Texture analysis imaging “what a clinical radiologist needs to know”

Published:November 24, 2021DOI:


      Texture analysis has arisen as a tool to explore the amount of data contained in images that cannot be explored by humans visually. Radiomics is a method that extracts a large number of features from radiographic medical images using data-characterisation algorithms. These features, termed radiomic features, have the potential to uncover disease characteristics. The goal of both radiomics and texture analysis is to go beyond size or human-eye based semantic descriptors, to enable the non-invasive extraction of quantitative radiological data to correlate them with clinical outcomes or pathological characteristics. In the latest years there has been a flourishing sub-field of radiology where texture analysis and radiomics have been used in many settings. It is difficult for the clinical radiologist to cope with such amount of data in all the different radiological sub-fields and to identify the most significant papers. The aim of this review is to provide a tool to better understand the basic principles underlining texture analysis and radiological data mining and a summary of the most significant papers of the latest years.


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        • Gillies R.J.
        • Kinahan P.E.
        • Hricak H.
        Radiomics: Images Are More than Pictures.
        They Are Data. Radiology. 2016; 278: 563-577
        • Varghese B.A.
        • Cen S.Y.
        • Hwang D.H.
        • Duddalwar V.A.
        Texture Analysis of Imaging: What Radiologists Need to Know.
        Am. J. Roentgenol. 2019; 212: 520-528
        • Gregory J.
        • Dioguardi Burgio M.
        • Corrias G.
        • Vilgrain V.
        • Ronot M.
        Evaluation of liver tumour response by imaging.
        JHEP Rep. 2020; 2100100
        • AlRayahi J.
        • Zapotocky M.
        • Ramaswamy V.
        • Hanagandi P.
        • Branson H.
        • Mubarak W.
        • Raybaud C.
        • Laughlin S.
        Pediatric Brain Tumor Genetics: What Radiologists Need to Know.
        RadioGraphics. 2018; 38: 2102-2122
      1. Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials, Board on Health Care Services, Board on Health Sciences Policy, & Institute of Medicine. Evolution of Translational Omics: Lessons Learned and the Path Forward. 13297 (National Academies Press, 2012).

        • Lambin P.
        • et al.
        Radiomics: Extracting more information from medical images using advanced feature analysis.
        Eur. J. Cancer. 2012; 48: 441-446
        • Guo Z.
        • Shu Y.
        • Zhou H.
        • Zhang W.
        • Wang H.
        Radiogenomics helps to achieve personalized therapy by evaluating patient responses to radiation treatment.
        Carcinogenesis. 2015; 36: 307-317
        • West C.M.
        • Barnett G.C.
        Genetics and genomics of radiotherapy toxicity: towards prediction.
        Genome Med. 2011; 3: 52
        • Saba L.
        • et al.
        The present and future of deep learning in radiology.
        Eur. J. Radiol. 2019; 114: 14-24
        • Dohan A.
        • Gallix B.
        • Guiu B.
        • Le Malicot K.
        • Reinhold C.
        • Soyer P.
        • Bennouna J.
        • Ghiringhelli F.
        • Barbier E.
        • Boige V.
        • Taieb J.
        • Bouché O.
        • François E.
        • Phelip J.-M.
        • Borel C.
        • Faroux R.
        • Seitz J.-F.
        • Jacquot S.
        • Ben Abdelghani M.
        • Khemissa-Akouz F.
        • Genet D.
        • Jouve J.L.
        • Rinaldi Y.
        • Desseigne F.
        • Texereau P.
        • Suc E.
        • Lepage C.
        • Aparicio T.
        • Hoeffel C.
        Early evaluation using a radiomic signature of unresectable hepatic metastases to predict outcome in patients with colorectal cancer treated with FOLFIRI and bevacizumab.
        Gut. 2020; 69: 531-539
        • Wibmer A.
        • et al.
        Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores.
        Eur. Radiol. 2015; 25: 2840-2850
        • Coroller T.P.
        • et al.
        CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma.
        Radiother. Oncol. 2015; 114: 345-350
      2. S.E. Bates, It’s All About the Test: The Complexity of Companion Diagnostic Co-development in Personalized Medicine, Clin. Cancer Res. 20 (2014) 1418–1418.

