AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics



The quantitative imaging features (radiomics) that can be obtained from the different modalities of current-generation hybrid imaging can give complementary information with regard to the tumour environment, as they measure different morphologic and functional imaging properties. These multi-parametric image descriptors can be combined with artificial intelligence applications into predictive models. It is now the time for hybrid PET/CT and PET/MRI to take the advantage offered by radiomics to assess the added clinical benefit of using multi-parametric models for the personalized diagnosis and prognosis of different disease phenotypes.


The aim of the paper is to provide an overview of current challenges and available solutions to translate radiomics into hybrid PET-CT and PET-MRI imaging for a smart and truly multi-parametric decision model.

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  1. 1.

    Bettinardi V, Mancosu P, Danna M, Giovacchini G, Landoni C, Picchio M, et al. Two dimensional vs three-dimensional imaging in whole body oncologic PET/CT: a discovery–STE phantom and patient study. Q J Nucl Med Mol Imaging. 2007;51(3):214–23.

    CAS  PubMed  Google Scholar 

  2. 2.

    Bailey DL, Pichler BJ, Gückel B, Antoch G, Barthel H, Bhujwalla ZM, et al. Combined PET/MRI: global warming—summary report of the 6th International Workshop on PET/MRI, March 27–29, 2017, Tübingen, Germany. Mol Imaging Biol. 2018;20(1):4–20.

    CAS  Article  PubMed  Google Scholar 

  3. 3.

    Rizzo G, Castiglioni I, Russo G, Tana MG, Dell’Acqua F, Gilardi MC, et al. Using deconvolution to improve PET spatial resolution in OSEM iterative reconstruction. Meth Inf Med. 2007;46(3):231–5.

    CAS  Google Scholar 

  4. 4.

    Gallivanone F, Canevari C, Gianolli G, Salvatore C, Della Rosa PA, Gilardi MC, et al. A partial volume effect correction tailored for 18F-FDG-PET oncological studies. Biomed Res Int. 2013;2013:780458.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  5. 5.

    Bettinardi V, Castiglioni I, De Bernardi E, Gilardi MC. PET quantification: strategies for partial volume correction. Clin Transl Imaging. 2014;2(3):199–218.

    Google Scholar 

  6. 6.

    Soret M, Bacharach SL, Buvat I. Partial-volume effect in PET tumor imaging. J Nucl Med. 2007;48(6):932–45.

    Article  PubMed  Google Scholar 

  7. 7.

    Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92. Erratum in: N Engl J Med. 2012 Sep 6;367(10):976.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Aerts HJ, Rios-Velazquez E, Leijenaar RT, Parmar C, Grossmann P, Cavalho S, et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun. 2014;5:4006.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  9. 9.

    Hastie T, Tibshirani R, Friedman J. The elements of statistical learning — data mining, inference, and prediction. 2nd ed. New York: Springer; 2008.

    Google Scholar 

  10. 10.

    Segal E, Sirlin CB, Ooi C, Adler AS, Gollub J, Chen X, et al. Decoding global gene expression programs in liver cancer by noninvasive imaging. Nat Biotech. 2007;25(6):675–80.

    CAS  Google Scholar 

  11. 11.

    Leger S, Zwanenburg A, Pilz K, et al. A comparative study of machine learning methods for time-to-event survival data for radiomics risk modelling. Sci Rep. 2017;7:13206.

    PubMed  PubMed Central  Google Scholar 

  12. 12.

    Parmar C, Grossmann P, Bussink J, et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep. 2015;5:13087.

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Nanni L, Brahnam S, Salvatore C, Castiglioni I. Texture descriptors and voxels for the early diagnosis of Alzheimer's disease. Artif Intell Med. 2019;97:19–26.

    PubMed  Google Scholar 

  14. 14.

    Mazo C, Alegre E, Trujillo M. Classification of cardiovascular tissues using LBP based descriptors and a cascade SVM. Comput Methods Prog Biomed. 2017;147:1–10.

    Article  Google Scholar 

  15. 15.

    Koster MJ, Matteson EL, Warrington KJ. Large-vessel giant cell arteritis: diagnosis, monitoring and management. Rheumatology (Oxford). 2018;57(suppl_2):ii32–42.

    CAS  Article  Google Scholar 

  16. 16.

    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures. They are data. Radiology. 2016;278(2):151169.

    Article  Google Scholar 

  17. 17.

    El Naqa I, Grigsby PW, Apte A, Kidd E, Donnelly E, Khullar D, et al. Exploring feature-based approaches in PET images for predicting cancer treatment outcomes. Pattern Recogn. 2009;42(6):1162–71.

    Google Scholar 

  18. 18.

    S. Reuzé, A. Schernberg, F. Orlhac, R. Sun, C. Chargari, L. Dercle, E. Deutsch, I. Buvat, and C. Robert. Radiomics in nuclear medicine applied to radiation therapy: methods, pitfalls, and challenges. Int J Rad Oncology. 2018;102(4):1117–42.

    Google Scholar 

  19. 19.

    Zwanenburg A, Leger S, Vallières M, Löck S. Image biomarker standardisation initiative. 2018 [Current version v9 2019].

  20. 20.

    Sakurai H, Asamura H, Miyaoka E, Yoshino I, Fujii Y, Nakanishi Y, et al. Differences in the prognosis of resected lung adenocarcinoma according to the histological subtype: a retrospective analysis of Japanese lung cancer registry data. Eur J Cardiothorac Surg. 2015;45:100–7.

    Article  Google Scholar 

  21. 21.

    Mann RM, Kuhl CK, Kinkel K, Boetes C. Breast MRI: guidelines from the European Society of Breast Imaging. Eur Radiol. 2008;18(7):1307–18.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Kunkel M, Reichert TE, Benz P, Lehr HA, Jeong JH, Wieand S, et al. Overexpression of Glut-1 and increased glucose metabolism in tumors are associated with a poor prognosis in patients with oral squamous cell carcinoma. Cancer. 2003;97(4):1015–24.

    CAS  PubMed  Google Scholar 

  23. 23.

    Basu S, Kwee TC, Gatenby R, Saboury B, Torigian DA, Alavi A. Evolving role of molecular imaging with PET in detecting and characterizing heterogeneity of cancer tissue at the primary and metastatic sites, a plausible explanation for failed attempts to cure malignant disorders. Eur J Nucl Med Mol Imaging. 2011;38:987–91.

    Article  PubMed  Google Scholar 

  24. 24.

    Tixier F, Groves AM, Goh V, Hatt M, Ingrand P, Le Rest CC, et al. Correlation of intra-tumor 18F-FDG uptake heterogeneity indices with perfusion CT derived parameters in colorectal cancer. PLoS One. 2014;9(6):e99567.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Rockwell S, Dobrucki IT, Kim EY, Marrison ST, Vu VT. Hypoxia and radiation therapy: past history, ongoing research, and future promise. Curr Mol Med. 2009;9(4):442–58.

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Hatt M, Hanzouli H, Rest CCL, Visvikis D. Comparison of edge-preserving filters for unbiased quantification in 18F-FDG PET imaging. J Nucl Med. 2015;56:1828.

