Advertisement

Current Oncology Reports

, 21:70 | Cite as

Radiomics: an Introductory Guide to What It May Foretell

  • Stephanie NougaretEmail author
  • Hichem Tibermacine
  • Marion Tardieu
  • Evis Sala
Gynecologic Cancers (NS Reed, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Gynecologic Cancers

Abstract

Purpose of Review

To briefly review the radiomics concept, its applications, and challenges in oncology in the era of precision medicine.

Recent Findings

Over the last 5 years, more than 500 studies have evaluated the role of radiomics to predict tumor diagnosis, genetic pattern, tumor response to therapy, and survival in multiple cancers. This new post-processing method is aimed at extracting multiple quantitative features from the image and converting them into mineable data.

Summary

Radiomics models developed have shown promising results and may play a role in the near future in the daily patient management especially to assess tumor heterogeneity acting as a whole tumor virtual biopsy. For now, radiomics is limited by its lack of standardization; future challenges will be to provide robust and reproducible metrics extracted from large multicenter databases.

Keywords

Cancer Radiomics Texture MRI CT PET/CT 

Notes

Compliance With Ethical Standards

Conflict of Interest

The authors declare they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

References

Papers of particular interest, published recently, have been highlighted as: •• Of major importance

  1. 1.
    Gillies RJ, Kinahan PE, Hricak H. Radiomics: images are more than pictures, they are data. Radiology. 2016;278(2):563–77.PubMedGoogle Scholar
  2. 2.
    Nougaret S, Tardieu M, Vargas HA, Reinhold C, Vande Perre S, Bonanno N, et al. Ovarian cancer: an update on imaging in the era of radiomics. Diagn Interv Imaging. 2018.  https://doi.org/10.1016/j.diii.2018.11.007.
  3. 3.
    Hillman RT, Chisholm GB, Lu KH, Futreal PA. Genomic rearrangement signatures and clinical outcomes in high-grade serous ovarian cancer. J Natl Cancer Inst. 2018;110(3).  https://doi.org/10.1093/jnci/djx176.PubMedCentralGoogle Scholar
  4. 4.
    Bruning A, Mylonas I. New emerging drugs targeting the genomic integrity and replication machinery in ovarian cancer. Arch Gynecol Obstet. 2011;283(5):1087–96.PubMedGoogle Scholar
  5. 5.
    Hu T, Wang S, Huang L, Wang J, Shi D, Li Y, et al. A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules. Eur Radiol. 2019;29(1):439–49.  https://doi.org/10.1007/s00330-018-5539-3.PubMedGoogle Scholar
  6. 6.
    Ortiz-Ramon R, Larroza A, Ruiz-Espana S, Arana E, Moratal D. Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol. 2018;28:4514–23.PubMedGoogle Scholar
  7. 7.
    She Y, Zhang L, Zhu H, Dai C, Xie D, Xie H, et al. The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol. 2018;28(12):5121–28.  https://doi.org/10.1007/s00330-018-5509-9.PubMedGoogle Scholar
  8. 8.
    Tan X, Ma Z, Yan L, Ye W, Liu Z, Liang C. Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol. 2019;29(1):392–400.  https://doi.org/10.1007/s00330-018-5581-1.PubMedGoogle Scholar
  9. 9.
    Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD. Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer. Eur Radiol. 2017;27(10):4082–90.PubMedGoogle Scholar
  10. 10.
    Kickingereder P, Bonekamp D, Nowosielski M, Kratz A, Sill M, Burth S, et al. Radiogenomics of glioblastoma: machine learning-based classification of molecular characteristics by using multiparametric and multiregional MR imaging features. Radiology. 2016;281(3):907–18.PubMedGoogle Scholar
  11. 11.
    Kuo MD, Jamshidi N. Behind the numbers: decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations. Radiology. 2014;270(2):320–5.PubMedGoogle Scholar