        • Nguyen P.L.
        • et al.
        The impact of pathology review on treatment recommendations for patients with adenocarcinoma of the prostate.
        Urol. Oncol. Semin. Orig. Investig. 2004; 22: 295-299
        • Xu X.
        • et al.
        Radiomic analysis of contrast-enhanced CT predicts microvascular invasion and outcome in hepatocellular carcinoma.
        J. Hepatol. 2019; 70: 1133-1144
        • Mulé S.
        • Thiefin G.
        • Costentin C.
        • Durot C.
        • Rahmouni A.
        • Luciani A.
        • Hoeffel C.
        Advanced Hepatocellular Carcinoma: Pretreatment Contrast-enhanced CT Texture Parameters as Predictive Biomarkers of Survival in Patients Treated with Sorafenib.
        Radiology. 2018; 288: 445-455
        • Orlhac F.
        • Frouin F.
        • Nioche C.
        • Ayache N.
        • Buvat I.
        Validation of A Method to Compensate Multicenter Effects Affecting CT Radiomics.
        Radiology. 2019; 291: 53-59
        • Park J.E.
        • Kim H.S.
        • Kim D.
        • Park S.Y.
        • Kim J.Y.
        • Cho S.J.
        • Kim J.H.
        A systematic review reporting quality of radiomics research in neuro-oncology: toward clinical utility and quality improvement using high-dimensional imaging features.
        BMC Cancer. 2020; 20
        • Buckler A.J.
        • Bresolin L.
        • Dunnick N.R.
        • Sullivan D.C.
        C., & For the Group. A Collaborative Enterprise for Multi-Stakeholder Participation in the Advancement of Quantitative Imaging.
        Radiology. 2011; 258: 906-914
      3. Z. Liu, L. Zhang, H. Ren, J.-Y. Kim, A robust region-based active contour model with point classification for ultrasound breast lesion segmentation, in: C.L. Novak, S. Aylward (Eds.), 86701P (2013).

      4. K. Suzuki, et al., CT liver volumetry using geodesic active contour segmentation with a level-set algorithm, in: N. Karssemeijer, R.M. Summers (eds.), 76240R (2010).

        • Peng J.
        • et al.
        3D liver segmentation using multiple region appearances and graph cuts: Segmentation using multiple region appearances and graph cuts.
        Med. Phys. 2015; 42: 6840-6852
        • Sun C.
        • et al.
        Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs.
        Artif. Intell. Med. 2017; 83: 58-66
        • Segal E.
        • et al.
        Decoding global gene expression programs in liver cancer by noninvasive imaging.
        Nat. Biotechnol. 2007; 25: 675-680
        • Galloway M.M.
        Texture analysis using gray level run lengths.
        Comput. Graph. Image Process. 1975; 4: 172-179
        • Pentland A.P.
        Fractal-Based Description of Natural Scenes.
        IEEE Trans. Pattern Anal. Mach. Intell. 1984; PAMI-6: 661-674
        • Amadasun M.
        • King R.
        Textural features corresponding to textural properties.
        IEEE Trans. Syst. Man Cybern. 1989; 19: 1264-1274
        • Thibault G.
        • Angulo J.
        • Meyer F.
        Advanced Statistical Matrices for Texture Characterization: Application to Cell Classification.
        IEEE Trans. Biomed. Eng. 2014; 61: 630-637
        • Ranjbar S.
        • Ross Mitchell J.
        Biomedical Texture Analysis. Elsevier, 2017: 223-245
        • Tunali I.
        • et al.
        Stability and reproducibility of computed tomography radiomic features extracted from peritumoral regions of lung cancer lesions.
        Med. Phys. 2019; 46: 5075-5085
        • Parmar C.
        • Grossmann P.
        • Bussink J.
        • Lambin P.
        • Aerts H.J.W.L.
        Machine Learning methods for Quantitative Radiomic Biomarkers.
        Sci. Rep. 2015; 5: 13087
        • Tibshirani R.
        Regression Shrinkage and Selection Via the Lasso.
        J. R. Stat. Soc. Ser. B Methodol. 1996; 58: 267-288
        • Geladi P.
        • Esbensen K.
        Regression on multivariate images: Principal component regression for modeling, prediction and visual diagnostic tools.
        J. Chemom. 1991; 5: 97-111
        • LeCun Y.
        • Bengio Y.
        • Hinton G.
        Deep learning.
        Nature. 2015; 521: 436-444
        • Suzuki K.
        Pixel-Based Machine Learning in Medical Imaging.
        Int. J. Biomed. Imaging. 2012; 2012: 1-18
        • Rizzo S.
        • et al.
        Radiomics: the facts and the challenges of image analysis.
        Eur. Radiol. Exp. 2018; 2: 36
        • Zhang Y.
        • Oikonomou A.
        • Wong A.
        • Haider M.A.
        • Khalvati F.
        Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer.
        Sci. Rep. 2017; 7: 46349
      5. E. Huynh, et al., Associations of Radiomic Data Extracted from Static and Respiratory-Gated CT Scans with Disease Recurrence in Lung Cancer Patients Treated with SBRT, PLOS One 12 (2017) e0169172.

