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  • Review Article
  • Published:

Radiomics: the bridge between medical imaging and personalized medicine

Key Points

  • Radiomics is becoming increasingly more important in medical imaging

  • The explosion of medical imaging data creates an environment ideal for machine-learning and data-based science

  • Radiomics-based decision-support systems for precision diagnosis and treatment can be a powerful tool in modern medicine

  • Large-scale data sharing is necessary for the validation and full potential that radiomics represents

  • Standardized data collection, evaluation criteria, and reporting guidelines are required for radiomics to mature as a discipline

Abstract

Radiomics, the high-throughput mining of quantitative image features from standard-of-care medical imaging that enables data to be extracted and applied within clinical-decision support systems to improve diagnostic, prognostic, and predictive accuracy, is gaining importance in cancer research. Radiomic analysis exploits sophisticated image analysis tools and the rapid development and validation of medical imaging data that uses image-based signatures for precision diagnosis and treatment, providing a powerful tool in modern medicine. Herein, we describe the process of radiomics, its pitfalls, challenges, opportunities, and its capacity to improve clinical decision making, emphasizing the utility for patients with cancer. Currently, the field of radiomics lacks standardized evaluation of both the scientific integrity and the clinical relevance of the numerous published radiomics investigations resulting from the rapid growth of this area. Rigorous evaluation criteria and reporting guidelines need to be established in order for radiomics to mature as a discipline. Herein, we provide guidance for investigations to meet this urgent need in the field of radiomics.

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Figure 1: Flowchart depicting the workflow of radiomics and the application of the RQS.
Figure 2: Radiomics in cardiology.
Figure 3: Radiomics digital phantom data.
Figure 4: Radiogenomics analysis can reveal relationships between imaging phenotypes and gene-expression patterns.
Figure 5: Schematic overview of a clinical decision-support system graphical user interface illustrating the concept of delta-radiomics.
Figure 6: Schematic diagram of the CAT system.
Figure 7: Overview of the methodological processes for RLHC and how the radiomics workflow fits into the development of a CDSS.

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References

  1. Aerts, H. et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat. Commun. 5, 4006 (2014).

    CAS  PubMed  Google Scholar 

  2. Hood, L. & Friend, S. H. Predictive, personalized, preventive, participatory (P4) cancer medicine. Nat. Rev. Clin. Oncol. 8, 184–187 (2011).

    PubMed  Google Scholar 

  3. Lambin, P. et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur. J. Cancer 48, 441–446 (2012).

    PubMed  PubMed Central  Google Scholar 

  4. Kumar, V. et al. Radiomics: the process and the challenges. Magn. Reson. Imaging 30, 1234–1248 (2012).

    PubMed  PubMed Central  Google Scholar 

  5. Haase, A. T. et al. Quantitative image analysis of HIV-1 infection in lymphoid tissue. Science 274, 985–989 (1996).

    CAS  PubMed  Google Scholar 

  6. Lambin, P. et al. Predicting outcomes in radiation oncology — multifactorial decision support systems. Nat. Rev. Clin. Oncol. 10, 27–40 (2013).

    PubMed  Google Scholar 

  7. [No authors listed] Medicine: Computers by the Bedside. Nature 224, 636–637 (1969).

  8. Schoolman, H. & Bernstein, L. Computer use in diagnosis, prognosis, and therapy. Science 200, 926–931 (1978).

    CAS  PubMed  Google Scholar 

  9. Gillies, R. J., Kinahan, P. E. & Hricak, H. Radiomics: images are more than pictures, they are data. Radiology 278, 563–577 (2016).

    PubMed  Google Scholar 

  10. Roelofs, E. et al. International data-sharing for radiotherapy research: an open-source based infrastructure for multicentric clinical data mining. Radiother. Oncol. 110, 370–374 (2014).

    PubMed  Google Scholar 

  11. Roelofs, E. et al. Benefits of a clinical data warehouse with data mining tools to collect data for a radiotherapy trial. Radiother. Oncol. 108, 174–179 (2013).