  12. 12.
    Pinker K, Chin J, Melsaether AN, Morris EA, Moy L. Precision medicine and radiogenomics in breast cancer: new approaches toward diagnosis and treatment. Radiology. 2018;287(3):732–47.PubMedGoogle Scholar
  13. 13.
    Woodard GA, Ray KM, Joe BN, Price ER. Qualitative radiogenomics: association between oncotype DX test recurrence score and BI-RADS mammographic and breast MR imaging features. Radiology. 2018;286(1):60–70.PubMedGoogle Scholar
  14. 14.
    Zhou M, Leung A, Echegaray S, Gentles A, Shrager JB, Jensen KC, et al. Non-small cell lung cancer radiogenomics map identifies relationships between molecular and imaging phenotypes with prognostic implications. Radiology. 2018;286(1):307–15.PubMedGoogle Scholar
  15. 15.
    Feng Z, Rong P, Cao P, Zhou Q, Zhu W, Yan Z, et al. Machine learning-based quantitative texture analysis of CT images of small renal masses: differentiation of angiomyolipoma without visible fat from renal cell carcinoma. Eur Radiol. 2018;28(4):1625–33.PubMedGoogle Scholar
  16. 16.
    Giganti F, Antunes S, Salerno A, Ambrosi A, Marra P, Nicoletti R, et al. Gastric cancer: texture analysis from multidetector computed tomography as a potential preoperative prognostic biomarker. Eur Radiol. 2017;27(5):1831–9.PubMedGoogle Scholar
  17. 17.
    Kim BR, Kim JH, Ahn SJ, Joo I, Choi SY, Park SJ, et al. CT prediction of resectability and prognosis in patients with pancreatic ductal adenocarcinoma after neoadjuvant treatment using image findings and texture analysis. Eur Radiol. 2019;29(1):362–372.  https://doi.org/10.1007/s00330-018-5574-0 PubMedGoogle Scholar
  18. 18.
    Lakhman Y, Veeraraghavan H, Chaim J, Feier D, Goldman DA, Moskowitz CS, et al. Differentiation of uterine leiomyosarcoma from atypical leiomyoma: diagnostic accuracy of qualitative MR imaging features and feasibility of texture analysis. Eur Radiol. 2017;27(7):2903–15.PubMedGoogle Scholar
  19. 19.
    Lisson CS, Lisson CG, Flosdorf K, Mayer-Steinacker R, Schultheiss M, von Baer A, et al. Diagnostic value of MRI-based 3D texture analysis for tissue characterisation and discrimination of low-grade chondrosarcoma from enchondroma: a pilot study. Eur Radiol. 2018;28(2):468–77.PubMedGoogle Scholar
  20. 20.
    Liu S, Liu S, Ji C, Zheng H, Pan X, Zhang Y, et al. Application of CT texture analysis in predicting histopathological characteristics of gastric cancers. Eur Radiol. 2017;27(12):4951–9.PubMedGoogle Scholar
  21. 21.
    Shen Q, Shan Y, Hu Z, Chen W, Yang B, Han J, et al. Quantitative parameters of CT texture analysis as potential markersfor early prediction of spontaneous intracranial hemorrhage enlargement. Eur Radiol. 2018;28(10):4389–96.  https://doi.org/10.1007/s00330-018-5364-8.PubMedGoogle Scholar
  22. 22.
    Wibmer A, Hricak H, Gondo T, Matsumoto K, Veeraraghavan H, Fehr D, et al. Haralick texture analysis of prostate MRI: utility for differentiating non-cancerous prostate from prostate cancer and differentiating prostate cancers with different Gleason scores. Eur Radiol. 2015;25(10):2840–50.PubMedPubMedCentralGoogle Scholar
  23. 23.
    Hodgdon T, McInnes MD, Schieda N, Flood TA, Lamb L, Thornhill RE. Can quantitative CT texture analysis be used to differentiate fat-poor renal angiomyolipoma from renal cell carcinoma on unenhanced CT images? Radiology. 2015;276(3):787–96.PubMedGoogle Scholar
  24. 24.
    Imbriaco M, Cuocolo R. Does texture analysis of MR images of breast tumors help predict response to treatment? Radiology. 2018;286(2):421–3.PubMedGoogle Scholar
  25. 25.
    Miles KA, Ganeshan B, Griffiths MR, Young RC, Chatwin CR. Colorectal cancer: texture analysis of portal phase hepatic CT images as a potential marker of survival. Radiology. 2009;250(2):444–52.PubMedGoogle Scholar