    Google Scholar 

  27. 27.

    Rizzo S, Botta F, Raimondi S, Origgi D, Fanciullo C, Morganti AG, et al. Radiomics: the facts and the challenges of image analysis. Eur Radiol Exp. 2018;2(1):36.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Win T, Miles KA, Janes SM, Ganeshan B, Shastry M, Endozo R, et al. Tumor heterogeneity and permeability as measured on the CT component of PET/CT predict survival in patients with nonsmall cell lung cancer. Clin Cancer Res. 2013;19:3591–9.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Yoon SH, Park CM, Park SJ, Yoon J-H, Hahn S, Goo JM. Tumor heterogeneity in lung cancer: assessment with dynamic contrast-enhanced MR imaging. Radiology. 2016;280(3):940–8.

    Article  PubMed  Google Scholar 

  30. 30.

    Michallek F, Dewey M. Fractal analysis in radiological and nuclear medicine perfusion imaging: a systematic review. Eur Radiol. 2014;24:60–9.

    Article  PubMed  Google Scholar 

  31. 31.

    O’Sullivan F, Roy S, Eary J. A statistical measure of tissue heterogeneity with application to 3D PET sarcoma data. Biostatistics. 2003;4:433–48.

    PubMed  Google Scholar 

  32. 32.

    Chalkidou A, O’Doherty MJ, Marsden PK. False discovery rates in PET and CT studies with texture features: a systematic review. PLoS One. 2015;10(5):e0124165.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Maani R, Yang YH, Kaira S. Voxel-based texture analysis of the brain. Plos One. 2015;10(3):e0117759.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Scalco E, Rizzo G. Texture analysis of medical images for radiotgerapy applications. Br J Radiol. 2017;90(1070):20160642.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Fave X, Cook M, Frederick A, Zhang L, Yang J, Fried D, et al. Preliminary investigation into sources of uncertainty in quantitative imaging features. Comput Med Imaging Graph. 2015;44:54–61.

    Article  PubMed  Google Scholar 

  36. 36.

    Mackin D, Fave X, Zhang L, Fried D, Yang J, Taylor B, et al. Measuring computed tomography scanner variability of radiomics features. Investig Radiol. 2015;50(11):757–65.

    Article  Google Scholar 

  37. 37.

    Orlhac F, Soussan M, Maisonobe JA, Garcia CA, Vanderlinden B, Buvat I. Tumor texture analysis in 18F-FDG PET: relationships between texture parameters, histogram indices, standardized uptake values, metabolic volumes, and total lesion glycolysis. J Nucl Med. 2014;55(3):414–22.

    CAS  Article  PubMed  Google Scholar 

  38. 38.

    Becker AS, Wagner MW, Wurnig MC, Boss A. Diffusion-weighted imaging of the abdomen: Impact of b-values on texture analysis features. NMR Biomed. 2017; 30(1).

    Google Scholar 

  39. 39.

    Orlhac F, Nioche C, Soussan M, Buvat I. Understanding changes in tumor texture indices in PET: a comparison between visual assessment and index values in simulated and patient data. J Nucl Med. 2017;58(3):387–92.

    Article  PubMed  Google Scholar 

  40. 40.

    Collewet G, Strzelecki M, Mariette F. Influence of MRI acquisition protocols and image intensity normalization methods on texture classification. Magn Reson Imaging. 2004;22(1):81–91.

    CAS  Article  PubMed  Google Scholar 

  41. 41.

    Gallivanone F, Carne I, Interlenghi M, D'Ambrosio D, Baldi M, Fantinato D, et al. A method for manufacturing oncological phantoms for the quantification of 18F-FDG PET and DW-MRI studies. Contrast Media Mol Imaging. 2017;2017:3461684.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. 42.

    Boellaard R, Delgado-Bolton R, Oyen WJ, Giammarile F, Tatsch K, Eschner W, et al. FDG PET/CT: EANM procedure guidelines for tumour imaging: version 2.0. Eur J Nucl Med Mol Imaging. 2015;42(2):328–54.

    CAS  Article  Google Scholar 

  43. 43.

    Sollini M, Cozzi L, Antunovic L, Chiti A, Kirienko M. PET Radiomics in NSCLC: state of the art and a proposal for harmonization of methodology. Sci Rep. 2017;7(1):358.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Orlhac F, Boughdad S, Philippe C, Stalla-Bourdillon H, Nioche C, Champion L, et al. A Postreconstruction harmonization method for multicenter radiomic studies in PET. J Nucl Med. 2018;59(8):1321–8.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Thie JA. Understanding the standardized uptake value, its methods, and implications for usage. J Nucl Med. 2004;45(9):1431–4.

    PubMed  Google Scholar 

  46. 46.

    Zhou W, Bartlett DJ, Diehn FE, Glazebrook KN, Kotsenas AL, Carter RE, Fletcher JG, McCollough CH, Leng S. Reduction of metal artifacts and improvement in dose efficiency using photon-counting detector computed tomography and tin filtration. Invest Radiol. 2018;54(4):204–11.

    CAS  PubMed  Google Scholar 

  47. 47.

    Vovk U, Pernus F, Likar B. A review of methods for correction of intensity inhomogeneity in MRI. IEEE Trans Med Imaging. 2007;26(3):405–21.

    PubMed  Google Scholar 

  48. 48.

    Gallivanone F, Stefano A, Grosso E, Canevari C, Gianolli L, Messa C, et al. PVE correction in PET-CT whole-body oncological studies from PVE-affected images. IEEE Trans Nucl Sci. 2011;58(3):736–47.

    Google Scholar 

  49. 49.

    Strul D, Bendriem B. Robustness of anatomically guided pixel-by-pixel algorithms for partial volume effect correction in positron emission tomography. J Cereb Blood Flow Metab. 1999;19(5):547–59.

    CAS  PubMed  Google Scholar 

  50. 50.

    Erlandsson K, Buvat I, Pretorius PH, Thomas BA, Hutton BF. A review of partial volume correction techniques for emission tomography and their applications in neurology, cardiology and oncology. Phys Med Biol. 2012;57(21):R119–59.

    Article  PubMed  Google Scholar 

  51. 51.

    Rousset O, Rahmim A, Alavi A, Zaidi H. Partial volume correction strategies in PET. PET Clin. 2007; 2(2):235–249.

    Article  PubMed  Google Scholar 

  52. 52.

    Zaidi H, Ruest T, Schoenahl F, Montandon ML. Comparative assessment of statistical brain MR image segmentation algorithms and their impact on partial volume correction in PET. Neuroimage. 2006;32(4):1591–607.

    PubMed  Google Scholar 

  53. 53.

    Moore SC, Southekal S, Park MA, McQuaid SJ, Kijewski MF, Müller SP. Improved regional activity quantitation in nuclear medicine using a new approach to correct for tissue partial volume and spillover effects. IEEE Trans Med Imaging. 2012;31(2):405–16.

    Article  PubMed  Google Scholar 

  54. 54.

    Southekal S, McQuaid SJ, Kijewski MF, Moore SC. Evaluation of a method for projection-based tissue-activity estimation within small volumes of interest. Phys Med Biol. 2012;57(3):685–701.

