 · Cited by 915 · see citing works
““Reproducibility” refers to features that remain the same when imaged using different equipment, different software, different image acquisition settings, or different operators (eg, other clinics), be that in the same subject or in different subjects (14, 20).”
Self-citation
A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities
Vallières, M., Freeman, C. R., Skamene, S., & Naqa, I. E. (2015) · Physics in Medicine and Biology · doi:10.1088/0031-9155/60/14/5471 · Cited by 892 · see citing works
Predicting cancer outcomes with radiomics and artificial intelligence in radiology
Bera, K., Braman, N., Gupta, A., Velcheti, V., & Madabhushi, A. (2022) · Nature Reviews Clinical Oncology · doi:10.1038/s41571-021-00560-7 · Cited by 819 · see citing works
Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology
Limkin, E. J., Sun, R., Dercle, L., Zacharaki, E. I., Robert, C., Reuzé, S., Schernberg, A., Nikos Paragios, A., Deutsch, É., & Ferté, C. (2017) · Annals of Oncology · doi:10.1093/annonc/mdx034 · Cited by 756 · see citing works
Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer
Huang, Y., Liu, Z., He, L., Chen, X., Pan, D., Ma, Z., Liang, C., Tian, J., & Liang, C. (2016) · Radiology · doi:10.1148/radiol.2016152234 · Cited by 741 · see citing works
AI in Medical Imaging Informatics: Current Challenges and Future Directions
Panayides, A. S., Amini, A., Filipovic, N. D., Sharma, A., Tsaftaris, S. A., Young, A., Foran, D., Do, N., Golemati, S., Kurc, T., et al. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2020.2991043 · Cited by 713 · see citing works
CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma
Coroller, T. P., Grossmann, P., Hou, Y., Rios Velazquez, E., Leijenaar, R. T. H., Hermann, G., Lambin, P., Haibe-Kains, B., Mak, R. H., & Aerts, H. J. W. L. (2015) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2015.02.015 · Cited by 695 · see citing works
“Radiomics [7–9] aims to provide a comprehensive quantification of the tumor phenotype by analyzing robustly [10–12] a large set of quantitative data characterization algorithms.”
Self-citation
The Potential of Radiomic-Based Phenotyping in Precision Medicine: A Review
Aerts, H. J. W. L. (2016) · JAMA Oncology · doi:10.1001/jamaoncol.2016.2631 · Cited by 669 · see citing works
Self-citation
Non‐small cell lung cancer in China
Chen, P., Liu, Y., Wen, Y., & Zhou, C. (2022) · 癌症：英文版 · doi:10.1002/cac2.12359 · Cited by 673 · see citing works
“Radiomics is a novel technology developed in recent years that might improve the specificity and accuracy of lung cancer diagnosis [114].”
Background
Measuring Computed Tomography Scanner Variability of Radiomics Features
Mackin, D., Fave, X. J., Zhang, L., Fried, D., Yang, J., Taylor, B. A., Rodriguez-Rivera, E., Dodge, C. T., Jones, A. K., & Court, L. E. (2015) · Investigative Radiology · doi:10.1097/rli.0000000000000180 · Cited by 649 · see citing works
Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters
Berenguer, R., Pastor-Juan, M. D. R., Canales‐Vázquez, J., Castro‐García, M., Villas, M. V., Legorburo, F. M., & Sabater, S. (2018) · Radiology · doi:10.1148/radiol.2018172361 · Cited by 622 · see citing works
Machine and deep learning methods for radiomics
Avanzo, M., Wei, L., Stancanello, J., Vallières, M., Rao, A. U. K., Morin, O., Mattonen, S. A., & Naqa, I. E. (2020) · Medical Physics · doi:10.1002/mp.13678 · Cited by 614 · see citing works
Robust Radiomics Feature Quantification Using Semiautomatic Volumetric Segmentation
Parmar, C., Velazquez, E. R., Leijenaar, R. T. H., Jermoumi, M., Carvalho, S., Mak, R. H., Mitra, S., Shankar, B. U., Kikinis, R., Haibe‐Kains, B., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0102107 · Cited by 611 · see citing works
“Radiomics addresses this issue, and refers to the high throughput extraction of a large number of quantitative and minable imaging features, assuming that these features convey prognostic and predictive information [3,4].”