  26. 26.
    Ng F, Ganeshan B, Kozarski R, Miles KA, Goh V. Assessment of primary colorectal cancer heterogeneity by using whole-tumor texture analysis: contrast-enhanced CT texture as a biomarker of 5-year survival. Radiology. 2013;266(1):177–84.PubMedGoogle Scholar
  27. 27.
    Ueno Y, Forghani B, Forghani R, Dohan A, Zeng XZ, Chamming's F, et al. Endometrial carcinoma: MR imaging-based texture model for preoperative risk stratification-a preliminary analysis. Radiology. 2017;284(3):748–57.PubMedGoogle Scholar
  28. 28.
    Kjaer L, Ring P, Thomsen C, Henriksen O. Texture analysis in quantitative MR imaging. Tissue characterisation of normal brain and intracranial tumours at 1.5 T. Acta Radiol. 1995;36(2):127–35.PubMedGoogle Scholar
  29. 29.
    Skogen K, Schulz A, Helseth E, Ganeshan B, Dormagen JB, Server A. Texture analysis on diffusion tensor imaging: discriminating glioblastoma from single brain metastasis. Acta Radiol. 2018;60(3):356–66.  https://doi.org/10.1177/0284185118780889.PubMedGoogle Scholar
  30. 30.
    Li Z, Mao Y, Li H, Yu G, Wan H, Li B. Differentiating brain metastases from different pathological types of lung cancers using texture analysis of T1 postcontrast MR. Magn Reson Med. 2016;76(5):1410–9.PubMedGoogle Scholar
  31. 31.
    Haider MA, Vosough A, Khalvati F, Kiss A, Ganeshan B, Bjarnason GA. CT texture analysis: a potential tool for prediction of survival in patients with metastatic clear cell carcinoma treated with sunitinib. Cancer Imaging. 2017;17(1):4.PubMedPubMedCentralGoogle Scholar
  32. 32.
    Scrima AT, Lubner MG, Abel EJ, Havighurst TC, Shapiro DD, Huang W, et al. Texture analysis of small renal cell carcinomas at MDCT for predicting relevant histologic and protein biomarkers. Abdom Radiol (NY). 2018;44(6):1999–2008.  https://doi.org/10.1007/s00261-018-1649-2.Google Scholar
  33. 33.
    Horvat N, Veeraraghavan H, Khan M, Blazic I, Zheng J, Capanu M, et al. MR imaging of rectal cancer: radiomics analysis to assess treatment response after neoadjuvant therapy. Radiology. 2018;287(3):833–43.PubMedPubMedCentralGoogle Scholar
  34. 34.
    Davnall F, Yip CS, Ljungqvist G, Selmi M, Ng F, Sanghera B, et al. Assessment of tumor heterogeneity: an emerging imaging tool for clinical practice? Insights Imaging. 2012;3(6):573–89.PubMedPubMedCentralGoogle Scholar
  35. 35.
    Ganeshan B, Miles KA. Quantifying tumour heterogeneity with CT. Cancer Imaging. 2013;13:140–9.PubMedPubMedCentralGoogle Scholar
  36. 36.
    Zhao B, Tan Y, Tsai WY, Qi J, Xie C, Lu L, et al. Reproducibility of radiomics for deciphering tumor phenotype with imaging. Sci Rep. 2016;6:23428.PubMedPubMedCentralGoogle Scholar
  37. 37.
    Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ. CT texture analysis: definitions, applications, biologic correlates, and challenges. Radiographics. 2017;37(5):1483–503.PubMedGoogle Scholar
  38. 38.
    •• Vallieres M, Zwanenburg A, Badic B, Cheze Le Rest C, Visvikis D, Hatt M. Responsible radiomics research for faster clinical translation. J Nucl Med. 2018;59(2):189–93 Paper calling for a need in radiomics standardization. PubMedPubMedCentralGoogle Scholar
  39. 39.
    Tibshirani R. The lasso method for variable selection in the Cox model. Stat Med. 1997;16(4):385–95.PubMedGoogle Scholar
  40. 40.
    Mwangi B, Tian TS, Soares JC. A review of feature reduction techniques in neuroimaging. Neuroinformatics. 2014;12(2):229–44.PubMedPubMedCentralGoogle Scholar
  41. 41.
    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.PubMedPubMedCentralGoogle Scholar
  42. 42.