        • Wilkinson L.
        • Friendly M.
        The History of the Cluster Heat Map.
        Am. Stat. 2009; 63: 179-184
        • Hochberg Y.
        • Benjamini Y.
        More powerful procedures for multiple significance testing.
        Stat. Med. 1990; 9: 811-818
        • Lubner M.G.
        • Smith A.D.
        • Sandrasegaran K.
        • Sahani D.V.
        • Pickhardt P.J.
        CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges.
        RadioGraphics. 2017; 37: 1483-1503
        • O’Connor J.P.B.
        • et al.
        Imaging Intratumor Heterogeneity: Role in Therapy Response, Resistance, and Clinical Outcome.
        Clin. Cancer Res. 2015; 21: 249-257
        • Meacham C.E.
        • Morrison S.J.
        Tumour heterogeneity and cancer cell plasticity.
        Nature. 2013; 501: 328-337
        • Eskey C.J.
        • Koretsky A.P.
        • Domach M.M.
        • Jain R.K.
        2H-nuclear magnetic resonance imaging of tumor blood flow: spatial and temporal heterogeneity in a tissue-isolated mammary adenocarcinoma.
        Cancer Res. 1992; 52: 6010-6019
        • Shipitsin M.
        • Campbell L.L.
        • Argani P.
        • Weremowicz S.
        • Bloushtain-Qimron N.
        • Yao J.
        • Nikolskaya T.
        • Serebryiskaya T.
        • Beroukhim R.
        • Hu M.
        • Halushka M.K.
        • Sukumar S.
        • Parker L.M.
        • Anderson K.S.
        • Harris L.N.
        • Garber J.E.
        • Richardson A.L.
        • Schnitt S.J.
        • Nikolsky Y.
        • Gelman R.S.
        • Polyak K.
        Molecular Definition of Breast Tumor Heterogeneity.
        Cancer Cell. 2007; 11: 259-273
        • Junttila M.R.
        • de Sauvage F.J.
        Influence of tumour micro-environment heterogeneity on therapeutic response.
        Nature. 2013; 501: 346-354
        • Gatenby R.A.
        • Grove O.
        • Gillies R.J.
        Quantitative Imaging in Cancer Evolution and Ecology.
        Radiology. 2013; 269: 8-14
        • Simpson-Herren L.
        • Noker P.E.
        • Wagoner S.D.
        Variability of tumor response to chemotherapy II. Contribution of tumor heterogeneity.
        Cancer Chemother. Pharmacol. 1988; 22
        • Forghani R.
        • et al.
        Radiomics and Artificial Intelligence for Biomarker and Prediction Model Development in Oncology.
        Comput. Struct. Biotechnol. J. 2019; 17: 995-1008
        • Bai Y.
        • Lin Y.
        • Tian J.
        • Shi D.
        • Cheng J.
        • Haacke E.M.
        • Hong X.
        • Ma B.o.
        • Zhou J.
        • Wang M.
        Grading of Gliomas by Using Monoexponential, Biexponential, and Stretched Exponential Diffusion-weighted MR Imaging and Diffusion Kurtosis MR Imaging.
        Radiology. 2016; 278: 496-504
      6. H. Cho, S. Lee, J. Kim, H. Park, Classification of the glioma grading using radiomics analysis, PeerJ 6, (2018) e5982.

      7. H. Cho, H. Park, Classification of low-grade and high-grade glioma using multi-modal image radiomics features, in: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 3081–3084 (IEEE, 2017).

        • Sun P.
        • Wang D.
        • Mok V.C.
        • Shi L.
        Comparison of Feature Selection Methods and Machine Learning Classifiers for Radiomics Analysis in Glioma Grading.
        IEEE Access. 2019; 7: 102010-102020
        • Jiang Y.
        • et al.
        Histogram analysis in prostate cancer: a comparison of diffusion kurtosis imaging model versus monoexponential model.
        Acta Radiol. 2020; 61: 1431-1440
        • Brynolfsson P.
        • et al.
        ADC texture-An imaging biomarker for high-grade glioma?: ADC texture, an imaging biomarker for high-grade glioma?.
        Med. Phys. 2014; 41101903
      8. J. Li, et al., High-order radiomics features based on T2 FLAIR MRI predict multiple glioma immunohistochemical features: A more precise and personalized gliomas management, PLOS One 15 (2020) e0227703.