BackgroundMethodInfluentialSelf-citation
Surgical data science for next-generation interventions
Maier‐Hein, L., Vedula, S. S., Speidel, S., Navab, N., Kikinis, R., Park, A., Eisenmann, M., Feußner, H., Forestier, G., Giannarou, S., et al. (2017) · Nature Biomedical Engineering · doi:10.1038/s41551-017-0132-7 · Cited by 616 · see citing works
Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels
Shafiq‐ul‐Hassan, M., Zhang, G. G., Latifi, K., Ullah, G., Hunt, D., Balagurunathan, Y., Abdalah, M. A., Schabath, M. B., Goldgof, D. B., Mackin, D., et al. (2017) · Medical Physics · doi:10.1002/mp.12123 · Cited by 587 · see citing works
Self-citation
From big data analysis to personalized medicine for all: challenges and opportunities
Alyass, A., Turcotte, M., & Meyre, D. (2015) · BMC Medical Genomics · doi:10.1186/s12920-015-0108-y · Cited by 557 · see citing works
“integrate, analyze, and interpret omics data [10–12].”
Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer
Liu, Z., Zhang, X.-Y., Shi, Y., Wang, L., Zhu, H., Tang, Z., Wang, S., Li, X.-T., Tian, J., & Sun, Y. (2017) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-1038 · Cited by 548 · see citing works
“Radiomics, which is based on advanced pattern recognition tools, involves the extraction of a large number of quantitative features from digital images to determine relationships between such features and the underlying pathophysiology (17, 18).”
Background
Applications of multi‐omics analysis in human diseases
Chen, C., Wang, J., Pan, D., Wang, X., Xu, Y., Yan, J., Wang, L., Yang, X., Yang, M., & Liu, G. (2023) · MedComm · doi:10.1002/mco2.315 · Cited by 545 · see citing works
Radiomics strategies for risk assessment of tumour failure in head-and-neck cancer
Vallières, M., Kay‐Rivest, E., Perrin, L. J., Liem, X., Furstoss, C., Aerts, H. J. W. L., Khaouam, N., Nguyen‐Tan, P. F., Wang, C.-S., Sultanem, K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-10371-5 · Cited by 543 · see citing works
Self-citation
Reproducibility of radiomics for deciphering tumor phenotype with imaging
Zhao, B., Tan, Y., Tsai, W., Qi, J., Xie, C., Lü, L., & Schwartz, L. H. (2016) · Scientific Reports · doi:10.1038/srep23428 · Cited by 509 · see citing works
Beyond imaging: The promise of radiomics
Avanzo, M., Stancanello, J., & Naqa, I. E. (2017) · Physica Medica · doi:10.1016/j.ejmp.2017.05.071 · Cited by 489 · see citing works
“The fundamental hypothesis of radiomics is that quantitative analysis of tumor through a large amount of radiomic features can provide valuable diagnostic, prognostic or predictive information [19,20].”