    Article  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Yan J, Lim JC, Townsend DW. MRI-guided brain PET image filtering and partial volume correction. Phys Med Biol. 2015; 60(3):961–976.

    Article  PubMed  Google Scholar 

  56. 56.

    Incoronato M, Aiello M, Infante T, Cavaliere C, Grimaldi AM, Mirabelli P, et al. Radiogenomic analysis of oncological data: a technical survey. Int J Mol Sci. 2017;18(4):e805.

    CAS  Article  PubMed  Google Scholar 

  57. 57.

    Foster B, Bagci U, Mansoor A, Xu Z, Mollura DJ. A review on segmentation of positron emission tomography images. Comput Biol Med. 2014;50:76–96.

    Article  PubMed  Google Scholar 

  58. 58.

    Zaidi H, Alavi A. Naqa. Novel quantitative PET techniques for clinical decision support in oncology. Semin Nucl Med. 2018;48(6):548–64.

    Article  PubMed  Google Scholar 

  59. 59.

    Gallivanone F, Interlenghi M, Canervari C, Castiglioni I. A fully automatic, threshold-based segmentation method for the estimation of the metabolic tumor volume from PET images: validation on 3D printed anthropomorphic oncological lesions. J Instrum. 2016;11(1):C01022.

    Google Scholar 

  60. 60.

    Gallivanone F, Panzeri MM, Canevari C, Losio C, Gianolli L, De Cobelli F, et al. Biomarkers from in vivo molecular imaging of breast cancer: pretreatment 18F-FDG PET predicts patient prognosis, and pretreatment DWI-MR predicts response to neoadjuvant chemotherapy. MAGMA. 2017;30(4):359–73.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Veeraraghavan H, Dashevsky BZ, Onishi N, Sadinski M, Morris E, Deasy JO. Sutton appearance constrained semi-automatic segmentation from DCE-MRI is reproducible and feasible for breast cancer radiomics: a feasibility study. Sci Rep. 2018;8(1):4838.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Huang X, Sun W, Tseng TB, Li C, Qian W. Fast and fully-automated detection and segmentation of pulmonary nodules in thoracic CT scans using deep convolutional neural networks. Comput Med Imaging Graph. 2019;74:25–36.

    Article  PubMed  Google Scholar 

  63. 63.

    Ma Z, Wu X, Song Q, Luo Y, Wang Y, Zhou J. Automated nasopharyngeal carcinoma segmentation in magnetic resonance images by combination of convolutional neural networks and graph cut. Exp Ther Med. 2018;16(3):2511–21.

    Article  PubMed  PubMed Central  Google Scholar 

  64. 64.

    Haga A, Takahashi W, Aoki S, Nawa K, Yamashita H, Abe O, et al. Classification of early stage non-small cell lung cancers on computed tomographic images into histological types using radiomic features: interobserver delineation variability analysis. Radiol Phys Technol. 2018;11(1):27–35.

    Article  PubMed  Google Scholar 

  65. 65.

    Hatt M, Laurent B, Fayad H, Jaouen V, Visvikis D, Le Rest CC. Tumour functional sphericity from PET images: prognostic value in NSCLC and impact of delineation method. Eur J Nucl Med Mol Imaging. 2018;45(4):630–41.

    CAS  Article  PubMed  Google Scholar 

  66. 66.

    Huang Q, Lu L, Dercle L, Lichtenstein P, Li Y, Yin Q, et al. Interobserver variability in tumor contouring affects the use of radiomics to predict mutational status. J Med Imaging (Bellingham). 2018;5(1):011005.

    Article  Google Scholar 

  67. 67.

    Traverso A, Wee L, Dekker A, Gillies R. Repeatability and reproducibility of radiomic features: a systematic review. Int J Radiat Oncol Biol Phys. 2018;102(4):1143–58.

    Article  PubMed  PubMed Central  Google Scholar 

  68. 68.

    Gallivanone F, Interlenghi M, D'Ambrosio D, Trifirò G, Castiglioni I. Parameters influencing PET imaging features: a phantom study with irregular and heterogeneous synthetic lesions. Contrast Media Mol Imaging. 2018;2018:5324517.

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Wu W, Parmar C, Grossmann P, Quackenbush J, Lambin P, Bussink J, et al. Exploratory study to identify radiomics classifiers for lung cancer histology. Front Oncol. 2016;6:71.

    Article  PubMed  PubMed Central  Google Scholar 

  70. 70.

    Monti S, Cavaliere C, Covello M, Nicolai E, Salvatore M, Aiello M. An evaluation of the benefits of simultaneous acquisition on PET/MR coregistration in head/neck imaging. J Healthc Eng. 2017;2017:2634389.

    Article  PubMed  PubMed Central  Google Scholar 

  71. 71.

    Lalush DS. Magnetic resonance–derived improvements in PET imaging. Magn Reson Imaging Clin N Am. 2017;25(2):257–72.

    Article  PubMed  Google Scholar 

  72. 72.

    Manber R, Thielemans K, Hutton BF, Wan S, Fraioli F, Barnes A, et al. Clinical impact of respiratory motion correction in simultaneous PET/MR, using a joint PET/MR predictive motion model. J Nucl Med. 2018;59(9):1467–73.

    Article  PubMed  PubMed Central  Google Scholar 

  73. 73.

    Fürst S, Grimm R, Hong I, Souvatzoglou M, Casey ME, Schwaiger M, et al. Motion correction strategies for integrated PET/MR. J Nucl Med. 2015;56(2):261–9.

    CAS  Article  PubMed  Google Scholar 

  74. 74.

    Huang SY, Franc BL, Harnish RJ, Liu G, Mitra D, Copeland TP, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ Breast Cancer. 2018;4:24.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  75. 75.

    Leijenaar RT, Nalbantov G, Carvalho S, van Elmpt WJ, Troost EG, Boellaard R, et al. The effect of SUV discretization in quantitative FDG-PET radiomics: the need for standardized methodology in tumor texture analysis. Sci Rep. 2015;5:11075.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  76. 76.

    Orlhac F, Soussan M, Chouahnia K, Martinod E, Buvat I. 18F-FDG PET-derived textural indices reflect tissue-specific uptake pattern in non-small cell lung cancer. PLoS One. 2015;10(12):e0145063.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  77. 77.

    Lu L, Ehmke RC, Schwartz LH, Zhao B. Assessing agreement between radiomic features computed for multiple CT imaging settings. PLoS One. 2016;11(12):e0166550.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Tixier F, Hatt M, Le Rest CC, Le Pogam A, Corcos L, Visvikis D. Reproducibility of tumor uptake heterogeneity characterization through textural feature analysis in 18F-FDG PET. J Nucl Med. 2012;53:693–700.

    Article  PubMed  PubMed Central  Google Scholar 

  79. 79.

    Buvat I, Orlhac F, Soussan M. Tumor texture analysis in PET: where do we stand? J Nucl Med. 2015;56(11):1642–4.

    CAS  PubMed  Google Scholar 

  80. 80.