    PubMed  PubMed Central  Google Scholar 

  12. Miotto, R., Li, L., Kidd, B. A. & Dudley, J. T. Deep patient: an unsupervised representation to predict the future of patients from the electronic health records. Sci. Rep. 6, 26094 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Nead, K. T. et al. Androgen deprivation therapy and future alzheimer's disease risk. J. Clin. Oncol. 34, 566–571 (2016).

    CAS  PubMed  Google Scholar 

  14. Gatenby, R. A., Grove, O. & Gillies, R. J. Quantitative imaging in cancer evolution and ecology. Radiology 269, 8–14 (2013).

    PubMed  PubMed Central  Google Scholar 

  15. Aerts, H. L. The potential of radiomic-based phenotyping in precision medicine: A review. JAMA Oncol. 2, 1636–1642 (2016).

    PubMed  Google Scholar 

  16. Lambin, P. et al. Decision support systems for personalized and participative radiation oncology. Adv. Drug Delivery Rev. 109, 131–153 (2017).

    CAS  Google Scholar 

  17. Vickers, A. Prediction models: revolutionary in principle, but do they do more good than harm? J. Clin. Oncol. 29, 2951–2952 (2011).

    PubMed  Google Scholar 

  18. Yip, S. S. & Aerts, H. J. Applications and limitations of radiomics. Phys. Med. Biol. 61, R150–166 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Polan, D. F., Brady, S. L. & Kaufman, R. A. Tissue segmentation of computed tomography images using a Random Forest algorithm: a feasibility study. Phys. Med. Biol. 61, 6553–6569 (2016).

    PubMed  PubMed Central  Google Scholar 

  20. Balagurunathan, Y. et al. Reproducibility and prognosis of quantitative features extracted from CT images. Transl Oncol. 7, 72–87 (2014).

    PubMed  PubMed Central  Google Scholar 

  21. Grootjans, W. et al. The impact of optimal respiratory gating and image noise on evaluation of intra-tumor heterogeneity in 18F-FDG PET imaging of lung cancer. J. Nucl. Med. 57, 1692–1698 (2016).

    PubMed  Google Scholar 

  22. Larue, R. T., Defraene, G., de Ruysscher, D., Lambin, P. & van Elmpt, W. Quantitative radiomics studies for tissue characterization: a review of technology and methodological procedures. Br. J. Radiol. 90, 20160665 (2017).

    PubMed  PubMed Central  Google Scholar 

  23. Mackin, D. et al. Measuring computed tomography scanner variability of radiomics features. Invest. Radiol 50, 757–765 (2015).

    PubMed  PubMed Central  Google Scholar 

  24. Balagurunathan, Y. et al. Test–retest reproducibility analysis of lung CT image features. J. Digit. Imaging 27, 805–823 (2014).

    PubMed  PubMed Central  Google Scholar 

  25. Zhao, B. et al. Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non–small cell lung cancer. Radiology 252, 263–272 (2009).

    PubMed  PubMed Central  Google Scholar 

  26. Zhao, B. et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci. Rep. 6, 23428 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Hatt, M. et al. Characterization of PET/CT images using texture analysis: the past, the present... any future? Eur. J. Nucl. Med. Mol. Imaging 44, 151–165 (2016).

    PubMed  PubMed Central  Google Scholar 

  28. Fang, Y. H. et al. Development and evaluation of an open-source software package “CGITA” for quantifying tumor heterogeneity with molecular images. Biomed. Res. Int. 2014, 248505 (2014).

    PubMed  PubMed Central  Google Scholar 

  29. Zhang, L. et al. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med. Phys. 42, 1341–1353 (2015).

    PubMed  PubMed Central  Google Scholar 

  30. Parmar, C., Grossmann, P., Bussink, J., Lambin, P. & Aerts, H. J. Machine learning methods for quantitative radiomic biomarkers. Sci. Rep. 5, 13087 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. https://github.com/ (2017 May 18 th).