BackgroundMethodInfluential
Radiomics and radiogenomics in lung cancer: A review for the clinician
Thawani, R., McLane, M. J., Beig, N. G., Ghose, S., Prasanna, P., Velcheti, V., & Madabhushi, A. (2017) · Lung Cancer · doi:10.1016/j.lungcan.2017.10.015 · Cited by 451 · see citing works
Neuroimaging and neuromodulation approaches to study eating behavior and prevent and treat eating disorders and obesity
Val‐Laillet, D., Aarts, E., Weber, B., Ferrari, M., Quaresima, V., Stoeckel, L. E., Alonso‐Alonso, M., Audette, M. A., Malbert, C., & Stice, E. M. (2015) · NeuroImage Clinical · doi:10.1016/j.nicl.2015.03.016 · Cited by 443 · see citing works
A deep look into radiomics
Scapicchio, C., Gabelloni, M., Barucci, A., Cioni, D., Saba, L., & Neri, E. (2021) · La radiologia medica · doi:10.1007/s11547-021-01389-x · Cited by 435 · see citing works
Radiomic feature clusters and Prognostic Signatures specific for Lung and Head & Neck cancer
Parmar, C., Leijenaar, R. T. H., Großmann, P., Velazquez, E. R., Bussink, J., Rietveld, D. H. F., Rietbergen, M. M., Haibe‐Kains, B., Lambin, P., & Aerts, H. J. W. L. (2015) · Scientific Reports · doi:10.1038/srep11044 · Cited by 433 · see citing works
Self-citation
Stability of FDG-PET Radiomics features: An integrated analysis of test-retest and inter-observer variability
Leijenaar, R. T. H., Carvalho, S., Velazquez, E. R., van Elmpt, W., Parmar, C., Hoekstra, O. S., Hoekstra, C. J., Boellaard, R., Dekker, A. L. A. J., Gillies, R. J., et al. (2013) · Acta Oncologica · doi:10.3109/0284186x.2013.812798 · Cited by 428 · see citing works
Self-citation
Radiomics in Brain Tumor: Image Assessment, Quantitative Feature Descriptors, and Machine-Learning Approaches
Zhou, M., Scott, J. G., Chaudhury, B., Hall, L., Goldgof, D. B., Yeom, K. W., Iv, M., Ou, Y., Kalpathy–Cramer, J., Napel, S. A., et al. (2017) · American Journal of Neuroradiology · doi:10.3174/ajnr.a5391 · Cited by 415 · see citing works
“to deepen our understanding of imaging characteristics in cancer.(1,2) The identified quantitative features can be used to alert”
BackgroundSelf-citation
Radiomic Machine-Learning Classifiers for Prognostic Biomarkers of Head and Neck Cancer
Parmar, C., Großmann, P., Rietveld, D. H. F., Rietbergen, M. M., Lambin, P., & Aerts, H. J. W. L. (2015) · Frontiers in Oncology · doi:10.3389/fonc.2015.00272 · Cited by 410 · see citing works
“Predictive and prognostic models are an important part of radiomics (27).”
BackgroundSelf-citation
Biobanking in health care: evolution and future directions
Coppola, L., Cianflone, A., Grimaldi, A., Incoronato, M., Bevilacqua, P., Messina, F., Baselice, S., Soricelli, A., Mirabelli, P., & Salvatore, M. (2019) · Journal of Translational Medicine · doi:10.1186/s12967-019-1922-3 · Cited by 408 · see citing works
Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives
Park, J. E., Park, S. Y., Kim, H. J., & Kim, H. S. (2019) · Korean Journal of Radiology · doi:10.3348/kjr.2018.0070 · Cited by 388 · see citing works
“The signal intensities on MRI result from a complex interplay of tissue relaxation time and imaging acquisition (9), with information on MRI not based solely on tissue properties.”
BackgroundMethodInfluential
Radiomics in Oncology: A Practical Guide
Shur, J. D., Doran, S., Kumar, S., Dafydd, D. A., Downey, K., O’Connor, J. P. B., Papanikolaou, N. K., Messiou, C., Koh, D., & Orton, M. R. (2021) · Radiographics · doi:10.1148/rg.2021210037 · Cited by 388 · see citing works
Defining the biological basis of radiomic phenotypes in lung cancer
Grossmann, P., Stringfield, O., El-Hachem, N., Bui, M. M., Rios Velazquez, E., Parmar, C., Leijenaar, R. T., Haibe-Kains, B., Lambin, P., Gillies, R. J., et al. (2017) · eLife · doi:10.7554/elife.23421 · Cited by 365 · see citing works
“…quantitative imaging features that objectively define tumor intensity, shape, size, and texture (Gillies et al., 2016; Aerts, 2016; Lambin et al., 2012; Kumar et al., 2012) in a robust and reproducible way (Zhao et al., 2016; Fried et al., 2014; Balagurunathan et al., 2014; Leijenaar et al., 2013).”