    Besenyi Z, Ágoston G, Hemelein R, Annamária B, Nagy FT, Varga A, et al. Detection of myocardial inflammation by 18F-FDG-PET/CT in patients with systemic sclerosis without cardiac symptoms: a pilot study. Clin Exp Rheumatol. 2018.

  81. 81.

    Berti A, Della-Torre E, Gallivanone F, Canevari C, Milani R, Lanzillotta M, et al. Quantitative measurement of 18F-FDG PET/CT uptake reflects the expansion of circulating plasmablasts in IgG4-related disease. Rheumatology (Oxford). 2017;56(12):2084–92.

    CAS  Article  Google Scholar 

  82. 82.

    Mabey E, Rutherford A, Galloway J. Differentiating disease flare from infection: a common problem in rheumatology. Do 18F-FDG PET/CT scans and novel biomarkers hold the answer? Curr Rheumatol Rep. 2018;20(11):70.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  83. 83.

    Schwartz LH, Seymour L, Litière S, Ford R, Gwyther S, Mandrekar S, et al. RECIST 1.1 — standardisation and disease-specific adaptations: perspectives from the RECIST Working Group. Eur J Cancer. 2016;62:138–45.

    Article  PubMed  PubMed Central  Google Scholar 

  84. 84.

    Wahl RL, Jacene H, Kasamon Y, Lodge MA. From RECIST to PERCIST: evolving considerations for PET response criteria in solid tumors. J Nucl Med. 2009;50(Suppl):122S–50S.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Vargas HA, Hötker AM, Goldman DA, Moskowitz CS, Gondo T, Matsumoto K, et al. Updated prostate imaging reporting and data system (PIRADS v2) recommendations for the detection of clinically significant prostate cancer using multiparametric MRI: critical evaluation using whole-mount pathology as standard of reference. Eur Radiol. 2016;26(6):1606–12.

    CAS  Article  PubMed  Google Scholar 

  86. 86.

    Carvalho S, Leijenaar RTH, Troost EGC, van Timmeren JE, Oberije C, van Elmpt W, et al. 18F-fluorodeoxyglucose positron-emission tomography (FDG-PET) — radiomics of metastatic lymph nodes and primary tumor in non-small cell lung cancer (NSCLC) — a prospective externally validated study. PLoS One. 2018;13(3):e0192859.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Li K, Sun H, Lu Z, Xin J, Zhang L, Guo Y, et al. Value of [18F]FDG PET radiomic features and VEGF expression in predicting pelvic lymphatic metastasis and their potential relationship in early-stage cervical squamous cell carcinoma. Eur J Radiol. 2018;106:160–6.

    Article  PubMed  Google Scholar 

  88. 88.

    De Bernardi E, Buda A, Guerra L, Vicini D, Elisei F, Landoni C, et al. Radiomics of the primary tumour as a tool to improve 18F-FDG-PET sensitivity in detecting nodal metastases in endometrial cancer. EJNMMI Res. 2018;8(1):86.

    Article  PubMed  PubMed Central  Google Scholar 

  89. 89.

    van Helden EJ, Vacher YJL, van Wieringen WN, van Velden FHP, Verheul HMW, Hoekstra OS, et al. Radiomics analysis of pre-treatment [18F]FDG PET/CT for patients with metastatic colorectal cancer undergoing palliative systemic treatment. Eur J Nucl Med Mol Imaging. 2018;45(13):2307–17.

    Article  PubMed  PubMed Central  Google Scholar 

  90. 90.

    Parvez A, Tau N, Hussey D, Maganti M, Metser U. 18F-FDG PET/CT metabolic tumor parameters and radiomics features in aggressive non-Hodgkin's lymphoma as predictors of treatment outcome and survival. Ann Nucl Med. 2018.

    CAS  PubMed  Google Scholar 

  91. 91.

    Beukinga RJ, Hulshoff JB, Mul VEM, Noordzij W, Kats-Ugurlu G, Slart RHJA, et al. Prediction of response to neoadjuvant chemotherapy and radiation therapy with baseline and restaging 18F-FDG PET imaging biomarkers in patients with esophageal cancer. Radiology. 2018;287(3):983–92.

    Article  PubMed  Google Scholar 

  92. 92.

    Sohaib SA, Turner B, Hanson JA, Farquharson M, Oliver RT, Reznek RH. CT assessment of tumour response to treatment: comparison of linear, cross-sectional and volumetric measures of tumour size. Br J Radiol. 2000;73(875):1178–84.

    CAS  Article  PubMed  Google Scholar 

  93. 93.

    Coroller TP, Grossmann P, Hou Y, Rios Velazquez E, Leijenaar RT, Hermann G, et al. CT-based radiomic signature predicts distant metastasis in lung adenocarcinoma. Radiother Oncol. 2015;114(3):345–50.

    Article  PubMed  PubMed Central  Google Scholar 

  94. 94.

    Sato Y, Yanagawa M, Hata A, Enchi Y, Kikuchi N, Honda O, et al. Volumetric analysis of the thymic epithelial tumors: correlation of tumor volume with the WHO classification and Masaoka staging. J Thorac Dis. 2018;10(10):5822–32.

    Article  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Wang H, Schabath MB, Liu Y, Berglund AE, Bloom GC, Kim J, et al. Semiquantitative computed tomography characteristics for lung adenocarcinoma and their association with lung cancer survival. Clin Lung Cancer. 2015;16(6):e141–63.

    Article  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Groheux D, Giacchetti S, Espié M, Rubello D, Moretti JL, Hindié E. Early monitoring of response to neoadjuvant chemotherapy in breast cancer with 18F-FDG PET/CT: defining a clinical aim. Eur J Nucl Med Mol Imaging. 2011;38(3):419–25.

    Article  PubMed  Google Scholar 

  97. 97.

    Groheux D, Giacchetti S, Moretti JL, Porcher R, Espié M, Lehmann-Che J, et al. Correlation of high 18F-FDG uptake to clinical, pathological and biological prognostic factors in breast cancer. Eur J Nucl Med Mol Imaging. 2011;38(3):426–35.

    Article  PubMed  Google Scholar 

  98. 98.

    Gallivanone F, Canevari C, Sassi I, Zuber V, Marassi A, Gianolli L, et al. Partial volume corrected 18F-FDG PET mean standardized uptake value correlates with prognostic factors in breast cancer. Q J Nucl Med Mol Imaging. 2014;58(4):424–39.

    CAS  PubMed  Google Scholar 

  99. 99.

    Giganti F, De Cobelli F, Canevari C, Orsenigo E, Gallivanone F, Esposito A, et al. Response to chemotherapy in gastric adenocarcinoma with diffusion-weighted MRI and (18) F-FDG-PET/CT: correlation of apparent diffusion coefficient and partial volume corrected standardized uptake value with histological tumor regression grade. J Magn Reson Imaging. 2014;40(5):1147–57.

    Article  PubMed  Google Scholar 

  100. 100.

    Picchio M, Kirienko M, Mapelli P, Dell'Oca I, Villa E, Gallivanone F, et al. Predictive value of pre-therapy (18)F-FDG PET/CT for the outcome of (18)F-FDG PET-guided radiotherapy in patients with head and neck cancer. Eur J Nucl Med Mol Imaging. 2014;41(1):21–31.