  32. Collins, G., Reitsma, J., Altman, D. & Moons, K. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. Ann. Intern. Med. 162, 55–63 (2015).

    PubMed  Google Scholar 

  33. Lemeshow, S. & Hosmer, D. W. Jr. A review of goodness of fit statistics for use in the development of logistic regression models. Am. J. Epidemiol. 115, 92–106 (1982).

    CAS  PubMed  Google Scholar 

  34. Debray, T. P. et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J. Clin. Epidemiol. 68, 279–289 (2015).

    PubMed  Google Scholar 

  35. Steyerberg, E. et al. Assessing the performance of prediction models: a framework for traditional and novel measures. Epidemiology 21, 128–138 (2010).

    PubMed  PubMed Central  Google Scholar 

  36. Leek, J. T. & Peng, R. D. Statistics: P values are just the tip of the iceberg. Nature 520, 612 (2015).

    CAS  PubMed  Google Scholar 

  37. Drummond, C. Replicability is not reproducibility: nor is it good science. In Evaluation Methods for Machine Learning (2009).

    Google Scholar 

  38. Peng, R. D. Reproducible research in computational science. Science 334, 1226–1227 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Peng, R. D., Dominici, F. & Zeger, S. L. Reproducible epidemiologic research. Am. J. Epidemiol. 163, 783–789 (2006).

    PubMed  Google Scholar 

  40. Lambin, P. Radiomics digital phantom. CancerData.org https://www.cancerdata.org/resource/doi%3A10.17195/candat.2016.08.1 (2017).

    Google Scholar 

  41. http://www.radiomics.world/ (2017 May 18 th).

  42. Altman, D. G., McShane, L. M., Sauerbrei, W. & Taube, S. E. Reporting recommendations for tumor marker prognostic studies (REMARK): explanation and elaboration. BMC Med. 10, 51 (2012).

    PubMed  PubMed Central  Google Scholar 

  43. Pepe, M. S. & Feng, Z. Improving biomarker identification with better designs and reporting. Clin. Chem. 57, 1093–1095 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Poste, G. Biospecimens, biomarkers, and burgeoning data: the imperative for more rigorous research standards. Trends Mol. Med. 18, 717–722 (2012).

    CAS  PubMed  Google Scholar 

  45. Rosenstein, B. S. et al. Radiogenomics: radiobiology enters the era of big data and team science. Int. J. Radiat. Oncol. Biol. Phys. 89, 709–713 (2014).

    PubMed  PubMed Central  Google Scholar 

  46. Rutman, A. M. & Kuo, M. D. Radiogenomics: creating a link between molecular diagnostics and diagnostic imaging. Eur. J. Radiol. 70, 232–241 (2009).

    PubMed  Google Scholar 

  47. Chang, H. Y. et al. Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. Proc. Natl Acad. Sci. USA 102, 3738–3743 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Chen, X. et al. Gene expression patterns in human liver cancers. Mol. Biol. Cell 13, 1929–1939 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Chung, C. H., Bernard, P. S. & Perou, C. M. Molecular portraits and the family tree of cancer. Nat. Genet. 32 (Suppl.), 533–540 (2002).

    CAS  PubMed  Google Scholar 

  50. Paik, S. et al. A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. N. Engl. J. Med. 351, 2817–2826 (2004).

    CAS  PubMed  Google Scholar 

  51. Paik, S. et al. Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. J. Clin. Oncol. 24, 3726–3734 (2006).

    CAS  PubMed  Google Scholar 

  52. Segal, E., Friedman, N., Kaminski, N., Regev, A. & Koller, D. From signatures to models: understanding cancer using microarrays. Nat. Genet. 37, S38–S45 (2005).

    CAS  PubMed  Google Scholar 

  53. Diehn, M. et al. Identification of noninvasive imaging surrogates for brain tumor gene-expression modules. Proc. Natl Acad. Sci. USA 105, 5213–5218 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Gevaert, O. et al. Non-small cell lung cancer: identifying prognostic imaging biomarkers by leveraging public gene expression microarray data—methods and preliminary results. Radiology 264, 387–396 (2012).