BackgroundSelf-citation
Principles and methods of integrative genomic analyses in cancer
Kristensen, V. N., Lingjærde, O. C., Russnes, H. G., Vollan, H. K. M., Frigessi, A., & Børresen‐Dale, A. (2014) · Nature reviews. Cancer · doi:10.1038/nrc3721 · Cited by 368 · see citing works
Radiomics: a new application from established techniques
Parekh, V. S., & Jacobs, M. A. (2016) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2016.1164013 · Cited by 373 · see citing works
Unravelling tumour heterogeneity using next-generation imaging: radiomics, radiogenomics, and habitat imaging
Sala, E., Mema, E., Himoto, Y., Veeraraghavan, H., Brenton, J. D., Snyder, A., Weigelt, B., & Vargas, H. A. (2016) · Clinical Radiology · doi:10.1016/j.crad.2016.09.013 · Cited by 369 · see citing works
“First, it requires standardisation of imaging protocols, including image acquisition and post-processing, as well as robust segmentation algorithms that require minimal operator input (13).”
BackgroundMethodInfluential
Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures
Larue, R. T. H. M., Defraene, G., Ruysscher, D. D., Lambin, P., & van Elmpt, W. (2016) · British Journal of Radiology · doi:10.1259/bjr.20160665 · Cited by 361 · see citing works
“lung lobe, distance to the organ at risk) have been used.(72)”
BackgroundInfluentialSelf-citation
Delta-radiomics features for the prediction of patient outcomes in non–small cell lung cancer
Fave, X. J., Zhang, L., Yang, J., Mackin, D., Balter, P. A., Gomez, D. R., Followill, D. S., Jones, A. K., Stingo, F. C., Liao, Z. X., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-00665-z · Cited by 353 · see citing works
A review in radiomics: Making personalized medicine a reality via routine imaging
Guiot, J., Vaidyanathan, A., Deprez, L., Zerka, F., Danthine, D., Frix, A.-N., Lambin, P., Bottari, F., Tsoutzidis, N., Miraglio, B., et al. (2021) · Medicinal Research Reviews · doi:10.1002/med.21846 · Cited by 344 · see citing works
“The hypothesis is that quantitative analysis of medical image data can provide complementary information to aid physicians in the decision‐making process, aided by automatic or semiautomatic software, in a fast and reproducible way.(5) Radiomics is the result of several decades of computer‐aided diagnosis, prognosis,…”
BackgroundSelf-citation
From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities
Afshar, P., Mohammadi, A., Plataniotis, K. N., Oikonomou, A., & Benali, H. (2019) · IEEE Signal Processing Magazine · doi:10.1109/msp.2019.2900993 · Cited by 334 · see citing works
“GLRLM A matrix that presents the length of consecutive pixels having the same intensity [62] Short-run emphasis; long-run emphasis; gray-level nonuniformity; run-length nonuniformity; run percentage; low gray-level run emphasis; high gray-level run emphasis [2]”
BackgroundMethodInfluential
Deep Learning based Radiomics (DLR) and its usage in noninvasive IDH1 prediction for low grade glioma
Li, Z., Wang, Y., Yu, J., Guo, Y., & Cao, W. (2017) · Scientific Reports · doi:10.1038/s41598-017-05848-2 · Cited by 331 · see citing works
“Normally, the process of radiomics analysis includes image acquisition, image segmentation, feature extraction, feature selection and informatics analyses 3 .”