        • Kumar R.
        • Gupta A.
        • Arora H.S.
        • Pandian G.N.
        • Raman B.
        CGHF: A Computational Decision Support System for Glioma Classification Using Hybrid Radiomics- and Stationary Wavelet-Based Features.
        IEEE Access. 2020; 8: 79440-79458
        • Kickingereder P.
        • Burth S.
        • Wick A.
        • Götz M.
        • Eidel O.
        • Schlemmer H.-P.
        • Maier-Hein K.H.
        • Wick W.
        • Bendszus M.
        • Radbruch A.
        • Bonekamp D.
        Radiomic Profiling of Glioblastoma: Identifying an Imaging Predictor of Patient Survival with Improved Performance over Established Clinical and Radiologic Risk Models.
        Radiology. 2016; 280: 880-889
        • Wang F.
        • et al.
        Radiomic Nomogram Improves Preoperative T Category Accuracy in Locally Advanced Laryngeal Carcinoma.
        Front. Oncol. 2019; 9: 1064
        • Ren J.
        • et al.
        Magnetic resonance imaging based radiomics signature for the preoperative discrimination of stage I-II and III-IV head and neck squamous cell carcinoma.
        Eur. J. Radiol. 2018; 106: 1-6
        • Romeo V.
        • et al.
        Prediction of Tumor Grade and Nodal Status in Oropharyngeal and Oral Cavity Squamous-cell Carcinoma Using a Radiomic Approach.
        Anticancer Res. 2020; 40: 271-280
        • Wapnir I.L.
        • et al.
        Long-Term Outcomes of Invasive Ipsilateral Breast Tumor Recurrences After Lumpectomy in NSABP B-17 and B-24 Randomized Clinical Trials for DCIS.
        JNCI J. Natl. Cancer Inst. 2011; 103: 478-488
        • Lari S.A.
        • Kuerer H.M.
        Biological Markers in DCIS and Risk of Breast Recurrence: A Systematic Review.
        J. Cancer. 2011; 2: 232-261
        • Masud R.
        • Al-Rei M.
        • Lokker C.
        Computer-Aided Detection for Breast Cancer Screening in Clinical Settings: Scoping Review.
        JMIR Med. Inform. 2019; 7e12660
        • Lehman C.D.
        • et al.
        Diagnostic Accuracy of Digital Screening Mammography With and Without Computer-Aided Detection.
        JAMA Intern. Med. 2015; 175: 1828
        • McKinney S.M.
        • Sieniek M.
        • Godbole V.
        • Godwin J.
        • Antropova N.
        • Ashrafian H.
        • Back T.
        • Chesus M.
        • Corrado G.S.
        • Darzi A.
        • Etemadi M.
        • Garcia-Vicente F.
        • Gilbert F.J.
        • Halling-Brown M.
        • Hassabis D.
        • Jansen S.
        • Karthikesalingam A.
        • Kelly C.J.
        • King D.
        • Ledsam J.R.
        • Melnick D.
        • Mostofi H.
        • Peng L.
        • Reicher J.J.
        • Romera-Paredes B.
        • Sidebottom R.
        • Suleyman M.
        • Tse D.
        • Young K.C.
        • De Fauw J.
        • Shetty S.
        International evaluation of an AI system for breast cancer screening.
        Nature. 2020; 577: 89-94
      9. J. Hofmanninger, G. Langs, Mapping visual features to semantic profiles for retrieval in medical imaging, in: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 457–465 (IEEE, 2015).