    Lian C, Ruan S, Denoeux T, Jardin F, Vera P. Selecting radiomic features from FDG-PET images for cancer treatment outcome prediction. Med Image Anal. 2016;32:257–68.PubMedGoogle Scholar
  43. 43.
    Cameron A, Khalvati F, Haider MA, Wong A. MAPS: a quantitative radiomics approach for prostate cancer detection. IEEE Trans Biomed Eng. 2016;63(6):1145–56.PubMedGoogle Scholar
  44. 44.
    Hu P, Wang J, Zhong H, Zhou Z, Shen L, Hu W, et al. Reproducibility with repeat CT in radiomics study for rectal cancer. Oncotarget. 2016;7(44):71440–6.PubMedPubMedCentralGoogle Scholar
  45. 45.
    Lee G, Lee HY, Park H, Schiebler ML, van Beek EJR, Ohno Y, et al. Radiomics and its emerging role in lung cancer research, imaging biomarkers and clinical management: state of the art. Eur J Radiol. 2017;86:297–307.PubMedGoogle Scholar
  46. 46.
    Li H, Zhu Y, Burnside ES, Huang E, Drukker K, Hoadley KA, 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.  https://doi.org/10.1038/npjbcancer.2016.12.
  47. 47.
    Liang C, Huang Y, He L, Chen X, Ma Z, Dong D, 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. 2016;7(21):31401–12.PubMedPubMedCentralGoogle Scholar
  48. 48.
    Ingrisch M, Schneider MJ, Norenberg D, Negrao de Figueiredo G, Maier-Hein K, Suchorska B, et al. Radiomic analysis reveals prognostic information in T1-weighted baseline magnetic resonance imaging in patients with glioblastoma. Investig Radiol. 2017;52(6):360–6.Google Scholar
  49. 49.
    Kickingereder P, Burth S, Wick A, Gotz M, Eidel O, Schlemmer HP, 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):880–9.PubMedGoogle Scholar
  50. 50.
    Bae S, Choi YS, Ahn SS, Chang JH, Kang SG, Kim EH, et al. Radiomic MRI phenotyping of glioblastoma: improving survival prediction. Radiology. 2018;289(3):797–806.  https://doi.org/10.1148/radiol.2018180200.PubMedGoogle Scholar
  51. 51.
    Prasanna P, Patel J, Partovi S, Madabhushi A, Tiwari P. Radiomic features from the peritumoral brain parenchyma on treatment-naive multi-parametric MR imaging predict long versus short-term survival in glioblastoma multiforme: preliminary findings. Eur Radiol. 2017;27(10):4188–97.PubMedGoogle Scholar
  52. 52.
    Ren J, Tian J, Yuan Y, Dong D, Li X, Shi Y, et al. Magnetic resonance imaging based radiomics signature for the preoperative discrimination of stage I-II and III-IV head and neck squamous cell carcinoma. Eur J Radiol. 2018;106:1–6.PubMedGoogle Scholar
  53. 53.
    Ouyang FS, Guo BL, Zhang B, Dong YH, Zhang L, Mo XK, et al. Exploration and validation of radiomics signature as an independent prognostic biomarker in stage III-IVb nasopharyngeal carcinoma. Oncotarget. 2017;8(43):74869–79.PubMedPubMedCentralGoogle Scholar
  54. 54.
    Wang G, He L, Yuan C, Huang Y, Liu Z, Liang C. Pretreatment MR imaging radiomics signatures for response prediction to induction chemotherapy in patients with nasopharyngeal carcinoma. Eur J Radiol. 2018;98:100–6.PubMedGoogle Scholar
  55. 55.
    Zhang B, Ouyang F, Gu D, Dong Y, Zhang L, Mo X, et al. Advanced nasopharyngeal carcinoma: pre-treatment prediction of progression based on multi-parametric MRI radiomics. Oncotarget. 2017;8(42):72457–65.PubMedPubMedCentralGoogle Scholar
  56. 56.
    Rusu M, Rajiah P, Gilkeson R, Yang M, Donatelli C, Thawani R, et al. Co-registration of pre-operative CT with ex vivo surgically excised ground glass nodules to define spatial extent of invasive adenocarcinoma on in vivo imaging: a proof-of-concept study. Eur Radiol. 2017;27(10):4209–17.PubMedPubMedCentralGoogle Scholar
  57. 57.
    Si MJ, Tao XF, Du GY, Cai LL, Han HX, Liang XZ, et al. Thin-section computed tomography-histopathologic comparisons of pulmonary focal interstitial fibrosis, atypical adenomatous hyperplasia, adenocarcinoma in situ, and minimally invasive adenocarcinoma with pure ground-glass opacity. Eur J Radiol. 2016;85(10):1708–15.PubMedGoogle Scholar