    CAS  Article  PubMed  Google Scholar 

  101. 101.

    Inglese M, Cavaliere C, Monti S, Forte E, Incoronato M, Nicolai E, et al. A multi-parametric PET/MRI study of breast cancer: evaluation of DCE-MRI pharmacokinetic models and correlation with diffusion and functional parameters. NMR Biomed. 2019; 32(1):e4026.

    PubMed  Google Scholar 

  102. 102.

    Vallières M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med Biol. 2015;60(14):5471–96.

    Article  PubMed  Google Scholar 

  103. 103.

    Lv W, Yuan Q, Wang Q, Ma J, Feng Q, Chen W, Rahmim A, Lu L. Radiomics analysis of PET and CT components of PET/CT imaging integrated with clinical parameters: application to prognosis for nasopharyngeal carcinoma. Mol Imaging Biol. 2019.

    CAS  PubMed  Google Scholar 

  104. 104.

    Lin G, Keshari KR, Park JM. Cancer metabolism and tumor heterogeneity: imaging perspectives using MR imaging and spectroscopy. Contrast Media Mol Imaging. 2017;2017:6053879.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  105. 105.

    Crowe W, Wang L, Zhang Z, Varagic J, Bourland JD, Chan MD, et al. MRI evaluation of the effects of whole brain radiotherapy on breast cancer brain metastasis. Int J Radiat Biol. 2018;30:1–27.

    Article  Google Scholar 

  106. 106.

    Syed AK, Woodall R, Whisenant JG, Yankeelov TE, Sorace AG. Characterizing trastuzumab-induced alterations in Intratumoral heterogeneity with quantitative imaging and immunohistochemistry in HER2+ breast cancer. Neoplasia. 2018;21(1):17–29.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Sun NN, Liu C, Ge XL, Wang J. Dynamic contrast-enhanced MRI for advanced esophageal cancer response assessment after concurrent chemoradiotherapy. Diagn Interv Radiol. 2018;24(4):195–202.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Salvaggio G, Calamia M, Purpura P, Bartolotta TV, Picone D, Dispensa N, et al. Role of apparent diffusion coefficient values in prostate diseases characterization on diffusion-weighted magnetic resonance imaging. Minerva Urol Nefrol. 2018;71(2):154–60.

    Article  PubMed  Google Scholar 

  109. 109.

    Oda T, Sue M, Sasaki Y, Ogura I. Diffusion-weighted magnetic resonance imaging in oral and maxillofacial lesions: preliminary study on diagnostic ability of apparent diffusion coefficient maps. Oral Radiol. 2018;34(3):224–8.

    Article  PubMed  Google Scholar 

  110. 110.

    Li QW, Qiu B, Wang B, Wang DL, Yin SH, Yang H, et al. Prediction of pathologic responders to neoadjuvant chemoradiotherapy by diffusion-weighted magnetic resonance imaging in locally advanced esophageal squamous cell carcinoma: a prospective study. Dis Esophagus. 2018; 31(2).

  111. 111.

    Hamstra DA, Rehemtulla A, Ross BD. Diffusion magnetic resonance imaging: a biomarker for treatment response in oncology. J Clin Oncol. 2007;25(26):4104–9.

    Article  PubMed  Google Scholar 

  112. 112.

    Arponen O, Sudah M, Masarwah A, Taina M, Rautiainen S, Könönen M, et al. Diffusion-weighted imaging in 3.0 tesla breast MRI: diagnostic performance and tumor characterization using small subregions vs. whole tumor regions of interest. PLoS One. 2015;10(10):e0138702.

    CAS  Article  PubMed  Google Scholar 

  113. 113.

    Merhemic Z, Imsirovic B, Bilalovic N, Stojanov D, Boban J, Thurnher MM. Apparent diffusion coefficient reproducibility in brain tumors measured on 1.5 and 3 T clinical scanners: a pilot study. Eur J Radiol. 2018;108:249–53.

    Article  PubMed  Google Scholar 

  114. 114.

    Shukla-Dave A, Obuchowski NA, Chenevert TL, Jambawalikar S, Schwartz LH, Malyarenko D, et al. Quantitative imaging biomarkers alliance (QIBA) recommendations for improved precision of DWI and DCE-MRI derived biomarkers in multicenter oncology trials. J Magn Reson Imaging. 2018;49(7):e101-e121.

    Article  PubMed  Google Scholar 

  115. 115.

    Rana L, Sood D, Chauhan R, Shukla R, Gurnal P, Nautiyal H, et al. MR imaging of hypoxic ischemic encephalopathy - distribution patterns and ADC value correlations. Eur J Radiol Open. 2018;5:215–20.

    Article  PubMed  PubMed Central  Google Scholar 

  116. 116.

    Zhu MJ, Wang Y, Li HJ, Yang M, Mo XM, Cheng R, et al. Brain alteration in neonates with congenital heart disease using apparent diffusion coefficient histograms. Zhonghua Yi Xue Za Zhi. 2018;98(39):3162–5.

    CAS  Article  PubMed  Google Scholar 

  117. 117.

    Seyithanoğlu MH, Abdallah A, Dündar TT, Kitiş S, Aralaşmak A, Gündağ Papaker M, et al. Investigation of brain impairment using diffusion-weighted and diffusion tensor magnetic resonance imaging in experienced healthy divers. Med Sci Monit. 2018;24:8279–89.

    Article  PubMed  PubMed Central  Google Scholar 

  118. 118.

    Li Y, Jiang J, Shen T, Wu P, Zuo C. Radiomics features as predictors to distinguish fast and slow progression of mild cognitive impairment to Alzheimer's disease. Conf Proc IEEE Eng Med Biol Soc. 2018;2018:127–30.

    Article  PubMed  Google Scholar 

  119. 119.

    Chitalia RD, Kontos D. Role of texture analysis in breast MRI as a cancer biomarker: a review. J Magn Reson Imaging. 2018;49(4):927–938.

    Article  PubMed  Google Scholar 

  120. 120.

    Sah BR, Owczarczyk K, Siddique M, Cook GJR, Goh V. Radiomics in esophageal and gastric cancer. Abdom Radiol (NY). 2018;44(6):2048-2058.

    Article  Google Scholar 

  121. 121.

    Yang F, Ford JC, Dogan N, Padgett KR, Breto AL, et al. Magnetic resonance imaging (MRI)-based radiomics for prostate cancer radiotherapy. Transl Androl Urol. 2018;7(3):445–58.

    Article  PubMed  PubMed Central  Google Scholar 

  122. 122.

    Zhang X, Xu X, Tian Q, Li B, Wu Y, Yang Z, et al. Radiomics assessment of bladder cancer grade using texture features from diffusion-weighted imaging. J Magn Reson Imaging. 2017;46(5):1281–8.

    Article  PubMed  PubMed Central  Google Scholar 

  123. 123.

    Rozenberg R, Thornhill RE, Flood TA, Hakim SW, Lim C, Schieda N. Whole-tumor quantitative apparent diffusion coefficient histogram and texture analysis to predict Gleason score upgrading in intermediate-risk 3 + 4 = 7 prostate cancer. AJR Am J Roentgenol. 2016;206(4):775–82.