        • Chae H.-D.
        • Park C.M.
        • Park S.J.
        • Lee S.M.
        • Kim K.G.
        • Goo J.M.
        Computerized texture analysis of persistent part-solid ground-glass nodules: differentiation of preinvasive lesions from invasive pulmonary adenocarcinomas.
        Radiology. 2014; 273: 285-293
        • Rusu M.
        • et al.
        Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study.
        Eur. Radiol. 2017; 27: 4209-4217
        • McNitt-Gray M.F.
        • et al.
        The Lung Image Database Consortium (LIDC) data collection process for nodule detection and annotation.
        Acad. Radiol. 2007; 14: 1464-1474
        • Firmino M.
        • Angelo G.
        • Morais H.
        • Dantas M.R.
        • Valentim R.
        Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy.
        Biomed. Eng. OnLine. 2016; 15: 2
        • Shen W.
        • Zhou M.
        • Yang F.
        • Yang C.
        • Tian J.
        Multi-scale Convolutional Neural Networks for Lung Nodule Classification.
        Inf. Process. Med. Imaging Proc. Conf. 2015; 24: 588-599
        • Sun T.
        • Zhang R.
        • Wang J.
        • Li X.
        • Guo X.
        • Gormley M.
        Computer-Aided Diagnosis for Early-Stage Lung Cancer Based on Longitudinal and Balanced Data.
        PLOS ONE. 2013; 8: e63559
        • Wu H.
        • et al.
        Combination of radiological and gray level co-occurrence matrix textural features used to distinguish solitary pulmonary nodules by computed tomography.
        J. Digit. Imaging. 2013; 26: 797-802
        • Raman S.P.
        • Chen Y.
        • Schroeder J.L.
        • Huang P.
        • Fishman E.K.
        CT texture analysis of renal masses: pilot study using random forest classification for prediction of pathology.
        Acad. Radiol. 2014; 21: 1587-1596
        • Hodgdon T.
        • et al.
        Can Quantitative CT Texture Analysis be Used to Differentiate Fat-poor Renal Angiomyolipoma from Renal Cell Carcinoma on Unenhanced CT Images?.
        Radiology. 2015; 276: 787-796
        • Takahashi N.
        • et al.
        CT negative attenuation pixel distribution and texture analysis for detection of fat in small angiomyolipoma on unenhanced CT.
        Abdom. Radiol. N. Y. 2016; 41: 1142-1151
        • Yan L.
        • et al.
        Angiomyolipoma with minimal fat: differentiation from clear cell renal cell carcinoma and papillary renal cell carcinoma by texture analysis on CT images.
        Acad. Radiol. 2015; 22: 1115-1121
        • Leng S.
        • et al.
        Subjective and objective heterogeneity scores for differentiating small renal masses using contrast-enhanced CT.
        Abdom. Radiol. N. Y. 2017; 42: 1485-1492
        • Raman S.P.
        • et al.
        Preliminary data using computed tomography texture analysis for the classification of hypervascular liver lesions: generation of a predictive model on the basis of quantitative spatial frequency measurements–a work in progress.
        J. Comput. Assist. Tomogr. 2015; 39: 383-395
        • Canellas R.
        • Mehrkhani F.
        • Patino M.
        • Kambadakone A.
        • Sahani D.
        Characterization of Portal Vein Thrombosis (Neoplastic Versus Bland) on CT Images Using Software-Based Texture Analysis and Thrombus Density (Hounsfield Units).
        AJR Am. J. Roentgenol. 2016; 207: W81-W87
        • Hanania A.N.
        • Bantis L.E.
        • Feng Z.
        • Wang H.
        • Tamm E.P.
        • Katz M.H.
        • Maitra A.
        • Koay E.J.
        Quantitative imaging to evaluate malignant potential of IPMNs.
        Oncotarget. 2016; 7: 85776-85784
        • Chang G.J.
        • Rodriguez-Bigas M.A.
        • Skibber J.M.
        • Moyer V.A.
        Lymph node evaluation and survival after curative resection of colon cancer: systematic review.
        J. Natl. Cancer Inst. 2007; 99: 433-441
        • Toiyama Y.
        • et al.
        Serum Angiopoietin-like Protein 2 Improves Preoperative Detection of Lymph Node Metastasis in Colorectal Cancer.
        Anticancer Res. 2015; 35: 2849-2856
        • Huang Y.-Q.
        • et al.
        Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer.
        J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2016; 34: 2157-2164
        • Van Cutsem E.
        • et al.
        Fluorouracil, leucovorin, and irinotecan plus cetuximab treatment and RAS mutations in colorectal cancer.
        J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2015; 33: 692-700
        • Douillard J.-Y.
        • et al.
        Panitumumab–FOLFOX4 treatment and RAS mutations in colorectal cancer.
        N. Engl. J. Med. 2013; 369: 1023-1034
        • Yang L.
        • et al.
        Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?.
        Eur. Radiol. 2018; 28: 2058-2067
        • Aerts H.J.W.L.
        • et al.
        Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach.
        Nat. Commun. 2014; 5: 4006
        • Parmar C.
        • et al.
        Radiomic feature clusters and prognostic signatures specific for Lung and Head & Neck cancer.
        Sci. Rep. 2015; 5: 11044
        • Kickingereder P.
        • et al.
        Large-scale Radiomic Profiling of Recurrent Glioblastoma Identifies an Imaging Predictor for Stratifying Anti-Angiogenic Treatment Response.
        Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2016; 22: 5765-5771
        • Tandel G.S.
        • Biswas M.
        • Kakde O.G.
        • Tiwari A.
        • Suri H.S.
        • Turk M.
        • Laird J.
        • Asare C.
        • Ankrah A.A.
        • Khanna N.N.
        • Madhusudhan B.K.
        • Saba L.
        • Suri J.S.
        A Review on a Deep Learning Perspective in Brain Cancer Classification.
        Cancers. 2019; 11: 111
        • Papp L.
        • et al.
        Glioma Survival Prediction with Combined Analysis of In Vivo 11C-MET PET Features, Ex Vivo Features, and Patient Features by Supervised Machine Learning.
        J. Nucl. Med. Off. Publ. Soc. Nucl. Med. 2018; 59: 892-899
        • Pérez-Beteta J.
        • Molina-García D.
        • Ortiz-Alhambra J.A.
        • Fernández-Romero A.
        • Luque B.
        • Arregui E.
        • Calvo M.
        • Borrás J.M.
        • Meléndez B.
        • Rodríguez de Lope Á.
        • Moreno de la Presa R.
        • Iglesias Bayo L.
        • Barcia J.A.
        • Martino J.
        • Velásquez C.
        • Asenjo B.
        • Benavides M.
        • Herruzo I.
        • Revert A.
        • Arana E.
        • Pérez-García V.M.
        Tumor Surface Regularity at MR Imaging Predicts Survival and Response to Surgery in Patients with Glioblastoma.
        Radiology. 2018; 288: 218-225
        • Yokota T.
        • et al.
        How Should We Approach Locally Advanced Squamous Cell Carcinoma of Head and Neck Cancer Patients Ineligible for Standard Non-surgical Treatment?.
        Curr. Oncol. Rep. 2020; 22: 118
        • Bologna M.
        • Calareso G.
        • Resteghini C.
        • Sdao S.
        • Montin E.
        • Corino V.
        • Mainardi L.
        • Licitra L.
        • Bossi P.
        Relevance of apparent diffusion coefficient features for a radiomics-based prediction of response to induction chemotherapy in sinonasal cancer.
        NMR Biomed. 2020;
        • Wang G.
        • et al.
        Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma.
        Eur. J. Radiol. 2018; 98: 100-106
        • Zhao L.
        • et al.
        MRI-based radiomics nomogram may predict the response to induction chemotherapy and survival in locally advanced nasopharyngeal carcinoma.
        Eur. Radiol. 2020; 30: 537-546
        • Jin X.
        • et al.
        Prediction of response after chemoradiation for esophageal cancer using a combination of dosimetry and CT radiomics.
        Eur. Radiol. 2019; 29: 6080-6088
      10. M.D. Anderson, Cancer Center Head and Neck Quantitative Imaging Working Group. Investigation of radiomic signatures for local recurrence using primary tumor texture analysis in oropharyngeal head and neck cancer patients, Sci. Rep. 8 (2018) 1524.

