Promises and challenges for the implementation of computational medical imaging (radiomics) in oncology

Annals of Oncology

Volumn 28, Issue 6, 2017, Pages 1191-1206

  Limkin, E J ^a,b   Sun, R ^a,b,c   Dercle, L ^b   Zacharaki, E I ^c   Robert, C ^a,b,c   Reuze S ^a,b,c   Schernberg, A ^a,b,c   Paragios, N ^c,d   Deutsch, E ^a,b   Ferte C ^a,b  

^a Institut Gustave Roussy   (France)

^b INSTITUT GUSTAVE ROUSSY   (France)

^c UNIVERSITÉ PARIS SACLAY   (France)

^d TheraPanacea   (France)

Author keywords

Computational medical imaging; Precision medicine; Quantitative imaging; Radiomics; Tumor biology

Indexed keywords

TUMOR MARKER;

BREAST CANCER; CANCER DIAGNOSIS; CANCER EPIDEMIOLOGY; CANCER PROGNOSIS; CANCER STAGING; CLINICAL PRACTICE; COLORECTAL CANCER; COMPUTER ASSISTED TOMOGRAPHY; DIAGNOSTIC IMAGING; FEATURE EXTRACTION; GLIOBLASTOMA; HUMAN; IMAGE ACQUISITION; IMAGE ANALYSIS; IMAGE SEGMENTATION; IMAGING AND DISPLAY; KIDNEY CARCINOMA; LUNG ADENOCARCINOMA; MACHINE LEARNING; NON SMALL CELL LUNG CANCER; NONHUMAN; NUCLEAR MAGNETIC RESONANCE IMAGING; ONCOLOGY; PERSONALIZED MEDICINE; PREDICTION; PRIORITY JOURNAL; RADIOMICS; RECTUM CANCER; REVIEW; SOFT TISSUE SARCOMA; STANDARDIZATION; STATISTICAL LEARNING; THREE DIMENSIONAL RENDERING; TREATMENT RESPONSE; IMAGE PROCESSING; NEOPLASM; PROCEDURES;

DIAGNOSTIC IMAGING; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; MEDICAL ONCOLOGY; NEOPLASMS; PRECISION MEDICINE;

EID: 85021117855     PISSN: 09237534     EISSN: 15698041     Source Type: Journal    
DOI: 10.1093/annonc/mdx034     Document Type: Review

References (121)

1. Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology 2016; 278(2): 563-577.
2. Kumar V, Gu Y, Basu S, et al. QIN "Radiomics: The Process and the Challenges." Magn Reson Imaging 2012; 30(9): 1234-1248.
3. Lambin P, Rios-Velazquez E, Leijenaar R, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48(4): 441-446.
4. Aerts HJWL, Velazquez ER, Leijenaar RTH, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; doi:10.1038/ncomms5006.
5. Heppner GH. Tumor heterogeneity. Cancer Res 1984; 44(6): 2259-2265.
6. Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature 2013; 501(7467): 346-354.
7. O'Connor JPB, Rose CJ, Waterton JC, et al. Imaging intratumor heterogeneity: role in therapy response, resistance, and clinical outcome. Clin Cancer Res 2015; 21(2): 249-257.
8. Bushberg JT, Boone JM. The Essential Physics of Medical Imaging. Philadelphia: Lippincott Williams & Wilkins 2011.
9. Galavis PE, Hollensen C, Jallow N, et al. Variability of textural features in FDG PET images due to different acquisition modes and reconstruction parameters. Acta Oncol 2010; 49(7): 1012-1016.
10. Zhao B, Tan Y, Tsai W-Y, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep 2016; 6: 23428.
11. Balagurunathan Y, Kumar V, Gu Y, et al. Test-retest reproducibility analysis of lung CT image features. J Digit Imaging 2014; 27(6): 805-823.
12. Parmar C, Rios Velazquez E, Leijenaar R, et al. Robust radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 2014; doi:10.1371/journal.pone.0102107.
13. Rios Velazquez E, Aerts HJWL, Gu Y, et al. A semiautomatic CT-based ensemble segmentation of lung tumors: comparison with oncologists' delineations and with the surgical specimen. Radiother Oncol 2012; 105(2): 167-173.
14. Velazquez ER, Parmar C, Jermoumi M, et al. Volumetric CT-based segmentation of NSCLC using 3D-Slicer. Sci Rep 2013; doi:10.1038/srep03529.
15. Gorthi S, Bach Cuadra M, Thiran J-P. Exporting contours to DICOMRT structure set. Insight J 2009.
16. FormatAnalyze-MRC CBU Imaging Wiki. http://imaging.mrc-cbu. cam.ac.uk/imaging/FormatAnalyze (7 February 2017, date last accessed).
17. NIfTI-1 Data Format - Neuroimaging Informatics Technology Initiative. http://nifti.nimh.nih.gov/nifti-1 (7 February 2017, date last accessed).
18. Wells WM. Efficient synthesis of Gaussian filters by cascaded uniform filters. IEEE Trans Pattern Anal Mach Intell 1986; PAMI-8(2): 234-239.
19. Leijenaar RTH, Nalbantov G, Carvalho S, et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep 2015; doi:10.1038/srep11075.
20. Orlhac F, Soussan M, Chouahnia K, et al. 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer. PLoS ONE 2015; 10(12): e0145063.
21. Brooks FJ, Grigsby PW. The effect of small tumor volumes on studies of intratumoral heterogeneity of tracer uptake. J Nucl Med. 2014; 55(1): 37-42.
22. Orlhac F, Soussan M, Maisonobe J-A, et al. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. Sci Rep 2014; 55(3): 414-422.
23. van Velden FHP, Kramer GM, Frings V, et al. Repeatability of radiomic features in non-small-cell lung cancer [(18)F]FDG-PET/CT studies: impact of reconstruction and delineation. Mol Imaging Biol 2016; doi:10.1007/s11307-016-0940-2.
24. El Naqa I, Grigsby PW, Apte A, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recognit 2009; 42(6): 1162-1171.
25. Haralick RM, Shanmugam K, Dinstein I. Textural features for image classification. IEEE Trans Syst Man Cybern 1973; SMC3(6): 610-621.
26. Galloway MM. Texture analysis using gray level run lengths. Comput Graph Image Process 1975; 4(2): 172-179.
27. Thibault G, Angulo J, Meyer F. Advanced statistical matrices for texture characterization: application to cell classification. IEEE Trans Biomed Eng. 2014; 61(3): 630-637.
28. Sun C, Wee WG. Neighboring gray level dependence matrix for texture classification. Comput Vis Graph Image Process 1983; 23(3): 341-352.