    Article  PubMed  Google Scholar 

  124. 124.

    Nketiah G, Elschot M, Kim E, Teruel JR, Scheenen TW, Bathen TF, et al. T2-weighted MRI-derived textural features reflect prostate cancer aggressiveness: preliminary results. Eur Radiol. 2017;27(7):3050–9.

    Article  PubMed  Google Scholar 

  125. 125.

    Sankar V, Kumar D, Clausi DA, Taylor GW, Wong A. SISC: end-to-end interpretable discovery radiomics-driven lung cancer prediction via stacked interpretable sequencing cells.

  126. 126.

    Pietikäinen M, Zhao G. Two decades of local binary patterns: a survey. In: Bingham E, Kaski S, Laaksonen J, Lampinen J (eds.) Advances in independent component analysis and learning machines. London: Academic; 2015. p. 175–210.

    Google Scholar 

  127. 127.

    Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  128. 128.

    Ravì D, Wong C, Deligianni F, Berthelot M, Andreu-Perez J, Lo B, et al. Deep learning for health informatics. IEEE J Biomed Health Inform. 2016;21(1):4–21.

    PubMed  Google Scholar 

  129. 129.

    Sharif Razavian A, Azizpour H, Sullivan J, Carlsson S. (2014). CNN features off-the-shelf: an astounding baseline for recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); 2014. p. 806–13).

  130. 130.

    Kawahara J, BenTaieb A, Hamarneh G. Deep features to classify skin lesions. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI); 2016. p. 1397–1400).

  131. 131.

    Merone M, Sansone C, Soda P. A computer-aided diagnosis system for HEp-2 fluorescence intensity classification. Artificial Intelligence in Medicine. 2019;97:71–78.

    PubMed  Google Scholar 

  132. 132.

    Boyd S, Parikh N, Chu E, Peleato B, Eckstein J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn. 2011;3:1–122.

    Google Scholar 

  133. 133.

    Karr AF, Lin X, Sanil AP, Reiter JP. Secure regression on distributed databases. J Comput Graph Stat. 2005;14:263–79.

    Article  Google Scholar 

  134. 134.

    Karr AF, Lin X, Sanil AP, Reiter JP. Privacy-preserving analysis of vertically partitioned data using secure matrix products. J Off Stat. 2009;25:125.

    Google Scholar 

  135. 135.

    Sanil AP, Karr AF, Lin X, Reiter JP. Privacy preserving regression modelling via distributed computation. In: Proc Tenth ACM SIGKDD Int Conf Knowl Discov Data Min ACM; 2004. p. 677–82.

    Google Scholar 

  136. 136.

    Chen R, Sivakumar K, Kargupta H. Learning Bayesian network structure from distributed data. In: Barbara D, Kamath C (eds.) Proceedings of the 2003 SIAM International Conference on Data Mining (SDM 2003), San Francisco CA; 2003. p. 284–8.

    Google Scholar 

  137. 137.

    Wright R, Yang Z. Privacy-preserving Bayesian network structure computation on distributed heterogeneous data. In: Proc Tenth ACM SIGKDD Int Conf Knowl Discov Data Min. New York, NY, USA: ACM; 2004. p. 713–8.

  138. 138.

    Yang Z, Wright RN. Improved privacy-preserving Bayesian network parameter learning on vertically partitioned data. 21st. Int Conf Data Eng Workshop. 2005;2005:1196.

    Article  Google Scholar 

  139. 139.

    Meng D, Sivakumar K, Kargupta H. Privacy-sensitive Bayesian network parameter learning. Data Min 2004 ICDM04 Fourth IEEE Int Conf On IEEE. 2004;2004:487–90.

    Google Scholar 

  140. 140.

    Jochems A, Deist Timo M, van Soest J, Eble M, Bulens P, Coucke P, et al. Distributed learning: developing a predictive model based on data from multiple hospitals without data leaving the hospital — a real life proof of concept. Radiother Oncol. 2016;121:459–67.

    PubMed  Google Scholar 

  141. 141.

    Jayasurya K, Fung G, Yu S, Dehing-Oberije C, De Ruysscher D, Hope A, et al. Comparison of Bayesian network and support vector machine models for twoyear survival prediction in lung cancer patients treated with radiotherapy. Med Phys. 2010;37:1401–7.

    CAS  PubMed  Google Scholar 

  142. 142.

    Lualdi M, Fasano M. Statistical analysis of proteomics data: a review on feature selection. J Proteomics. 2018;198:18-26.

    CAS  PubMed  Google Scholar 

  143. 143.

    Leijenaar RT, Carvalho S, Velazquez ER, van Elmpt WJ, Parmar C, Hoekstra OS, et al. Stability of FDG-PET Radiomics features: an integrated analysis of test–retest and inter-observer variability. Acta Oncol. 2013;52(7):1391–7.

    CAS  PubMed  Google Scholar 

  144. 144.

    O'Connor JP, Aboagye EO, Adams JE, Aerts HJ, Barrington SF, Beer AJ, et al. Imaging biomarker roadmap for cancer studies. Nat Rev Clin Oncol. 2017;14(3):169–86.

    CAS  Article  PubMed  Google Scholar 

  145. 145.

    Aerts HJ. The potential of radiomic-based phenotyping in precision medicine: a review. JAMA Oncol. 2016;2(12):1636–42.

    Article  PubMed  Google Scholar 

  146. 146.

    Antunovic L, Gallivanone F, Sollini M, Sagona A, Invento A, Manfrinato G, et al. [18F]FDG PET/CT features for the molecular characterization of primary breast tumors. Eur J Nucl Med Mol Imaging. 2017;44(12):1945–54.

    CAS  Article  PubMed  Google Scholar 

  147. 147.

    El Naqa I, Li R, Murphy MJ. Machine learning in radiation oncology: theory and applications. SpringerLink; 2015.

  148. 148.

    Huang SS. Supervised feature selection: a tutorial. Artif Intell Res. 2015;4(2):22–37.

    Article  Google Scholar 

  149. 149.

    Yoon HJ, Kim Y, Chung J, Kim BS. Predicting neo-adjuvant chemotherapy response and progression-free survival of locally advanced breast cancer using textural features of intratumoral heterogeneity on F-18 FDG PET/CT and diffusion-weighted MR imaging. Breast J. 2018;25(3):373–80.

    PubMed  Google Scholar 

  150. 150.

    Choi JW, Lee D, Hyun SH, Han M, Kim JH, Lee SJ. Intratumoural heterogeneity measured using FDG PET and MRI is associated with tumour–stroma ratio and clinical outcome in head and neck squamous cell carcinoma. Clin.Radiol. 2017;72:482–9.

    CAS  PubMed  Google Scholar 

  151. 151.

    Fave X, Mackin D, Yang J, Zhang J, Fried D, Balter P, et al. Can radiomics features be reproducibly measured from CBCT images for patients with non-small cell lung cancer? Med.Phys. 2015;42:6784–97.

    PubMed  PubMed Central  Google Scholar 

  152. 152.