    PubMed  PubMed Central  Google Scholar 

  55. Segal, E. et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat. Biotechnol. 25, 675–680 (2007).

    CAS  PubMed  Google Scholar 

  56. Gao, X. et al. The method and efficacy of support vector machine classifiers based on texture features and multi-resolution histogram from 18F-FDG PET-CT images for the evaluation of mediastinal lymph nodes in patients with lung cancer. Eur. J. Radiol. 84, 312–317 (2015).

    PubMed  Google Scholar 

  57. Harry, V. N., Semple, S. I., Parkin, D. E. & Gilbert, F. J. Use of new imaging techniques to predict tumour response to therapy. Lancet Oncol. 11, 92–102 (2010).

    PubMed  Google Scholar 

  58. O'Connor, J. P. et al. Quantitative imaging biomarkers in the clinical development of targeted therapeutics: current and future perspectives. Lancet Oncol. 9, 766–776 (2008).

    CAS  PubMed  Google Scholar 

  59. Panth, K. M. 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).

  60. Jemal, A. et al. Global cancer statistics. CA Cancer J. Clin. 61, 69–90 (2011).

    PubMed  Google Scholar 

  61. Wang, J. et al. Identifying triple-negative breast cancer using background parenchymal enhancement heterogeneity on dynamic contrast-enhanced MRI: a pilot radiomics study. PLoS ONE 10, e0143308 (2015).

    PubMed  PubMed Central  Google Scholar 

  62. Abernethy, A. et al. Rapid-learning system for cancer care. J. Clin. Oncol. 28, 4268–4274 (2010).

    PubMed  PubMed Central  Google Scholar 

  63. Lambin, P. et al. Modern clinical research: How rapid learning health care and cohort multiple randomised clinical trials complement traditional evidence based medicine. Acta Oncol. 54, 1289–1300 (2015).

    PubMed  Google Scholar 

  64. Dekker, A. et al. Rapid learning in practice: A lung cancer survival decision support system in routine patient care data. Radiother. Oncol. 113, 47–53 (2014).

    PubMed  PubMed Central  Google Scholar 

  65. Ginsburg, G., Staples, J. & Abernethy, A. Academic medical centers: ripe for rapid-learning personalized health care. Sci. Transl. Med. 3, 101cm27 (2011).

    PubMed  Google Scholar 

  66. Lambin, P. et al. Rapid learning health care in oncology — An approach towards decision support systems enabling customised radiotherapy. Radiother. Oncol. 109, 159–164 (2013).

    PubMed  Google Scholar 

  67. Buettner, R., Wolf, J. & Thomas, R. K. Lessons learned from lung cancer genomics: the emerging concept of individualized diagnostics and treatment. J. Clin. Oncol. 31, 1858–1865 (2013).

    CAS  PubMed  Google Scholar 

  68. Colen, R. et al. NCI Workshop Report: clinical and computational requirements for correlating imaging phenotypes with genomics signatures. Transl Oncol. 7, 556–569 (2014).

    PubMed  PubMed Central  Google Scholar 

  69. Rizzo, S. et al. CT radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur. Radiol. 26, 32–42 (2015).

    PubMed  Google Scholar 

  70. Taguchi, F. et al. Mass spectrometry to classify non-small-cell lung cancer patients for clinical outcome after treatment with epidermal growth factor receptor tyrosine kinase inhibitors: a multicohort cross-institutional study. J. Natl Cancer Inst. 99, 838–846 (2007).

    CAS  PubMed  Google Scholar 

  71. Yaromina, A., Krause, M. & Baumann, M. Individualization of cancer treatment from radiotherapy perspective. Mol. Oncol. 6, 211–221 (2012).

