Skip to main content

Advertisement

Log in

Radiomics and radiogenomics in ovarian cancer: a literature review

  • Special Section: Ovarian Cancer
  • Published:
Abdominal Radiology Aims and scope Submit manuscript

Abstract

Ovarian cancer remains one of the most lethal gynecological cancers in the world despite extensive progress in the areas of chemotherapy and surgery. Many studies have postulated that this is because of the profound heterogeneity that underpins response to therapy and prognosis. Standard imaging evaluation using CT or MRI does not take into account this tumoral heterogeneity especially in advanced stages with peritoneal carcinomatosis. As such, newly emergent fields in the assessment of tumor heterogeneity have been proposed using radiomics to evaluate the whole tumor burden heterogeneity as opposed to single biopsy sampling. This review provides an overview of radiomics, radiogenomics, and proteomics and examines the use of these newly emergent fields in assessing tumor heterogeneity and its implications in ovarian cancer.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Torre LA, Trabert B, DeSantis CE, Miller KD, Samimi G, Runowicz CD, Gaudet MM, Jemal A, Siegel RL (2018) Ovarian cancer statistics, 2018. CA Cancer J Clin 68 (4):284-296. https://doi.org/10.3322/caac.21456

    PubMed  PubMed Central  Google Scholar 

  2. Arend R, Martinez A, Szul T, Birrer MJ (2019) Biomarkers in ovarian cancer: To be or not to be. Cancer 125 Suppl 24:4563-4572. https://doi.org/10.1002/cncr.32595

    CAS  PubMed  Google Scholar 

  3. Fujiwara K, Hasegawa K, Nagao S (2019) Landscape of systemic therapy for ovarian cancer in 2019: Primary therapy. Cancer 125 Suppl 24:4582-4586. https://doi.org/10.1002/cncr.32475

    CAS  PubMed  Google Scholar 

  4. Lee JM, Minasian L, Kohn EC (2019) New strategies in ovarian cancer treatment. Cancer 125 Suppl 24:4623-4629. https://doi.org/10.1002/cncr.32544

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Bogani G, Lopez S, Mantiero M, Ducceschi M, Bosio S, Ruisi S, Sarpietro G, Guerrisi R, Brusadelli C, Dell'Acqua A, Di Donato V, Raspagliesi F (2020) Immunotherapy for platinum-resistant ovarian cancer. Gynecol Oncol. https://doi.org/10.1016/j.ygyno.2020.05.681

  6. Bowtell DD (2010) The genesis and evolution of high-grade serous ovarian cancer. Nat Rev Cancer 10 (11):803-808. https://doi.org/10.1038/nrc2946

    CAS  PubMed  Google Scholar 

  7. Patch AM, Christie EL, Etemadmoghadam D, Garsed DW, George J, Fereday S, Nones K, Cowin P, Alsop K, Bailey PJ, Kassahn KS, Newell F, Quinn MC, Kazakoff S, Quek K, Wilhelm-Benartzi C, Curry E, Leong HS, Australian Ovarian Cancer Study G, Hamilton A, Mileshkin L, Au-Yeung G, Kennedy C, Hung J, Chiew YE, Harnett P, Friedlander M, Quinn M, Pyman J, Cordner S, O'Brien P, Leditschke J, Young G, Strachan K, Waring P, Azar W, Mitchell C, Traficante N, Hendley J, Thorne H, Shackleton M, Miller DK, Arnau GM, Tothill RW, Holloway TP, Semple T, Harliwong I, Nourse C, Nourbakhsh E, Manning S, Idrisoglu S, Bruxner TJ, Christ AN, Poudel B, Holmes O, Anderson M, Leonard C, Lonie A, Hall N, Wood S, Taylor DF, Xu Q, Fink JL, Waddell N, Drapkin R, Stronach E, Gabra H, Brown R, Jewell A, Nagaraj SH, Markham E, Wilson PJ, Ellul J, McNally O, Doyle MA, Vedururu R, Stewart C, Lengyel E, Pearson JV, Waddell N, deFazio A, Grimmond SM, Bowtell DD (2015) Whole-genome characterization of chemoresistant ovarian cancer. Nature 521 (7553):489-494. https://doi.org/10.1038/nature14410