Method
Radiomic phenotype features predict pathological response in non-small cell lung cancer
Coroller, T. P., Agrawal, V., Narayan, V., Hou, Y., Großmann, P., Lee, S. W., Mak, R. H., & Aerts, H. J. W. L. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.04.004 · Cited by 327 · see citing works
Self-citation
Radiomic Features Are Associated With EGFR Mutation Status in Lung Adenocarcinomas
Liu, Y., Kim, J., Balagurunathan, Y., Li, Q., García, A. L., Stringfield, O., Ye, Z., & Gillies, R. J. (2016) · Clinical Lung Cancer · doi:10.1016/j.cllc.2016.02.001 · Cited by 312 · see citing works
Self-citation
ibex : An open infrastructure software platform to facilitate collaborative work in radiomics
Zhang, L., Fried, D., Fave, X. J., Hunter, L. A., Yang, J., & Court, L. E. (2015) · Medical Physics · doi:10.1118/1.4908210 · Cited by 316 · see citing works
Radiogenomics: What It Is and Why It Is Important
Mazurowski, M. A. (2015) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2015.04.019 · Cited by 289 · see citing works
“Finally, ‘ radiogenomics’ has been equated with another approach called ‘ radiomics’ [5], [6], [7].”
Background
FeAture Explorer (FAE): A tool for developing and comparing radiomics models
Song, Y. S., Zhang, J., Zhang, Y., Hou, Y., Yan, X., Wang, Y., Zhou, M., Yao, Y., & Yang, G. (2020) · PLoS ONE · doi:10.1371/journal.pone.0237587 · Cited by 287 · see citing works
“Radiomics extracts a large number of quantitative features from the regions of interest (ROIs) in medical images and maps them to clinical conclusions with machine learning (ML) [1, 2].”
Background
Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment
Pinker, K., Chin, J., Melsaether, A. N., Morris, E. A., & Moy, L. (2018) · Radiology · doi:10.1148/radiol.2018172171 · Cited by 275 · see citing works
Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer
Zhang, Y., Oikonomou, A., Wong, A. M., Haider, M. A., & Khalvati, F. (2017) · Scientific Reports · doi:10.1038/srep46349 · Cited by 269 · see citing works
Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features
Feng, X., Tustison, N. J., Patel, S. H., & Meyer, C. H. (2020) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2020.00025 · Cited by 268 · see citing works
“…segmentation of the tumor region has shown advantages in image-based tumor phenotyping over traditionally used clinical measures such as largest anterior-posterior, transverse, and inferior-superior tumor dimensions on a subjectively-chosen slice (Kumar et al., 2012; Gillies et al., 2016).”
Method
Stability of radiomics features in apparent diffusion coefficient maps from a multi-centre test-retest trial
Peerlings, J., Woodruff, H. C., Winfield, J. M., Ibrahim, A., BEERS, B. E. V., Heerschap, A., Jackson, A., Wildberger, J. E., Mottaghy, F. M., deSouza, N. M., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-41344-5 · Cited by 267 · see citing works
Self-citation
Gray-level invariant Haralick texture features
Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., & Garpebring, A. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212110 · Cited by 264 · see citing works
Assessing robustness of radiomic features by image perturbation
Zwanenburg, A., Leger, S., Agolli, L., Pilz, K., Troost, E. G. C., Richter, C., & Löck, S. (2019) · Scientific Reports · doi:10.1038/s41598-018-36938-4 · Cited by 258 · see citing works
“4 All intensity values outside range r are replaced by the nearest valid intensity: I j = 8 >> < >> : r 1; I j<r 1 r 2; I >r I j; otherwise (b) Intensities are then mapped to the [0;1] range by a simple transformation: I j;s= I j r 1 r 2 r 1 3. The number of supervoxels is estimated so that on ave…”
BackgroundMethodInfluential
Effects of contrast-enhancement, reconstruction slice thickness and convolution kernel on the diagnostic performance of radiomics signature in solitary pulmonary nodule
He, L., Huang, Y., Ma, Z., Liang, C., Liang, C., & Liu, Z. (2016) · Scientific Reports · doi:10.1038/srep34921 · Cited by 253 · see citing works
Image-Based Cardiac Diagnosis With Machine Learning: A Review
Martín-Isla, C., Campello, V. M., Izquierdo, C., Raisi‐Estabragh, Z., Baeßler, B., Petersen, S. E., & Lekadir, K. (2020) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2020.00001 · Cited by 250 · see citing works
“Introduced in 2012 (11, 12), the radiomics paradigm was, for a long time, mostly exploited in oncology (13).”