        • Grove Olya
        • Berglund Anders E.
        • Schabath Matthew B.
        • Aerts Hugo J.W.L.
        • Dekker Andre
        • Wang Hua
        • Velazquez Emmanuel Rios
        • Lambin Philippe
        • Gu Yuhua
        • Balagurunathan Yoganand
        • Eikman Edward
        • Gatenby Robert A.
        • Eschrich Steven
        • Gillies Robert J.
        • Muñoz-Barrutia Arrate
        Quantitative Computed Tomographic Descriptors Associate Tumor Shape Complexity and Intratumor Heterogeneity with Prognosis in Lung Adenocarcinoma.
        PLOS ONE. 2015; 10: e0118261
        • Ganeshan B.
        • Panayiotou E.
        • Burnand K.
        • Dizdarevic S.
        • Miles K.
        Tumour heterogeneity in non-small cell lung carcinoma assessed by CT texture analysis: a potential marker of survival.
        Eur. Radiol. 2012; 22: 796-802
        • Win T.
        • et al.
        Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with non-small cell lung cancer.
        Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2013; 19: 3591-3599
        • Song Jiangdian
        • Liu Zaiyi
        • Zhong Wenzhao
        • Huang Yanqi
        • Ma Zelan
        • Dong Di
        • Liang Changhong
        • Tian Jie
        Non-small cell lung cancer: quantitative phenotypic analysis of CT images as a potential marker of prognosis.
        Sci. Rep. 2016; 6
      11. Prognostic Value and Reproducibility of Pretreatment CT Texture Features in Stage III Non-Small Cell Lung Cancer - PubMed.

      12. Radiomics Signature: A Potential Biomarker for the Prediction of Disease-Free Survival in Early-Stage (I or II) Non—Small Cell Lung Cancer | Radiology.

      13. Noninvasive Risk Stratification of Lung Adenocarcinoma Using Quantitative Computed Tomography - PubMed.

      14. Predicting Adenocarcinoma Recurrence Using Computational Texture Models of Nodule Components in Lung CT - PubMed.

        • Noor N.M.
        • et al.
        Automatic Lung Segmentation Using Control Feedback System: Morphology and Texture Paradigm.
        J. Med. Syst. 2015; 39: 22
        • Saba L.
        • et al.
        Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients.
        J. Med. Syst. 2016; 40: 142
        • Zhou Y.
        • et al.
        CT-based radiomics signature: a potential biomarker for preoperative prediction of early recurrence in hepatocellular carcinoma.
        Abdom. Radiol. N. Y. 2017; 42: 1695-1704
        • Akai H.
        • et al.
        Predicting prognosis of resected hepatocellular carcinoma by radiomics analysis with random survival forest.
        Diagn. Interv. Imaging. 2018; 99: 643-651
        • Cozzi Luca
        • Dinapoli Nicola
        • Fogliata Antonella
        • Hsu Wei-Chung
        • Reggiori Giacomo
        • Lobefalo Francesca
        • Kirienko Margarita
        • Sollini Martina
        • Franceschini Davide
        • Comito Tiziana
        • Franzese Ciro
        • Scorsetti Marta
        • Wang Po-Ming
        Radiomics based analysis to predict local control and survival in hepatocellular carcinoma patients treated with volumetric modulated arc therapy.
        BMC Cancer. 2017; 17
        • Biswas M.
        • et al.
        Symtosis: A liver ultrasound tissue characterization and risk stratification in optimized deep learning paradigm.
        Comput. Methods Programs Biomed. 2018; 155: 165-177
        • Kuppili V.
        • et al.
        Extreme Learning Machine Framework for Risk Stratification of Fatty Liver Disease Using Ultrasound Tissue Characterization.
        J. Med. Syst. 2017; 41: 152
        • Lubner M.G.
        • Stabo N.
        • Abel E.J.
        • Del Rio A.M.
        • Pickhardt P.J.
        CT Textural Analysis of Large Primary Renal Cell Carcinomas: Pretreatment Tumor Heterogeneity Correlates With Histologic Findings and Clinical Outcomes.
        AJR Am. J. Roentgenol. 2016; 207: 96-105
        • Schieda N.
        • et al.
        Diagnosis of Sarcomatoid Renal Cell Carcinoma With CT: Evaluation by Qualitative Imaging Features and Texture Analysis.
        AJR Am. J. Roentgenol. 2015; 204: 1013-1023
        • van de Velde C.J.H.
        • et al.
        EURECCA colorectal: multidisciplinary management: European consensus conference colon & rectum.
        Eur. J. Cancer Oxf. Engl. 2014; 1990: 1.e1-1.e34
        • van Gijn W.
        • et al.
        Preoperative radiotherapy combined with total mesorectal excision for resectable rectal cancer: 12-year follow-up of the multicentre, randomised controlled TME trial.
        Lancet Oncol. 2011; 12: 575-582
        • Sanghera P.
        • Wong D.W.Y.
        • McConkey C.C.
        • Geh J.I.
        • Hartley A.
        Chemoradiotherapy for rectal cancer: an updated analysis of factors affecting pathological response.
        Clin. Oncol. R. Coll. Radiol. G. B. 2008; 20: 176-183
        • Maas Monique
        • Beets-Tan Regina G.H.
        • Lambregts Doenja M.J.
        • Lammering Guido
        • Nelemans Patty J.
        • Engelen Sanne M.E.
        • van Dam Ronald M.
        • Jansen Rob L.H.
        • Sosef Meindert
        • Leijtens Jeroen W.A.
        • Hulsewé Karel W.E.
        • Buijsen Jeroen
        • Beets Geerard L.
        Wait-and-see policy for clinical complete responders after chemoradiation for rectal cancer.
        J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2011; 29: 4633-4640
        • Liu Zhenyu
        • Zhang Xiao-Yan
        • Shi Yan-Jie
        • Wang Lin
        • Zhu Hai-Tao
        • Tang Zhenchao
        • Wang Shuo
        • Li Xiao-Ting
        • Tian Jie
        • Sun Ying-Shi
        Radiomics Analysis for Evaluation of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.
        Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 2017; 23: 7253-7262
      15. MR Imaging of Rectal Cancer: Radiomics Analysis to Assess Treatment Response After Neoadjuvant Therapy - PubMed.