    Antunes J, Viswanath S, Rusu M, Valls L, Hoimes C, Avril N, et al. Radiomics analysis on FLT-PET/MRI for characterization of early treatment response in renal cell carcinoma: a proof-of-concept study. Transl.Oncol. 2016;9:155–62.

    PubMed  PubMed Central  Google Scholar 

  153. 153.

    Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference, and prediction. Heidelberg: Springer; 2009

    Google Scholar 

  154. 154.

    Fawcett T. ROC graphs: notes and practical considerations for researchers. Mach Learn. 2004;31(1):1–38.

    Google Scholar 

  155. 155.

    Efron B, Tibshirani RJ. An introduction to the bootstrap. New York: Chapman & Hall/CRC;1994.

    Google Scholar 

  156. 156.

    Efron B, Tibshirani R. Improvements on cross-validation: the .632+ bootstrap method. J Am Stat Assoc. 1997;92(438):548–60.

    Google Scholar 

  157. 157.

    Hawkins DM, Basak SC, Mills D. Assessing model fit by cross-validation. J Chem Inf Comput Sci. 2003;43(2):579–86.

    CAS  PubMed  Google Scholar 

  158. 158.

    Raschka S. Model evaluation, model selection, and algorithm selection in machine learning. arXiv:1811.12808. 2018.

  159. 159.

    Vallieres M, Laberge S, Diamant A, El Naqa I. Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept. Phys.Med.Biol. 2017;62:8536–65.

    PubMed  Google Scholar 

  160. 160.

    Metz S, Ganter C, Lorenzen S, van Marwick S, Herrmann K, Lordick F, et al. Phenotyping of tumor biology in patients by multimodality multiparametric imaging: relationship of microcirculation, alphavbeta3 expression, and glucose metabolism. J Nucl Med. 2010;51:1691–8.

    PubMed  Google Scholar 

  161. 161.

    Soda P. A multi-objective optimisation approach for class imbalance learning. Pattern Recogn. 2011;44(8):1801–10.

    Google Scholar 

  162. 162.

    Provost FJ, Fawcett T, Kohavi R. The case against accuracy estimation for comparing induction algorithms. ICML. 1998;98:445–53.

    Google Scholar 

  163. 163.

    Efron B. Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc. 1983;78(382):316–31.

    Google Scholar 

  164. 164.

    Varma S, Simon R. Bias in error estimation when using cross-validation for model selection. BMC Bioinformatics. 2006;7(1):91.

    PubMed  PubMed Central  Google Scholar 

  165. 165.

    Bengio Y, Grandvalet Y. No unbiased estimator of the variance of k-fold cross-validation. J Mach Learn Res. 2004;5:1089–105.

    Google Scholar 

  166. 166.

    Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. Int Joint Conf Artif Intell. 1995;14(12):1137–43.

    Google Scholar 

  167. 167.

    Molinaro AM, Simon R, Pfeiffer RM. Prediction error estimation: a comparison of resampling methods. Bioinformatics. 2005;21(15):3301–7.

    CAS  PubMed  Google Scholar 

  168. 168.

    Refaeilzadeh P, Tang L, Liu H. On comparison of feature selection algorithms. In: Proceedings of AAAI Workshop on Evaluation Methods for Machine Learning II; 2007. p. 34–9.

    Google Scholar 

  169. 169.

    Avanzo M, Stancanello J, El Naqa I. Beyond imaging: the promise of radiomics. Phys.Med. 2017;38:122–39.

    Article  PubMed  Google Scholar 

  170. 170.

    Jain AK, Duin RPW, Mao J. Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell. 2000;22(1):4–38.

    Google Scholar 

  171. 171.

    Lohmann P, Kocher M, Ceccon G, Bauer EK, Stoffels G, Viswanathan S, et al. Combined FET PET/MRI radiomics differentiates radiation injury from recurrent brain metastasis. Neuroimage Clin. 2018;20:537–42.

    PubMed  PubMed Central  Google Scholar 

  172. 172.

    Vaidya M, Creach KM, Frye J, Dehdashti F, Bradley JD, El Naqa I. Combined PET/CT image characteristics for radiotherapy tumor response in lung cancer. Radiother Oncol. 2012;102(2):239–45.

    PubMed  Google Scholar 

  173. 173.

    El Naqa I, Suneja G, Lindsay PE, Hope AJ, Alaly JR, Vicic M, et al. Dose response explorer: an integrated open-source tool for exploring and modelling radiotherapy dose-volume outcome relationships. Phys Med Biol. 2006;51:5719–35.

    PubMed  Google Scholar 

  174. 174.

    Lucia F, Visvikis D, Desseroit MC, Miranda O, Malhaire JP, Robin P, et al. Prediction of outcome using pretreatment (18)F-FDG PET/CT and MRI radiomics in locally advanced cervical cancer treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2018;45:768–86.

    PubMed  Google Scholar 

  175. 175.

    Yin Q, Hung SC, Rathmell WK, Shen L, Wang L, Lin W, et al. Integrative radiomics expression predicts molecular subtypes of primary clear cell renal cell carcinoma. Clin Radiol. 2018;73:782–91.

    CAS  PubMed  PubMed Central  Google Scholar 

  176. 176.

    Parmar C, Leijenaar RTH, Grossmann P, Velazquez ER, Bussink J, Rietveld D, et al. Radiomic feature clusters and prognostic signatures specific for lung and head & neck cancer. Sci Rep. 2015;5:11044.

    PubMed  PubMed Central  Google Scholar 

  177. 177.

    Huang SY, Franc BL, Harnish RJ, Liu G, Mitra D, Copeland TP, et al. Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis. NPJ Breast Cancer. 2018;4:24.

    PubMed  PubMed Central  Google Scholar 

  178. 178.

    Vallieres M, Freeman CR, Skamene SR, El Naqa I. A radiomics model from joint FDG-PET and MRI texture features for the prediction of lung metastases in soft-tissue sarcomas of the extremities. Phys Med .Biol. 2015;60:5471–96.

    PubMed  Google Scholar 

  179. 179.

    Vriens D, Disselhorst JA, Oyen WJ, de Geus-Oei LF, Visser EP. Quantitative assessment of heterogeneity in tumor metabolism using FDG-PET. Int J Radiat Oncol Biol Phys. 2012;82:e725–31.

    PubMed  Google Scholar 

  180. 180.

    Crispin-Ortuzar M, Apte A, Grkovski M, Oh JH, Lee NY, Schoder H, et al. Predicting hypoxia status using a combination of contrast-enhanced computed tomography and [(18)F]-Fluorodeoxyglucose positron emission tomography radiomics features. Radiother Oncol. 2018;127:36–42.

    PubMed  Google Scholar 

  181. 181.

    Yosinski J, Clune J, Nguyen A, Fuchs T, Lipson H. Understanding neural networks through deep visualization.

  182. 182.

    Ganeshan B, Abaleke S, Young RCD, Chatwin CR, Miles KA. 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):137–43.

    Article  PubMed  PubMed Central  Google Scholar 

  183. 183.

    Strzelecki M, Szczypinski P, Materka A, Klepaczko A. A software tool for automatic classification and segmentation of 2D/3D medical image. Nucl Instr Meth Phys Res A. 2013;702:137–40.