  58. 58.
    Tunali I, Stringfield O, Guvenis A, Wang H, Liu Y, Balagurunathan Y, et al. Radial gradient and radial deviation radiomic features from pre-surgical CT scans are associated with survival among lung adenocarcinoma patients. Oncotarget. 2017;8(56):96013–26.PubMedPubMedCentralGoogle Scholar
  59. 59.
    Yang SM, Chen LW, Wang HJ, Chen LR, Lor KL, Chen YC, et al. Extraction of radiomic values from lung adenocarcinoma with near-pure subtypes in the International Association for the Study of Lung Cancer/the American Thoracic Society/the European Respiratory Society (IASLC/ATS/ERS) classification. Lung Cancer. 2018;119:56–63.PubMedGoogle Scholar
  60. 60.
    Yuan M, Zhang YD, Pu XH, Zhong Y, Li H, Wu JF, et al. Comparison of a radiomic biomarker with volumetric analysis for decoding tumour phenotypes of lung adenocarcinoma with different disease-specific survival. Eur Radiol. 2017;27(11):4857–65.PubMedGoogle Scholar
  61. 61.
    Rizzo S, Petrella F, Buscarino V, De Maria F, Raimondi S, Barberis M, et al. CT Radiogenomic characterization of EGFR, K-RAS, and ALK mutations in non-small cell lung cancer. Eur Radiol. 2016;26(1):32–42.PubMedGoogle Scholar
  62. 62.
    Halpenny DF, Plodkowski A, Riely G, Zheng J, Litvak A, Moscowitz C, et al. Radiogenomic evaluation of lung cancer - are there imaging characteristics associated with lung adenocarcinomas harboring BRAF mutations? Clin Imaging. 2017;42:147–51.PubMedGoogle Scholar
  63. 63.
    Bakr S, Gevaert O, Echegaray S, Ayers K, Zhou M, Shafiq M, et al. A radiogenomic dataset of non-small cell lung cancer. Sci Data. 2018;5:180202.PubMedPubMedCentralGoogle Scholar
  64. 64.
    Soufi M, Arimura H, Nagami N. Identification of optimal mother wavelets in survival prediction of lung cancer patients using wavelet decomposition-based radiomic features. Med Phys. 2018;45(11):5116–28.PubMedGoogle Scholar
  65. 65.
    Virginia BM, Laura F, Silvia R, Roberto F, Francesco F, Eva H, et al. Prognostic value of histogram analysis in advanced non-small cell lung cancer: a radiomic study. Oncotarget. 2018;9(2):1906–14.PubMedGoogle Scholar
  66. 66.
    Zhou H, Dong D, Chen B, Fang M, Cheng Y, Gan Y, et al. Diagnosis of distant metastasis of lung cancer: based on clinical and radiomic features. Transl Oncol. 2018;11(1):31–6.PubMedGoogle Scholar
  67. 67.
    Kontos D, Winham SJ, Oustimov A, Pantalone L, Hsieh MK, Gastounioti A, et al. Radiomic phenotypes of mammographic parenchymal complexity: toward augmenting breast density in breast cancer risk assessment. Radiology. 2019;290(1):41–49.  https://doi.org/10.1148/radiol.2018180179.PubMedGoogle Scholar
  68. 68.
    Fan M, Wu G, Cheng H, Zhang J, Shao G, Li L. Radiomic analysis of DCE-MRI for prediction of response to neoadjuvant chemotherapy in breast cancer patients. Eur J Radiol. 2017;94:140–7.PubMedGoogle Scholar
  69. 69.
    Sofic A, Husic-Selimovic A, Carovac A, Jahic E, Smailbegovic V, Kupusovic J. The significance of MRI evaluation of the uterine junctional zone in the early diagnosis of adenomyosis. Acta Inform Med. 2016;24(2):103–6.PubMedPubMedCentralGoogle Scholar
  70. 70.
    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.PubMedGoogle Scholar
  71. 71.
    Fehr D, Veeraraghavan H, Wibmer A, Gondo T, Matsumoto K, Vargas HA, et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A. 2015;112(46):E6265–73.PubMedPubMedCentralGoogle Scholar
  72. 72.
    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(9):782–91.PubMedGoogle Scholar
  73. 73.
    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(2):155–62.PubMedPubMedCentralGoogle Scholar