29. Narang S, Lehrer M, Yang D, et al. Radiomics in glioblastoma: current status, challenges and potential opportunities. Transl Cancer Res 2016; 5(4): 383-397.
30. Zheng Y, Englander S, Baloch S, et al. STEP: Spatiotemporal enhancement pattern for MR-based breast tumor diagnosis. Med Phys 2009; 36(7): 3192-3204.
31. Wang JJ-Y, Bensmail H, Gao X. Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification. Pattern Recognit 2013; 46(12): 3249-3255.
32. Cataldo SD, Ficarra E. Mining textural knowledge in biological images: applications, methods and trends. Comput Struct Biotechnol J 2016; doi:10.1016/j.csbj.2016.11.002.
33. Armato SG, Meyer CR, Mcnitt-Gray MF, et al. The reference image database to evaluate response to therapy in lung cancer (RIDER) project: a resource for the development of change-analysis software. Clin Pharmacol Ther 2008; 84(4): 448-456.
34. Clarke R, Ressom HW, Wang A, et al. The properties of highdimensional data spaces: implications for exploring gene and protein expression data. Nat Rev Cancer 2008; 8(1): 37-49.
35. Ferte C, Trister AD, Huang E, et al. Impact of bioinformatic procedures in the development and translation of high-throughput molecular classifiers in oncology. Clin Cancer Res 2013; 19(16): 4315-4325.
36. Holm S. A simple sequentially rejective multiple test procedure. Scand J Stat 1979; 6(2): 65-70.
37. Hochberg Y, Benjamini Y. More powerful procedures for multiple significance testing. Statist Med 1990; 9(7): 811-818.
38. Kanas VG, Zacharaki EI, Thomas GA, et al. Learning MRI-based classification models for MGMT methylation status prediction in glioblastoma. Comput Methods Programs Biomed 2017; 140: 249-257.
39. Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep 2015. doi:10.1038/srep13087.
40. Paragios N, Duncan J, Ayache N. Handbook of Biomedical Imaging: Methodologies and Clinical Research, Springer-Verlag New York, Inc. 2015.
41. Kumar D, Shafiee MJ, Chung AG, et al. Discovery radiomics for computed tomography cancer detection. 2015; arXiv:1509.00117.
42. Hawkins S, Wang H, Liu Y, et al. Predicting malignant nodules from screening CT scans. J Thoracic Oncol 2016; doi:10.1016/j.jtho.2016.07.002.
43. Shen W, Zhou M, Yang F, et al. Multi-scale convolutional neural networks for lung nodule classification. In S Ourselin, DC Alexander, C-F Westin, MJ Cardoso (eds): Information Processing in Medical Imaging. Cham: Springer International Publishing, 2015; 9123: 588-599.
44. Zacharaki EI, Morita N, Bhatt P, et al. Survival analysis of patients with high-grade gliomas based on data mining of imaging variables. AJNR Am J Neuroradiol. 2012; 33(6): 1065.
45. Zacharaki EI, Kanas VG, Davatzikos C. Investigating machine learning techniques for MRI-based classification of brain neoplasms. Int J Comput Assist Radiol Surg. 2011; 6(6): 821-828.
46. Liu Y, Balagurunathan Y, Atwater T, et al. Radiological image traits predictive of cancer status in pulmonary nodules. Clin Cancer Res 2016; clincanres.3102.2016.
47. Zacharaki EI, Wang S, Chawla S, et al. Classification of brain tumor type and grade using MRI texture and shape in a machine learning scheme. Magn Reson Med 2009; 62(6): 1609-1618.
48. Wang J, Liu X, Dong D, et al. Prediction of malignant and benign of lung tumor using a quantitative radiomic method. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2016: 1272-1275.
49. Nie K, Shi L, Chen Q, et al. Rectal cancer: assessment of neoadjuvant chemo-radiation outcome based on radiomics of multi-parametric MRI. Clin Cancer Res 2016; doi:10.1158/1078-0432.CCR-15-2997.
50. Zhang Q, Xiao Y, Dai W, et al. Deep learning based classification of breast tumors with shear-wave elastography. Ultrasonics 2016; 72: 150-157.