Background
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 · see citing works
Deep learning and radiomics in precision medicine
Parekh, V. S., & Jacobs, M. A. (2019) · Expert Review of Precision Medicine and Drug Development · doi:10.1080/23808993.2019.1585805 · Cited by 245 · see citing works
“In radiology, Radiomics are textural mathematical constructs that capture the spatial appearance of the tissue of interest (shape and texture) on different types of images using texture [10–13 , 50– 53].”
BackgroundMethod
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 245 · see citing works
“, their value remains the same when repeated multiple times on a single scanner]) need to be identified [42, 43] (Fig.”
Background
Radiomics Signature on Magnetic Resonance Imaging: Association with Disease-Free Survival in Patients with Invasive Breast Cancer
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 · see citing works
“Other pathologic diagnoses in the 17 patients were as follows:mucinous carcinoma (7), medullary carcinoma (2), invasive micropapillary carcinoma (3), tubular carcinoma (1), invasive apocrine carcinoma (3), and adenoid cystic carcinoma (1).”
Background
Volumetric CT-based segmentation of NSCLC using 3D-Slicer
Velazquez, E. R., Parmar, C., Jermoumi, M., Mak, R. H., van Baardwijk, A. A. W., Fennessy, F. M., Lewis, J. H., Ruysscher, D. D., Kikinis, R., Lambin, P., et al. (2013) · Scientific Reports · doi:10.1038/srep03529 · Cited by 240 · see citing works
Self-citation
Repeatability of Multiparametric Prostate MRI Radiomics Features
Schwier, M., van Griethuysen, J. J. M., Vangel, M. G., Pieper, S., Peled, S., Tempany, C. M., Aerts, H. J. W. L., Kikinis, R., Fennessy, F. M., & Fedorov, A. (2019) · Scientific Reports · doi:10.1038/s41598-019-45766-z · Cited by 232 · see citing works
“Since there is no systematic difference between the first and second scans, we model both between-subject and within-subject sources of variability as independent random effects and ICC(1,1) is therefore appropriate.”
MethodSelf-citation
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 244 · see citing works
Self-citation
Virtual clinical trials in medical imaging: a review
Abadi, E., Segars, W. P., Tsui, B. M. W., Kinahan, P. E., Bottenus, N. B., Frangi, A. F., Maidment, A. D. A., Lo, J. Y.-C., & Samei, E. (2020) · Journal of medical imaging · doi:10.1117/1.jmi.7.4.042805 · Cited by 230 · see citing works
“267 – 269.”
Deep Learning and Radiomics predict complete response after neo-adjuvant chemoradiation for locally advanced rectal cancer
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 · see citing works
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 229 · see citing works
Self-citation
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 229 · see citing works
“This type of information is crucial for the diagnosis of tumors, but it does not accurately reflect the morphological and behavioral complexities of a tumor, with limited benefits in the judgment of treatment sensitivity and prognosis [255].”
Background
Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization
Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., et al. (2021) · Physica Medica · doi:10.1016/j.ejmp.2021.03.009 · Cited by 226 · see citing works
“Shape features [19]: provide quantitative description of geometric properties of the ROIs/VOIs, such as surface area, total volume, diameter, sphericity or surface-to-volume ratio.”