        • Sauer R.
        • et al.
        Preoperative versus postoperative chemoradiotherapy for locally advanced rectal cancer: results of the German CAO/ARO/AIO-94 randomized phase III trial after a median follow-up of 11 years.
        J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 2012; 30: 1926-1933
        • Rödel C.
        • et al.
        Preoperative chemoradiotherapy and postoperative chemotherapy with fluorouracil and oxaliplatin versus fluorouracil alone in locally advanced rectal cancer: initial results of the German CAO/ARO/AIO-04 randomised phase 3 trial.
        Lancet Oncol. 2012; 13: 679-687
        • Bosset J.F.
        • et al.
        Preoperative chemoradiotherapy versus preoperative radiotherapy in rectal cancer patients: assessment of acute toxicity and treatment compliance. Report of the 22921 randomised trial conducted by the EORTC Radiotherapy Group.
        Eur. J. Cancer Oxf. Engl. 2004; 1990: 219-224
        • Meng Yankai
        • Zhang Yuchen
        • Dong Di
        • Li Chunming
        • Liang Xiao
        • Zhang Chongda
        • Wan Lijuan
        • Zhao Xinming
        • Xu Kai
        • Zhou Chunwu
        • Tian Jie
        • Zhang Hongmei
        Novel radiomic signature as a prognostic biomarker for locally advanced rectal cancer.
        J. Magn. Reson. Imaging JMRI. 2018; 48: 605-614
        • Baessler Bettina
        • Mannil Manoj
        • Oebel Sabrina
        • Maintz David
        • Alkadhi Hatem
        • Manka Robert
        Subacute and Chronic Left Ventricular Myocardial Scar: Accuracy of Texture Analysis on Nonenhanced Cine MR Images.
        Radiology. 2018; 286: 103-112
        • Baeßler B.
        • Mannil M.
        • Maintz D.
        • Alkadhi H.
        • Manka R.
        Texture analysis and machine learning of non-contrast T1-weighted MR images in patients with hypertrophic cardiomyopathy—Preliminary results.
        Eur. J. Radiol. 2018; 102: 61-67
        • Larroza A.
        • et al.
        Differentiation between acute and chronic myocardial infarction by means of texture analysis of late gadolinium enhancement and cine cardiac magnetic resonance imaging.
        Eur. J. Radiol. 2017; 92: 78-83
        • Antunes S.
        • et al.
        Characterization of normal and scarred myocardium based on texture analysis of cardiac computed tomography images.
        Conf. Proc. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. Annu. Conf. 2016; 2016: 4161-4164
        • Hinzpeter Ricarda
        • Wagner Matthias W.
        • Wurnig Moritz C.
        • Seifert Burkhardt
        • Manka Robert
        • Alkadhi Hatem
        • Zaragoza Carlos
        Texture analysis of acute myocardial infarction with CT: First experience study.
        PLoS ONE. 2017; 12: e0186876
        • Mannil M.
        • von Spiczak J.
        • Manka R.
        • Alkadhi H.
        Texture Analysis and Machine Learning for Detecting Myocardial Infarction in Noncontrast Low-Dose Computed Tomography: Unveiling the Invisible.
        Invest. Radiol. 2018; 53: 338-343
        • Mannil M.
        • et al.
        Texture analysis of myocardial infarction in CT: Comparison with visual analysis and impact of iterative reconstruction.
        Eur. J. Radiol. 2019; 113: 245-250
      16. U. Neisius, et al., Texture signatures of native myocardial T1 as novel imaging markers for identification of hypertrophic cardiomyopathy patients without scar, J. Magn. Reson. Imaging n/a.