51. Setio AAA, Ciompi F, Litjens G, et al. Pulmonary nodule detection in CT images: false positive reduction using multi-view convolutional networks. IEEE Trans Med Imaging 2016; 35(5): 1160-1169.
52. Carneiro G, Oakden-Rayner L, Bradley AP, et al. Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography. 2016.
53. Cheng J-Z, Ni D, Chou Y-H, et al. Computer-aided diagnosis with deep learning architecture: applications to breast lesions in US images and pulmonary nodules in CT scans. Sci Rep 2016; 6: 24454.
54. Kallenberg M, Petersen K, Nielsen M, et al. Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans Med Imaging 2016; 35(5): 1322-1331.
55. Calhoun PS, Kuszyk BS, Heath DG, et al. Three-dimensional volume rendering of spiral CT data: theory and method 1. Radiographics 1999; 19(3): 745-764.
56. Sun R, Orlhac F, Robert C, et al. In Regard to Mattonen. Int J Radiat Oncol Biol Phys. 2016; 95(5): 1544-1545.
57. Bentzen SM. Theragnostic imaging for radiation oncology: dosepainting by numbers. Lancet Oncol 2005; 6(2): 112-117.
58. Bentzen SM, Gregoire V. Molecular-imaging-based dose painting-a novel paradigm for radiation therapy prescription. Semin Radiat Oncol 2011; 21(2): 101-110.
59. IMV CT Benchmark Report. IMV Medical Information Division, Inc. 2015.
60. Eskey CJ, Koretsky AP, Domach MM. Jain RK. 2H-nuclear magnetic resonance imaging of tumor blood flow: spatial and temporal heterogeneity in a tissue-isolated mammary adenocarcinoma. Cancer Res 1992; 52(21): 6010-6019.
61. Grove O, Berglund AE, Schabath MB, et al. Quantitative computed tomographic descriptors associate tumor shape complexity and intratumor heterogeneity with prognosis in lung adenocarcinoma. PLoS ONE 2015; 10(3): e0118261.
62. Li H, Zhu Y, Burnside ES, et al. Quantitative MRI radiomics in the prediction of molecular classifications of breast cancer subtypes in the TCGA/TCIA data set. Npj Breast Cancer 2016; 2: 16012.
63. Ganeshan B, Goh V, Mandeville HC, et al. Non-small cell lung cancer: histopathologic correlates for texture parameters at CT. Radiology 2013; 266(1): 326-336.
64. Diehn M, Nardini C, Wang DS, et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc Natl Acad Sci 2008; 105(13): 5213-5218.
65. Mazurowski MA, Zhang J, Grimm LJ, et al. Radiogenomic analysis of breast cancer: luminal B molecular subtype is associated with enhancement dynamics at MR imaging. Radiology 2014; 273(2): 365-372.
66. Gevaert O, Xu J, Hoang CD, et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data-methods and preliminary results. Radiology 2012; 264(2): 387-396.
67. Jamshidi N, Diehn M, Bredel M, Kuo MD. Illuminating radiogenomic characteristics of glioblastoma multiforme through integration of MR imaging, messenger RNA expression, and DNA copy number variation. Radiology 2014; 270(1): 1-2.
68. Hayano K, Tian F, Kambadakone AR, et al. Texture analysis of noncontrast- enhanced computed tomography for assessing angiogenesis and survival of soft tissue sarcoma. J Comput Assist Tomogr 2015; 39(4): 607-612.
69. Zhu H, Chen H, Lin Z, et al. Identifying molecular genetic features and oncogenic pathways of clear cell renal cell carcinoma through the anatomical (PADUA) scoring system. Oncotarget 2016; 7(9): 10006-10014.
70. Hanahan D, Weinberg RA. The hallmarks of cancer. Cell 2000; 100(1): 57-70.
71. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell 2011; 144(5): 646-674.
72. Ganeshan B, Abaleke S, Young RCD, et al. Texture analysis of non-small cell lung cancer on unenhanced computed tomography: initial evidence for a relationship with tumour glucose metabolism and stage. Cancer Imaging 2010; 10(1): 137-143.
73. Panth KM, Leijenaar RTH, Carvalho S, et al. Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells. Radiother Oncol 2015; 116(3): 462-466.
74. Zhu Y, Li H, Guo W, et al. Deciphering genomic underpinnings of quantitative MRI-based radiomic phenotypes of invasive breast carcinoma. Sci Rep 2015; 5: 17787.