Background
Lung Texture in Serial Thoracic Computed Tomography Scans: Correlation of Radiomics-based Features With Radiation Therapy Dose and Radiation Pneumonitis Development
Cunliffe, A. R., Armato, S. G., Castillo, R., Pham, N., Guerrero, T. M., & Al‐Hallaq, H. (2015) · International Journal of Radiation Oncology*Biology*Physics · doi:10.1016/j.ijrobp.2014.11.030 · Cited by 225 · see citing works
“0-10 Gy 503 (165-776) 532 (300-936) 10-30 Gy 162 (17-398) 118 (22-294) 30 Gy 49 (1-154) 27 (0-90)”
Machine learning-based quantitative texture analysis of CT images of small renal masses: Differentiation of angiomyolipoma without visible fat from renal cell carcinoma
Feng, Z., Rong, P., Cao, P., Zhou, Q. S., Zhu, W., Yan, Z., Liu, Q., & Wang, W. (2017) · European Radiology · doi:10.1007/s00330-017-5118-z · Cited by 224 · see citing works
Incorporating diffusion- and perfusion-weighted MRI into a radiomics model improves diagnostic performance for pseudoprogression in glioblastoma patients
Kim, J. Y., Park, J. E., Jo, Y., Shim, W. H., Nam, S. J., Kim, J. H., Yoo, R., Choi, S. H., & Kim, H. S. (2018) · Neuro-Oncology · doi:10.1093/neuonc/noy133 · Cited by 222 · see citing works
Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis
Sun, W., Zheng, B., & Qian, W. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.04.006 · Cited by 223 · see citing works
External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma
Leijenaar, R. T. H., Carvalho, S., Hoebers, F. J. P., Aerts, H. J. W. L., van Elmpt, W., Huang, S. H., Chan, H. B., Waldron, J. N., O’Sullivan, B., & Lambin, P. (2015) · Acta Oncologica · doi:10.3109/0284186x.2015.1061214 · Cited by 216 · see citing works
Self-citation
Integrative methods for analyzing big data in precision medicine
Gligorijević, V., Malod‐Dognin, N., & Pržulj, N. (2015) · PROTEOMICS · doi:10.1002/pmic.201500396 · Cited by 216 · see citing works
“Even medical imaging, which was traditionally manually investigated, is now a subject of high-throughput capturing [14, 15].”
Background
CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer
Huynh, E., Coroller, T. P., Narayan, V., Agrawal, V., Hou, Y., Romano, J., Franco, I. “., Mak, R. H., & Aerts, H. J. W. L. (2016) · Radiotherapy and Oncology · doi:10.1016/j.radonc.2016.05.024 · Cited by 215 · see citing works
Self-citation
Voxel size and gray level normalization of CT radiomic features in lung cancer
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 · see citing works
Self-citation
Radiomics 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 220 · see citing works
Defining a Radiomic Response Phenotype: A Pilot Study using targeted therapy in NSCLC
Aerts, H. J. W. L., Großmann, P., Tan, Y., Oxnard, G. R., Rizvi, N. A., Schwartz, L. H., & Zhao, B. (2016) · Scientific Reports · doi:10.1038/srep33860 · Cited by 213 · see citing works
Self-citation
Radiomic signature as a diagnostic factor for histologic subtype classification of non-small cell lung cancer
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 · see citing works
Making Radiomics More Reproducible across Scanner and Imaging Protocol Variations: A Review of Harmonization Methods
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 218 · see citing works
“The radiomic hypothesis postulates that the quantitative study of medical image data can provide complementary knowledge in a quick and reproducible manner to support clinicians in their decision-making process, assisted by automated or semi-automated software [5,6].”