    PubMed  PubMed Central  Google Scholar 

  72. Dancey, J. E. et al. Guidelines for the development and incorporation of biomarker studies in early clinical trials of novel agents. Clin. Cancer Res. 16, 1745–1755 (2010).

    CAS  PubMed  Google Scholar 

  73. Krause, M., Yaromina, A., Eicheler, W., Koch, U. & Baumann, M. Cancer stem cells: targets and potential biomarkers for radiotherapy. Clin. Cancer Res. 17, 7224–7229 (2011).

    CAS  PubMed  Google Scholar 

  74. Lindegaard, J. C., Overgaard, J., Bentzen, S. M. & Pedersen, D. Is there a radiobiologic basis for improving the treatment of advanced stage cervical cancer? J. Natl Cancer Inst. Monogr. 21, 105–112 (1996).

    Google Scholar 

  75. Yaromina, A. et al. Pre-treatment number of clonogenic cells and their radiosensitivity are major determinants of local tumour control after fractionated irradiation. Radiother. Oncol. 83, 304–310 (2007).

    CAS  PubMed  Google Scholar 

  76. Lambin, P. et al. The ESTRO Breur Lecture 2009. From population to voxel-based radiotherapy: exploiting intra-tumour and intra-organ heterogeneity for advanced treatment of non-small cell lung cancer. Radiother. Oncol. 96, 145–152 (2010).

    PubMed  Google Scholar 

  77. Prokopiou, S. et al. A proliferation saturation index to predict radiation response and personalize radiotherapy fractionation. Radiat. Oncol. 10, 159 (2015).

    PubMed  PubMed Central  Google Scholar 

  78. Yin, Q. et al. Associations between tumor vascularity, vascular endothelial growth factor expression and PET/MRI radiomic signatures in primary clear-cell-renal-cell-carcinoma: proof-of-concept study. Sci. Rep. 7, 43356 (2017).

    PubMed  PubMed Central  Google Scholar 

  79. Menegakis, A. et al. Residual γH2AX foci after ex vivo irradiation of patient samples with known tumour-type specific differences in radio-responsiveness. Radiother. Oncol. 116, 480–485 (2015).

    CAS  PubMed  Google Scholar 

  80. Menegakis, A. et al. γH2AX assay in ex vivo irradiated tumour specimens: A novel method to determine tumour radiation sensitivity in patient-derived material. Radiother. Oncol. 116, 473–479 (2015).

    PubMed  Google Scholar 

  81. Slonina, D. & Gasinska, A. Intrinsic radiosensitivity of healthy donors and cancer patients as determined by the lymphocyte micronucleus assay. Int. J. Radiat. Biol. 72, 693–701 (1997).

    CAS  PubMed  Google Scholar 

  82. Fertil, B. & Malaise, E. P. Intrinsic radiosensitivity of human cell lines is correlated with radioresponsiveness of human tumors: analysis of 101 published survival curves. Int. J. Radiat. Oncol. Biol. Phys. 11, 1699–1707 (1985).

    CAS  PubMed  Google Scholar 

  83. Menegakis, A. et al. Prediction of clonogenic cell survival curves based on the number of residual DNA double strand breaks measured by γH2AX staining. Int. J. Radiat. Biol. 85, 1032–1041 (2009).

    CAS  PubMed  Google Scholar 

  84. Bjork-Eriksson, T., West, C., Karlsson, E. & Mercke, C. Tumor radiosensitivity (SF2) is a prognostic factor for local control in head and neck cancers. Int. J. Radiat. Oncol. Biol. Phys. 46, 13–19 (2000).

    CAS  PubMed  Google Scholar 

  85. Chitnis, M. M. et al. IGF-1R inhibition enhances radiosensitivity and delays double-strand break repair by both non-homologous end-joining and homologous recombination. Oncogene 33, 5262–5273 (2014).

    CAS  PubMed  Google Scholar 

  86. Du, S. et al. Attenuation of the DNA damage response by transforming growth factor-beta inhibitors enhances radiation sensitivity of non-small-cell lung cancer cells in vitro and in vivo. Int. J. Radiat. Oncol. Biol. Phys. 91, 91–99 (2015).