75. Zinn PO, Mahajan B, Majadan B, et al. Radiogenomic mapping of edema/cellular invasion MRI-phenotypes in glioblastoma multiforme. PLoS ONE 2011; 6(10): e25451.
76. Basavanhally AN, Ganesan S, Agner S, et al. Computerized image-based detection and grading of lymphocytic infiltration in HER2+ breast cancer histopathology. IEEE Trans Biomed Eng 2010; 57(3): 642-653.
77. Segal E, Sirlin CB, Ooi C, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotechnol 2007; 25(6): 675-680.
78. Toward Precision Medicine: Building a Knowledge Network for Biomedical Research and a New Taxonomy of Disease, Washington, DC: National Academies Press, 2011.
79. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med 2011; 365(5): 395-409.
80. Zinovev D, Feigenbaum J, Furst J, Raicu D. Probabilistic lung nodule classification with belief decision trees. Conf Proc IEEE Eng Med Biol Soc 2011; 4493-4498.
81. Wu W, Parmar C, Grossmann P, et al. Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol 2016; doi:10.3389/fonc.2016.00071.
82. Maldonado F, Boland JM, Raghunath S, et al. Noninvasive characterization of the histopathologic features of pulmonary nodules of the lung adenocarcinoma spectrum using computer-aided nodule assessment and risk yield (CANARY)-a Pilot study. J Thorac Oncol 2013; 8(4): 452-460.
83. Ganeshan B, Miles KA, Young RCD, Chatwin CR. Texture analysis in non-contrast enhanced CT: impact of malignancy on texture in apparently disease-free areas of the liver. Eur J Radiol 2009; 70(1): 101-110.
84. Coroller TP, Grossmann P, Hou Y, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol 2015; 114(3): 345-350.
85. Huang Y, Liang C, He L, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol 2016; doi:10.1200/JCO.2015.65.9128.
86. Liang C, Huan Y, He L, et al. The development and validation of a CTbased radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget 2014; doi:10.18632/oncotarget.8919.
87. Parmar C, Leijenaar RTH, Grossmann P, et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep 2015; 5: 11044.
88. Leijenaar RTH, Carvalho S, Hoebers FJP, et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol 2015; 54(9): 1423-1429.
89. Lee J, Narang S, Martinez J, et al. Spatial habitat features derived from multiparametric magnetic resonance imaging data are associated with molecular subtype and 12-month survival status in glioblastoma multiforme. PLoS ONE 2015; 10(9): e0136557.
90. Kickingereder P, Burth S, Wick A, et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology.2016; 280(3): 880-889.
91. Liu Y, Kim J, Qu F, et al. CT features associated with epidermal growth factor receptor mutation status in patients with lung adenocarcinoma. Radiology 2016; 280(1): 271-280. doi: 10.1148/radiol.2016151455.
92. Aerts HJWL, Grossmann P, Tan Y, et al. Defining a radiomic response phenotype: a Pilot study using targeted therapy in NSCLC. Sci Rep 2016; doi:10.1038/srep33860.
93. Michoux N, Van den Broeck S, Lacoste L, et al. Texture analysis on MR images helps predicting non-response to NAC in breast cancer. BMC Cancer 2015; 15: 574.
94. Ahmed A, Gibbs P, Pickles M, Turnbull L. Texture analysis in assessment and prediction of chemotherapy response in breast cancer. J Magn Reson Imaging 2013; 38(1): 89-101.
95. Cook GJR, O'BrienME, SiddiqueMet al. Non-small cell lung cancer treated with erlotinib: heterogeneity of (18)F-FDG uptake at PET-association with treatment response and prognosis. Radiology 2015; 276(3): 883-893.
96. Goh V, Ganeshan B, Nathan P, et al. Assessment of response to tyrosine kinase inhibitors in metastatic renal cell cancer: CT texture as a predictive biomarker. Radiology.2011; 261(1): 165-171.