        • Schofield R.
        • et al.
        Texture analysis of cardiovascular magnetic resonance cine images differentiates aetiologies of left ventricular hypertrophy.
        Clin. Radiol. 2019; 74: 140-149
        • Araki T.
        • et al.
        Stroke Risk Stratification and its Validation using Ultrasonic Echolucent Carotid Wall Plaque Morphology: A Machine Learning Paradigm.
        Comput. Biol. Med. 2017; 80: 77-96
        • Acharya R.U.
        • et al.
        Symptomatic vs. Asymptomatic Plaque Classification in Carotid Ultrasound.
        J. Med. Syst. 2012; 36: 1861-1871
        • Acharya U.R.
        • et al.
        Understanding symptomatology of atherosclerotic plaque by image-based tissue characterization.
        Comput. Methods Programs Biomed. 2013; 110: 66-75
        • Kotze Carl W.
        • Rudd James H.F.
        • Ganeshan Balaji
        • Menezes Leon J.
        • Brookes Jocelyn
        • Agu Obiekezie
        • Yusuf Syed W.
        • Groves Ashley M.
        CT signal heterogeneity of abdominal aortic aneurysm as a possible predictive biomarker for expansion.
        Atherosclerosis. 2014; 233: 510-517
        • Liu Hui
        • Shao Ying
        • Guo Dongmei
        • Zheng Yuanjie
        • Zhao Zuowei
        • Qiu Tianshuang
        Cirrhosis Classification Based on Texture Classification of Random Features.
        Comput. Math. Methods Med. 2014; 2014: 1-8
        • Park H.J.
        • et al.
        Texture-Based Automated Quantitative Assessment of Regional Patterns on Initial CT in Patients With Idiopathic Pulmonary Fibrosis: Relationship to Decline in Forced Vital Capacity.
        AJR Am. J. Roentgenol. 2016; 207: 976-983
        • Lubner M.G.
        • Malecki K.
        • Kloke J.
        • Ganeshan B.
        • Pickhardt P.J.
        Texture analysis of the liver at MDCT for assessing hepatic fibrosis.
        Abdom. Radiol. N. Y. 2017; 42: 2069-2078
        • Daginawala N.
        • et al.
        Using texture analyses of contrast enhanced CT to assess hepatic fibrosis.
        Eur. J. Radiol. 2016; 85: 511-517
        • Tak Kayeong
        • Lee Subin
        • Choi Euna
        • Suh Seung Wan
        • Oh Dae Jong
        • Moon Woori
        • Kim Hye Sung
        • Byun Seonjeong
        • Bae Jong Bin
        • Han Ji Won
        • Kim Jae Hyoung
        • Kim Ki Woong
        Magnetic Resonance Imaging Texture of Medial Pulvinar in Dementia with Lewy Bodies.
        Dement. Geriatr. Cogn. Disord. 2020; 49: 8-15
        • Harrison L.C.V.
        • et al.
        MRI texture analysis in multiple sclerosis: toward a clinical analysis protocol.
        Acad. Radiol. 2010; 17: 696-707
        • Mathias J.M.
        • Tofts P.S.
        • Losseff N.A.
        Texture analysis of spinal cord pathology in multiple sclerosis.
        Magn. Reson. Med. 1999; 42: 929-935
        • Yu O.
        • Mauss Y.
        • Zollner G.
        • Namer I.J.
        • Chambron J.
        Distinct patterns of active and non-active plaques using texture analysis on brain NMR images in multiple sclerosis patients: preliminary results.
        Magn. Reson. Imaging. 1999; 17: 1261-1267
        • Caruana Giovanni
        • Pessini Lucas M.
        • Cannella Roberto
        • Salvaggio Giuseppe
        • de Barros Andréa
        • Salerno Annalaura
        • Auger Cristina
        • Rovira Àlex
        Texture analysis in susceptibility-weighted imaging may be useful to differentiate acute from chronic multiple sclerosis lesions.
        Eur. Radiol. 2020; 30: 6348-6356
        • Zhang Y.
        • et al.
        Pathological correlates of magnetic resonance imaging texture heterogeneity in multiple sclerosis.
        Ann. Neurol. 2013; 74: 91-99
        • Tabari Azadeh
        • Torriani Martin
        • Miller Karen K.
        • Klibanski Anne
        • Kalra Mannudeep K.
        • Bredella Miriam A.
        Anorexia Nervosa: Analysis of Trabecular Texture with CT.
        Radiology. 2017; 283: 178-185
        • Limkin E.J.
        • et al.
        Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology.
        Ann. Oncol. 2017; 28: 1191-1206
      17. O’Connor, J.P.B. et al., Imaging biomarker roadmap for cancer studies, Nat. Rev. Clin. Oncol. 14 (2017) 169–186.