    CAS  Article  Google Scholar 

  184. 184.

    Szczypinski P, Strzelecki M, Materka A, Klepaczko A. MaZda—A software package for image texture analysis. Comput Methods Prog Biomed. 2009;94(1):66–76.

    Article  Google Scholar 

  185. 185.

    Szczypinski P, Strzelecki M, Materka A. MaZda — a software for texture analysis. Proc. of ISITC 2007, November 23–23, 2007, Republic of Korea. 2007;2007:245–9.

    Google Scholar 

  186. 186.

    Nioche C, Orlhac F, Boughdad S, Reuzé S, Goya-Outi J, Robert C, et al. LIFEx: a freeware for radiomic feature calculation in multimodality imaging to accelerate advances in the characterization of tumor heterogeneity. Cancer Res. 2018;78(16):4786–9.

    CAS  Article  PubMed  Google Scholar 

  187. 187.

    Schaer R, Dicente Cid Y, Alkim E, John S, Rubin DL, Depeursinge A. Web-based tools for exploring the potential of quantitative imaging biomarkers in radiology: intensity and texture analysis on the ePAD platform. In: Biomedical texture analysis. London: Academic; 2017. p. 379–410.

    Google Scholar 

  188. 188.

    van Griethuysen JJM, Fedorov A, Parmar C, Hosny A, Aucoin N, Narayan V, et al. Computational radiomics system to decode the radiographic phenotype. Cancer Res. 2017;77(21):e104–7.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  189. 189.

    Dicente Cid Y, Castelli J, Schaer R, Scher N, Pomoni A, Prior O et al. QuantImage: an online tool for high-throughput 3D radiomics feature extraction in PET-CT. In: Biomedical texture analysis. London: Academic; 2017. p. 349–77.

    Google Scholar 

  190. 190.

    Zhang L, Fried DV, Fave XJ, Hunter LA, Yang J, Court LE. IBEX: an open infrastructure software platform to facilitate collaborative work in radiomics. Med Phys. 2015;42(3):1341–53.

    Article  PubMed  PubMed Central  Google Scholar 

  191. 191.

    3D Slicer — an open source software platform for medical image informatics, image processing, and three-dimensional visualization.

  192. 192.

    Hatt M, Tixier F, Pierce L, Kinahan PE, Le Rest CC, Visvikis D. Characterization of PET/CT images using texture analysis: the past, the present… any future? Eur J Nucl Med Mol Imaging. 2017;44(1):151–65.

    PubMed  Google Scholar 

  193. 193.

    Feedback Medical.

  194. 194.


  195. 195.

    Quantib: AI in radiology.

  196. 196.

    Oncoradiomics SA.

  197. 197.

    Zebra Medical.

  198. 198.

    Median Technologies.

  199. 199.

    MaxQ AI.

  200. 200.


  201. 201.


  202. 202.

    Regge D, Mazzetti S, Giannini V, Bracco C, Stasi M. Big data in oncologic imaging. Radiol Med. 2017;122(6):458–63.

    Article  PubMed  Google Scholar 

  203. 203.

    European Society of Radiology (ESR). ESR position paper on imaging biobanks. Insights imaging. 2015;6(4):403–10.

    Article  Google Scholar 

  204. 204.

    Neri E, Regge D. Imaging biobanks in oncology: European perspective. Future Oncol. 2017;13(5):433–41.

    CAS  Article  PubMed  Google Scholar 

  205. 205.

    TheCancer Imaging Archive.

  206. 206.

    UK Biobank.

  207. 207.

    German National Cohort (GNC) Consortium. The German National Cohort: aims, study design and organization. Eur J Epidemiol. 2014;29(5):371–82.

    Article  Google Scholar 

  208. 208.

    Boyd S. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found Trends Mach Learn. 2010;3(1):1–122.

    Google Scholar 

  209. 209.

    Lambin P, Leijenaar RTH, Deist TM, Peerlings J, de Jong JCC, van Timmeren J, et al. Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol. 2017;14(12):749–62.

    PubMed  Google Scholar 

  210. 210.

    Clarke LP, Nordstrom RJ, Zhang H, Tandon P, Zhang Y, Redmond G. et al. The Quantitative Imaging Network: NCI’s historical perspective and planned goals. Transl Oncol. 2014;7(1):1–4.

    PubMed  PubMed Central  Google Scholar 

  211. 211.

    Cheng HT, Koc L, Harmsen J, Shaked T, Chandra T, Aradhye H, Anil R. Wide & deep learning for recommender systems. In: Workshop on Deep Learning for Recommender Systems; 2016. p. 7–10

    Google Scholar 

  212. 212.

    Gonzalez ME, Dinelle K, Vafai N, Heffernan N, McKenzie J, Appel-Cresswell S, et al. Novel spatial analysis method for PET images using 3D moment invariants: applications to Parkinson’s disease. Neuroimage. 2013;68:11–21.

    Article  PubMed  Google Scholar 

  213. 213.

    van Velden FH, Cheebsumon P, Yaqub M, Smit EF, Hoekstra OS, Lammertsma AA, et al. Evaluation of a cumulative SUV-volume histogram method for parameterizing heterogeneous intratumoural FDG uptake in non-small cell lung cancer PET studies. Eur J Nucl Med Mol Imaging. 2011;38:1636–47.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  214. 214.

    Chawla NV, Bowyer KW, Hall LO, Philip Kegelmeyer W. SMOTE: synthetic minority over-sampling technique. J Artif Intell Res. 2002;16:321–57.

    Google Scholar 

  215. 215.

    Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. MedImage Anal. 2017;42:60–88.

    Google Scholar 

  216. 216.

    LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436–44.

    CAS  PubMed  Google Scholar 

  217. 217.

    Teramoto A, Fujita H, Yamamuro O, Tamaki T. Automated detection of pulmonary nodules in PET/CT images: ensemble false-positive reduction using a convolutional neural network technique. Med.Phys. 2016;43:2821–7.

    PubMed  Google Scholar 

  218. 218.

    Hosny, et al. Deep learning for lung cancer prognostication: a retrospective multi-cohort radiomics study. PLoS Med. 2018;15(11):e1002711.

    PubMed  PubMed Central  Google Scholar 

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Isabella Castiglioni declares that she has no conflict of interest. Francesca Gallivanone declares that she has no conflict of interest. Paolo Soda declares that he has no conflict of interest. Michele Avanzo declares that he has no conflict of interest. Joseph Stancanello discloses an interest in Oncoradiomics SA. Marco Aiello declares that he has no conflict of interest. Matteo Interlenghi declares that he has no conflict of interest. Marco Salvatore declares that he has no conflict of interest. This article does not contain any studies with human participants or animals performed by any of the authors.

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Castiglioni, I., Gallivanone, F., Soda, P. et al. AI-based applications in hybrid imaging: how to build smart and truly multi-parametric decision models for radiomics. Eur J Nucl Med Mol Imaging 46, 2673–2699 (2019).

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  • Radiomics
  • Artificial intelligence
  • Decision models
  • Hybrid imaging
  • PET/CT