  74. 74.
    Klaassen R, Larue R, Mearadji B, van der Woude SO, Stoker J, Lambin P, et al. Feasibility of CT radiomics to predict treatment response of individual liver metastases in esophagogastric cancer patients. PLoS One. 2018;13(11):e0207362.PubMedPubMedCentralGoogle Scholar
  75. 75.
    Hu HT, Wang Z, Huang XW, Chen SL, Zheng X, Ruan SM, et al. Ultrasound-based radiomics score: a potential biomarker for the prediction of microvascular invasion in hepatocellular carcinoma. Eur Radiol. 2018;29(6):2890–2901.  https://doi.org/10.1007/s00330-018-5797-0.PubMedGoogle Scholar
  76. 76.
    Peng J, Zhang J, Zhang Q, Xu Y, Zhou J, Liu L. A radiomics nomogram for preoperative prediction of microvascular invasion risk in hepatitis B virus-related hepatocellular carcinoma. Diagn Interv Radiol. 2018;24(3):121–7.PubMedPubMedCentralGoogle Scholar
  77. 77.
    Wu M, Tan H, Gao F, Hai J, Ning P, Chen J, et al. Predicting the grade of hepatocellular carcinoma based on non-contrast-enhanced MRI radiomics signature. Eur Radiol. 2018;29(6):2802–11.  https://doi.org/10.1007/s00330-018-5787-2.PubMedGoogle Scholar
  78. 78.
    Chakraborty J, Midya A, Gazit L, Attiyeh M, Langdon-Embry L, Allen PJ, et al. CT radiomics to predict high-risk intraductal papillary mucinous neoplasms of the pancreas. Med Phys. 2018;45(11):5019–29.PubMedGoogle Scholar
  79. 79.
    Badic B, Desseroit MC, Hatt M, Visvikis D. Potential complementary value of noncontrast and contrast enhanced CT radiomics in colorectal cancers. Acad Radiol. 2018;26(4):469–79.  https://doi.org/10.1016/j.acra.2018.06.004.PubMedGoogle Scholar
  80. 80.
    Huang YQ, Liang CH, He L, Tian J, Liang CS, Chen X, et al. Development and validation of a radiomics nomogram for preoperative prediction of lymph node metastasis in colorectal cancer. J Clin Oncol. 2016;34(18):2157–64.PubMedGoogle Scholar
  81. 81.
    Cui Y, Yang X, Shi Z, Yang Z, Du X, Zhao Z, et al. Radiomics analysis of multiparametric MRI for prediction of pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer. Eur Radiol. 2018;29(3):1211–20.  https://doi.org/10.1007/s00330-018-5683-9.PubMedGoogle Scholar
  82. 82.
    Meng J, Liu S, Zhu L, Zhu L, Wang H, Xie L, et al. Texture analysis as imaging biomarker for recurrence in advanced cervical cancer treated with CCRT. Sci Rep. 2018;8(1):11399.PubMedPubMedCentralGoogle Scholar
  83. 83.
    Meng J, Zhu L, Zhu L, Xie L, Wang H, Liu S, et al. Whole-lesion ADC histogram and texture analysis in predicting recurrence of cervical cancer treated with CCRT. Oncotarget. 2017;8(54):92442–53.PubMedPubMedCentralGoogle Scholar
  84. 84.
    Ross JS, Ali SM, Wang K, Palmer G, Yelensky R, Lipson D, et al. Comprehensive genomic profiling of epithelial ovarian cancer by next generation sequencing-based diagnostic assay reveals new routes to targeted therapies. Gynecol Oncol. 2013;130(3):554–9.PubMedGoogle Scholar
  85. 85.
    Wallbillich JJ, Forde B, Havrilesky LJ, Cohn DE. A personalized paradigm in the treatment of platinum-resistant ovarian cancer - a cost utility analysis of genomic-based versus cytotoxic therapy. Gynecol Oncol. 2016;142(1):144–9.PubMedGoogle Scholar
  86. 86.
    Lee JY, Kim HS, Suh DH, Kim MK, Chung HH, Song YS. Ovarian cancer biomarker discovery based on genomic approaches. J Cancer Prev. 2013;18(4):298–312.PubMedPubMedCentralGoogle Scholar
  87. 87.
    Gorringe KL, George J, Anglesio MS, Ramakrishna M, Etemadmoghadam D, Cowin P, et al. Copy number analysis identifies novel interactions between genomic loci in ovarian cancer. PLoS One. 2010;5(9).  https://doi.org/10.1371/journal.pone.0011408.PubMedPubMedCentralGoogle Scholar
  88. 88.