    CAS  PubMed  Google Scholar 

  87. Kahn, J. et al. The mTORC1/mTORC2 inhibitor AZD2014 enhances the radiosensitivity of glioblastoma stem-like cells. Neuro Oncol. 16, 29–37 (2014).

    CAS  PubMed  Google Scholar 

  88. West, C. M., Davidson, S. E., Roberts, S. A. & Hunter, R. D. The independence of intrinsic radiosensitivity as a prognostic factor for patient response to radiotherapy of carcinoma of the cervix. Br. J. Cancer 76, 1184–1190 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Cheng, Q. et al. Development and evaluation of an online three-level proton versus photon decision support prototype for head and neck cancer — Comparison of dose, toxicity and cost-effectiveness. Radiother. Oncol. 118, 281–285 (2016).

    PubMed  Google Scholar 

  90. Okada, H. et al. Immunotherapy response assessment in neuro-oncology: a report of the RANO working group. Lancet Oncol. 16, e534–e542 (2015).

    PubMed  PubMed Central  Google Scholar 

  91. Tang, C. et al. Pathology-based non-small cell lung cancer radiomics signature describing the local tumor immune environment: discovery and validation. Int. J. Radi. Oncol. Biol. Phys. 96, S42–S43 (2016).

    Google Scholar 

  92. Formenti, S. C. & Demaria, S. Combining radiotherapy and cancer immunotherapy: a paradigm shift. J. Natl Cancer Inst. 105, 256–265 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. Coulie, P. G., Van den Eynde, B. J., van der Bruggen, P. & Boon, T. Tumour antigens recognized by T lymphocytes: at the core of cancer immunotherapy. Nat. Rev. Cancer 14, 135–146 (2014).

    CAS  PubMed  Google Scholar 

  94. Schumacher, T. N. & Schreiber, R. D. Neoantigens in cancer immunotherapy. Science 348, 69–74 (2015).

    CAS  PubMed  Google Scholar 

  95. Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Mellman, I. & Steinman, R. M. Dendritic cells: specialized and regulated antigen processing machines. Cell 106, 255–258 (2001).

    CAS  PubMed  Google Scholar 

  97. Demaria, S., Golden, E. B. & Formenti, S. C. Role of local radiation therapy in cancer immunotherapy. JAMA Oncol. 1, 1325–1332 (2015).

    PubMed  Google Scholar 

  98. Golden, E. B. et al. Local radiotherapy and granulocyte-macrophage colony-stimulating factor to generate abscopal responses in patients with metastatic solid tumours: a proof-of-principle trial. Lancet Oncol. 16, 795–803 (2015).

    CAS  PubMed  Google Scholar 

  99. Garon, E. B. et al. Pembrolizumab for the treatment of non-small-cell lung cancer. N. Engl. J. Med. 372, 2018–2028 (2015).

    PubMed  Google Scholar 

  100. Rizvi, N. A. et al. Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science 348, 124–128 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. Sanghera, S., Barton, P., Bhattacharya, S., Horne, A. W. & Roberts, T. E. Pharmaceutical treatments to prevent recurrence of endometriosis following surgery: a model-based economic evaluation. BMJ Open 6, e010580 (2016).

    PubMed  PubMed Central  Google Scholar 

  102. Carvalho, S. et al. Early variation of FDG-PET radiomics features in NSCLC is related to overall survival — the “delta radiomics” concept. Radiother. Oncol. 118, S20–S21 (2016).

    Google Scholar 

  103. Fave, X. et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med. Phys. 42, 6784–6797 (2015).

    PubMed  PubMed Central  Google Scholar 

  104. Leijenaar, R. T. H. et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci. Rep. 5, 11075 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Fave, X. et al. Delta-radiomics features for the prediction of patient outcomes in non-small cell lung cancer. Sci. Rep. 7, 588 (2017).