97. Bundschuh RA, Dinges J, Neumann L, et al. Textural parameters of tumor heterogeneity in 18F-FDG PET/CT for therapy response assessment and prognosis in patients with locally advanced rectal cancer. Sci Rep 2014; 55(6): 891-897.
98. Chiou VL, Burotto M. Pseudoprogression and immune-related response in solid tumors. J Clin Oncol. 2015; 33(31): 3541-3543.
99. Tumeh PC, Harview CL, Yearley JH, et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature 2014; 515(7528): 568-571.
100. Ferté C. Dynamic quantitative imaging approaches to identify pseudoprogression in cancer patients treated by immune checkpoints blockers. Sci RepTherapeutics 2015.
101. Infante M, Lutman RF, Imparato S, et al. Differential diagnosis and management of focal ground-glass opacities. Eur Respir J 2009; 33(4): 821-827.
102. Mattonen SA, Tetar S, Palma DA, et al. Automated texture analysis for prediction of recurrence after stereotactic ablative radiation therapy for lung cancer. Int J Radiat Oncol Biol Phys 2015; 93(3): S5-S6.
103. Antunes J, Viswanath S, Rusu M, et al. Radiomics analysis on FLTPET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Transl Oncol 2016; 9(2): 155-162.
104. Drucker E, Krapfenbauer K. Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine. EPMA J 2013; 4(1): 7.
105. Wagner JA, Prince M, Wright EC, et al. The Biomarkers Consortium: practice and Pitfalls of open-source precompetitive collaboration. Clin Pharmacol Ther 2010; 87(5): 539-542.
106. Altman DG, McShane LM, Sauerbrei W, Taube SE. Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. BMC Med 2012; 10: 51.
107. Brazma A, Hingamp P, Quackenbush J, et al. Minimum information about a microarray experiment (MIAME)-toward standards for microarray data. Nat Genet 2001; 29(4): 365-371.
108. O'Connor JPB, Aboagye EO, Adams JE, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol 2016. doi:10.1038/nrclinonc.2016.162.
109. https://www.deepmind.com/health (7 February 2017, date last accessed).
110. Grand-challenges-all challenges. https://grand-challenge.org/All_ Challenges/(7 February 2017, date last accessed).
111. LUNA-Home. https://luna.grand-challenge.org/(7 February 2017, date last accessed).
112. The Digital Mammography DREAM Challenge-Sage Bionetworks. https://www.synapse.org/#!Synapse:syn4224222/wiki/401743 (7 February 2017, date last accessed).
113. Mackin D, Fave X, Zhang L, et al. Measuring computed tomography scanner variability of radiomics features. Invest Radiol 2015; 50(11): 757-765.
114. Kim H, Park CM, Lee M et al. Impact of reconstruction algorithms on CT radiomic features of pulmonary tumors: analysis of intra- and inter-reader variability and inter-reconstruction algorithm variability. PLoS ONE 2016; 11(10): e0164924.
115. Clark K, Vendt B, Smith K et al. The cancer imaging archive (TCIA): maintaining and operating a public information repository. J Digit Imaging 2013; 26(6): 1045-1057.
116. Home - GEO - NCBI. http://www.ncbi.nlm.nih.gov/geo/(7 February 2017, date last accessed).
117. Clarke LP, Nordstrom RJ, Zhang H et al. The quantitative imaging network: NCI's historical perspective and planned goals. Transl Oncol.2014; 7(1): 1-4.
118. Sullivan, DC. Standards for Quantitative Imaging Biomarkers to Advance Research and Outcomes as part of the Cancer Moonshot. https://medium.com/cancer-moonshot/standards-for-quantitative-imaging-biomarkers-to-advance-research-and-outcomes-in-the-cancer-6e4e4ebf4e75#.9wfwnojfn (7 February 2017, date last accessed).
119. euroCAT.info: home. http://www.eurocat.info/(7 February 2017, date last accessed).
120. Fehr D, Veeraraghavan H, Wibmer A et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci USA. 2015; 112(46): E6265-E6273.
121. Li H, Zhu Y, Burnside ES et al. MR imaging radiomics signatures for predicting the risk of breast cancer recurrence as given by research versions of MammaPrint, Oncotype DX, and PAM50 Gene Assays. Radiology 2016; 152110.