    Konecny GE, Winterhoff B, Wang C. Gene-expression signatures in ovarian cancer: promise and challenges for patient stratification. Gynecol Oncol. 2016;141(2):379–85.PubMedGoogle Scholar
  89. 89.
    Skubitz AP, Pambuccian SE, Argenta PA, Skubitz KM. Differential gene expression identifies subgroups of ovarian carcinoma. Transl Res. 2006;148(5):223–48.PubMedGoogle Scholar
  90. 90.
    Stanescu AD, Ples L, Edu A, Olaru GO, Comanescu AC, Poteca AG, et al. Different patterns of heterogeneity in ovarian carcinoma. Romanian J Morphol Embryol. 2015;56(4):1357–63.Google Scholar
  91. 91.
    Nymoen DA, Hetland Falkenthal TE, Holth A, Ow GS, Ivshina AV, Trope CG, et al. Expression and clinical role of chemoresponse-associated genes in ovarian serous carcinoma. Gynecol Oncol. 2015;139(1):30–9.PubMedGoogle Scholar
  92. 92.
    Zangwill BC, Balsara G, Dunton C, Varello M, Rebane BA, Hernandez E, et al. Ovarian carcinoma heterogeneity as demonstrated by DNA ploidy. Cancer. 1993;71(7):2261–7.PubMedGoogle Scholar
  93. 93.
    Mota A, Trivino JC, Rojo-Sebastian A, Martinez-Ramirez A, Chiva L, Gonzalez-Martin A, et al. Intra-tumor heterogeneity in TP53 null high grade serous ovarian carcinoma progression. BMC Cancer. 2015;15:940.PubMedPubMedCentralGoogle Scholar
  94. 94.
    Bashashati A, Ha G, Tone A, Ding J, Prentice LM, Roth A, et al. Distinct evolutionary trajectories of primary high-grade serous ovarian cancers revealed through spatial mutational profiling. J Pathol. 2013;231(1):21–34.PubMedPubMedCentralGoogle Scholar
  95. 95.
    De Mattos-Arruda L, Weigelt B, Cortes J, Won HH, Ng CK, Nuciforo P, et al. Capturing intra-tumor genetic heterogeneity by de novo mutation profiling of circulating cell-free tumor DNA: a proof-of-principle. Ann Oncol. 2014;25(9):1729–35.PubMedPubMedCentralGoogle Scholar
  96. 96.
    •• Vargas HA, Veeraraghavan H, Micco M, Nougaret S, Lakhman Y, Meier AA, et al. A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. Eur Radiol. 2017;27(9):3991–4001 Study evaluating ovarian cancer heterogeneity and showing that inter-site disssimilarities were linked with poorer outcome. PubMedPubMedCentralGoogle Scholar
  97. 97.
    Rizzo S, Botta F, Raimondi S, Origgi D, Buscarino V, Colarieti A, et al. Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months. Eur Radiol. 2018;28:4849–59.PubMedGoogle Scholar
  98. 98.
    Berenguer R, Pastor-Juan MDR, Canales-Vazquez J, Castro-Garcia M, Villas MV, Mansilla Legorburo F, et al. Radiomics of CT features may be nonreproducible and redundant: influence of CT acquisition parameters. Radiology. 2018;288(2):407–15.PubMedGoogle Scholar
  99. 99.
    •• Zwanenburg A, Leger S, Vallières M, Löck S. Initiative for the IBS. Image biomarker standardisation initiative. https://www.arxivorg/abs/161207003. 2018. Paper calling for a need in radiomics standardization.
  100. 100.
    Sanduleanu S, Woodruff HC, de Jong EEC, van Timmeren JE, Jochems A, Dubois L, et al. Tracking tumor biology with radiomics: a systematic review utilizing a radiomics quality score. Radiother Oncol. 2018;127(3):349–60.PubMedGoogle Scholar
  101. 101.
    Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162(1):55–63.PubMedGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Stephanie Nougaret
    • 1
    • 2
    Email author
  • Hichem Tibermacine
    • 1
    • 2
  • Marion Tardieu
    • 1
    • 2
  • Evis Sala
    • 3
  1. 1.Montpellier Cancer Research Institute (IRCM)MontpellierFrance
  2. 2.Department of Radiology, Montpellier Cancer institute, INSERM, U1194University of MontpellierMontpellierFrance
  3. 3.Department of RadiologyBox 218 and Cancer Research UK Cambridge Centre, Cambridge Biomedical CampusCambridgeUK

Personalised recommendations