    PubMed  PubMed Central  Google Scholar 

  106. Deasy, J. O. et al. Improving normal tissue complication probability models: the need to adopt a “data-pooling” culture. Int. J. Radi. Oncol. Biol. Phys. 76, S151–S154 (2010).

    Google Scholar 

  107. Skripcak, T. et al. Creating a data exchange strategy for radiotherapy research: Towards federated databases and anonymised public datasets. Radiother. Oncol. 113, 303–309 (2014).

    PubMed  PubMed Central  Google Scholar 

  108. Budin-Ljosne, I. et al. DataSHIELD: an ethically robust solution to multiple-site individual-level data analysis. Public Health Genomics 18, 87–96 (2015).

    PubMed  Google Scholar 

  109. Schilsky, R. L., Michels, D. L., Kearbey, A. H., Yu, P. P. & Hudis, C. A. Building a rapid learning health care system for oncology: the regulatory framework of CancerLinQ. J. Clin. Oncol. 32, 2373–2379 (2014).

    PubMed  Google Scholar 

  110. MAASTRO clinic. euroCAT: Distributed Learning for Individualized Medicine. youtube.com. http://youtu.be/ZDJFOxpwqEA. (2014).

  111. The Cancer Imaging Archive. TCIA Collections. cancerimagingarchive.net http://www.cancerimagingarchive.net/ (2017).

  112. National Cancer Institute, Division of Cancer Treatment & Diagnosis. Quantitative Imaging Network (QIN) [online], (2017).

  113. Radiological Society of North America. Quantitative Imaging Biomarkers Alliance® (QIBA®). rsna.org https://www.rsna.org/qiba/ (2017).

  114. QuiC ConCePT. quic-concept.eu http://www.quic-concept.eu/ (2017).

  115. Benedict, S. H. et al. Overview of the American Society for Radiation Oncology–National Institutes of Health–American Association of Physicists in Medicine Workshop 2015: exploring opportunities for radiation oncology in the era of big data. Int. J. Radi. Oncol. Biol. Phys. 95, 873–879 (2016).

    Google Scholar 

  116. Meldolesi, E. et al. An umbrella protocol for standardized data collection (SDC) in rectal cancer: a prospective uniform naming and procedure convention to support personalized medicine. Radiother. Oncol. 112, 59–62 (2014).

    PubMed  Google Scholar 

  117. EuroCAT Umbrella Protocol for NSCLC. CancerData.org https://www.cancerdata.org/resource/doi%3A10.17195/candat.2013.08.1 (2017 May 18 th).

  118. van Rossum, P. S. et al. The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer. J. Nucl. Med. 57, 691–700 (2016).

    CAS  PubMed  Google Scholar 

  119. 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. 34, 2157–2164 (2016).

    PubMed  Google Scholar 

  120. Coroller, T. P. et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother. Oncol. 114, 345–350 (2015).

    PubMed  PubMed Central  Google Scholar 

  121. Huynh, E. et al. CT-based radiomic analysis of stereotactic body radiation therapy patients with lung cancer. Radiother. Oncol. 120, 258–266 (2016).

    PubMed  Google Scholar 

  122. Cunliffe, A. et al. Lung texture in serial thoracic computed tomography scans: correlation of radiomics-based features with radiation therapy dose and radiation pneumonitis development. Int. J. Radiat. Oncol. Biol. Phys. 91, 1048–1056 (2015).

    PubMed  PubMed Central  Google Scholar 

  123. Liang, C. et al. The development and validation of a CT-based radiomics signature for the preoperative discrimination of stage I-II and stage III-IV colorectal cancer. Oncotarget 7, 31401–31412 (2016).

    PubMed  PubMed Central  Google Scholar 

  124. Hawkins, S. et al. Predicting malignant nodules from screening CT scans. J. Thorac. Oncol. 11, 2120–2128 (2016).

    PubMed  PubMed Central  Google Scholar 

  125. Grossmann, P. et al. Imaging-genomics reveals driving pathways of MRI derived volumetric tumor phenotype features in glioblastoma. BMC Cancer 16, 611 (2016).