BackgroundSelf-citation
Bladder Cancer Treatment Response Assessment in CT using Radiomics with Deep-Learning
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 · see citing works
The role of deep learning and radiomic feature extraction in cancer-specific predictive modelling: a review
Vial, A., Stirling, D. A., Field, M., Ros, M., Ritz, C. H., Carolan, M., Holloway, L. C., & Miller, A. (2018) · Translational Cancer Research · doi:10.21037/tcr.2018.05.02 · Cited by 207 · see citing works
“It has been found that texture features are the one of the most important imaging features for the field of radiomics (1-5, Kumar et al. (36) describe that there are a vast number of extractible radiomic features for each patient analysed, hence the quantity of features needs to be reduced to avoid…”
BackgroundInfluential
The impact of image reconstruction settings on 18F-FDG PET radiomic features: multi-scanner phantom and patient studies
Shiri, I., Rahmim, A., Ghafarian, P., Geramifar, P., Abdollahi, H., & Bitarafan‐Rajabi, A. (2017) · European Radiology · doi:10.1007/s00330-017-4859-z · Cited by 205 · see citing works
Performance comparison of modified ComBat for harmonization of radiomic features for multicenter studies
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Radiomic Analysis of Myocardial Native T1 Imaging Discriminates Between Hypertensive Heart Disease and Hypertrophic Cardiomyopathy
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Artificial intelligence-driven radiomics study in cancer: the role of feature engineering and modeling
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Background
Preoperative prediction of microvascular invasion in hepatocellular cancer: a radiomics model using Gd-EOB-DTPA-enhanced MRI
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Background
Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue.
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Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis
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MethodSelf-citation
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Radiomics: the facts and the challenges of image analysis
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Radiomics: Images Are More than Pictures, They Are Data
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Self-citation
Application of Radiomics in Radiotherapy: Challenges and Future Prospects
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Radiomics and quantitative imaging
Mackin, D., & Court, L. E. (2019) · CRC Press eBooks · doi:10.1201/9781315207582-14

Authors: Kumar, V., Gu, Y., Basu, S., Berglund, A., Eschrich, S. A., Schabath, M. B., Forster, K., Aerts, H. J. W. L., Dekker, A., Fenstermacher, D., Goldgof, D. B., Hall, L. O., Lambin, P., Balagurunathan, Y., Gatenby, R. A., & Gillies, R. J.
Affiliations: Moffitt Cancer Center (US)
University of South Florida (US)
Harvard University (US)
Maastricht University Medical Centre (NL)
Corresponding author: Gillies, Robert J.
ORCID: Kumar, Virendra
Gu, Yuhua
Berglund, Anders
Eschrich, Steven A.
Schabath, Matthew B.
Aerts, Hugo J.W.L.
Dekker, Andre
Fenstermacher, David
Goldgof, Dmitry B.
Hall, Lawrence O.
Lambin, Philippe
Balagurunathan, Yoganand
Gatenby, Robert A.
Gillies, Robert J.
Venue: Magnetic Resonance Imaging
Publisher: Elsevier BV
Published: 2012-11
Volume: 30
Issue: 9
Pages: 1234-1248
ISSN: 0730-725X (ISSN-L)
DOI: 10.1016/j.mri.2012.06.010
PMC: PMC3563280
OpenAlex: W2097475056
Cited by: 2,258
References: 104
Citations by year: 2026: 106 · 2025: 189 · 2024: 224 · 2023: 236 · 2022: 277 · 2021: 290 · 2020: 253 · 2019: 209 · 2018: 190 · 2017: 127 · 2016: 86 · 2015: 43 · 2014: 16 · 2013: 6 · 2012: 3
FWCI: 16.42
Type: journal article
Language: en
Open access: green
europepmc.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Magnetic Resonance Imaging (publishedVersion)
PubMed (publishedVersion)
Europe PMC (PubMed Central) (acceptedVersion)
PubMed Central (acceptedVersion)
Funders: Radiological Society of North America
National Cancer Institute (U01CA143062, P30CA076292)
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, quantitative imaging features, image segmentation, feature extraction, data sharing, non-small cell lung cancer, computed tomography, positron emission tomography, prediction model
MeSH terms: Algorithms, Carcinoma, Non-Small-Cell Lung, Humans, Image Processing, Computer-Assisted, Lung Neoplasms, Magnetic Resonance Imaging, Medical Informatics, Pattern Recognition, Automated, Risk Factors, Software, Tomography, X-Ray Computed, Reproducibility of Results, Multivariate Analysis, Radiation Oncology, Phantoms, Imaging, Positron-Emission Tomography

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