    PubMed  PubMed Central  Google Scholar 

  126. Huang, Y. et al. Radiomics signature: a potential biomarker for the prediction of disease-free survival in early-stage (I or II) non-small cell lung cancer. Radiology 281, 947–957 (2016).

    PubMed  Google Scholar 

  127. Leijenaar, R. T. et al. External validation of a prognostic CT-based radiomic signature in oropharyngeal squamous cell carcinoma. Acta Oncol. 54, 1423–1429 (2015).

    CAS  PubMed  Google Scholar 

  128. Cui, Y. et al. Quantitative analysis of 18F-fluorodeoxyglucose positron emission tomography identifies novel prognostic imaging biomarkers in locally advanced pancreatic cancer patients treated with stereotactic body radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 96, 102–109 (2016).

    PubMed  Google Scholar 

  129. Li, H. 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 281, 382–391 (2016).

    PubMed  Google Scholar 

Download references

Acknowledgements

The authors acknowledge financial support from ERC advanced grant (ERC-ADG-2015, no. 694812) and the QuIC-ConCePT project, which is partly funded by EFPI A companies and the Innovative Medicine Initiative Joint Undertaking (IMI JU) under Grant Agreement no. 115151. This research is also supported by the Dutch Technology Foundation STW (grant no. 10696 duCAT & P14-19 Radiomics STRaTegy), which is the applied science division of NWO, and the Technology Programme of the Ministry of Economic Affairs. Authors also acknowledge financial support from the National Institute of Health (NIH-USA U01 CA 143062–01, Radiomics of NSCLC), EU 7 th framework program (EURECA, ARTFORCE – no. 257144, REQUITE – no. 601826), SME phase 2 (EU proposal 673780 – RAIL), the European Program H2020 (BD2Decide – PHC30-689715, ImmunoSABR – no. 733008, PREDICT - ITN no. 766276), Kankeronderzoekfonds Limburg from the Health Foundation Limburg and the Dutch Cancer Society (KWF UM 2011–5020, KWF UM 2009–4454, KWF MAC 2013–6425, KWF MAC 2013–6089) and Alpe d'HuZes-KWF (DESIGN), Center for Translational Molecular Medicine (TraIT), EUROSTARS (SeDI, CloudAtlas, and DART), Interreg V-A Euregio Meuse-Rhine (“Euradiomics”) and Varian Medical Systems (VATE and ROO).

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Correspondence to Philippe Lambin.

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A.D., leader of the Knowledge Engineering division at MAASTRO, A.J., T.L., J. v- S. and S.W. declare they receive financial support from Varian Medical Systems, a company developing a rapid learning health-care system. R.L. is a salaried employee of, and T.D. consults for ptTheragnostic B.V., a company developing biomarkers and software to individualize radiotherapy treatment. R.T.H.L. and P.L. are co-inventors of radiomics patents (EP2793164A1, US20160203599A1, and WO 2016060557A1).

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Glossary

Phantom studies

An artificial structure that imitates human tissue properties is scanned on multiple machines to characterize scan output against a known physical standard.

Calibration-in-the-large

Describes whether the predictions deviate systematically (intercept), whereas the calibration slope should ideally be equal to 1.

The independence assumption

The definition in terms of conditional probabilities is that the probability of B is not changed by knowing that A has occurred. Statistically independent variables are always uncorrelated, but the converse is not necessarily true.

Feature discretization

The process of converting continuous features to discrete binned interval features.

Bootstrapping

Measures the accuracy (defined in terms of bias, variance, confidence intervals, prediction error, etc.) to characterize the sample distribution by way of repeated random sampling methods.

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Lambin, P., Leijenaar, R., Deist, T. et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14, 749–762 (2017). https://doi.org/10.1038/nrclinonc.2017.141

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