    Google Scholar 

  8. Takaya H, Nakai H, Sakai K, Nishio K, Murakami K, Mandai M, Matsumura N (2020) Intratumor heterogeneity and homologous recombination deficiency of high-grade serous ovarian cancer are associated with prognosis and molecular subtype and change in treatment course. Gynecol Oncol 156 (2):415-422. https://doi.org/10.1016/j.ygyno.2019.11.013

    CAS  PubMed  Google Scholar 

  9. Nelson L, Tighe A, Golder A, Littler S, Bakker B, Moralli D, Murtuza Baker S, Donaldson IJ, Spierings DCJ, Wardenaar R, Neale B, Burghel GJ, Winter-Roach B, Edmondson R, Clamp AR, Jayson GC, Desai S, Green CM, Hayes A, Foijer F, Morgan RD, Taylor SS (2020) A living biobank of ovarian cancer ex vivo models reveals profound mitotic heterogeneity. Nat Commun 11 (1):822. https://doi.org/10.1038/s41467-020-14551-2

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Jimenez-Sanchez A, Cybulska P, Mager KL, Koplev S, Cast O, Couturier DL, Memon D, Selenica P, Nikolovski I, Mazaheri Y, Bykov Y, Geyer FC, Macintyre G, Gavarro LM, Drews RM, Gill MB, Papanastasiou AD, Sosa RE, Soslow RA, Walther T, Shen R, Chi DS, Park KJ, Hollmann T, Reis-Filho JS, Markowetz F, Beltrao P, Vargas HA, Zamarin D, Brenton JD, Snyder A, Weigelt B, Sala E, Miller ML (2020) Unraveling tumor-immune heterogeneity in advanced ovarian cancer uncovers immunogenic effect of chemotherapy. Nat Genet 52 (6):582-593. https://doi.org/10.1038/s41588-020-0630-5

    CAS  PubMed  Google Scholar 

  11. Gao Y, Chen L, Cai G, Xiong X, Wu Y, Ma D, Li SC, Gao Q (2020) Heterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study. Oncoimmunology 9 (1):1760067. https://doi.org/10.1080/2162402X.2020.1760067

    PubMed  PubMed Central  Google Scholar 

  12. Tan TZ, Heong V, Ye J, Lim D, Low J, Choolani M, Scott C, Tan DSP, Huang RY (2019) Decoding transcriptomic intra-tumour heterogeneity to guide personalised medicine in ovarian cancer. J Pathol 247 (3):305-319. https://doi.org/10.1002/path.5191

    CAS  PubMed  Google Scholar 

  13. Roberts CM, Cardenas C, Tedja R (2019) The Role of Intra-Tumoral Heterogeneity and Its Clinical Relevance in Epithelial Ovarian Cancer Recurrence and Metastasis. Cancers (Basel) 11 (8). https://doi.org/10.3390/cancers11081083

  14. Garziera M, Roncato R, Montico M, De Mattia E, Gagno S, Poletto E, Scalone S, Canzonieri V, Giorda G, Sorio R, Cecchin E, Toffoli G (2019) New Challenges in Tumor Mutation Heterogeneity in Advanced Ovarian Cancer by a Targeted Next-Generation Sequencing (NGS) Approach. Cells 8 (6). https://doi.org/10.3390/cells8060584

  15. Lee JY, Yoon JK, Kim B, Kim S, Kim MA, Lim H, Bang D, Song YS (2015) Tumor evolution and intratumor heterogeneity of an epithelial ovarian cancer investigated using next-generation sequencing. BMC Cancer 15:85. https://doi.org/10.1186/s12885-015-1077-4

    PubMed  PubMed Central  Google Scholar 

  16. Tong JG, Valdes YR, Sivapragasam M, Barrett JW, Bell JC, Stojdl D, DiMattia GE, Shepherd TG (2017) Spatial and temporal epithelial ovarian cancer cell heterogeneity impacts Maraba virus oncolytic potential. BMC Cancer 17 (1):594. https://doi.org/10.1186/s12885-017-3600-2

    PubMed  PubMed Central  Google Scholar 

  17. Subramanian DN, Zethoven M, McInerny S, Morgan JA, Rowley SM, Lee JEA, Li N, Gorringe KL, James PA, Campbell IG (2020) Exome sequencing of familial high-grade serous ovarian carcinoma reveals heterogeneity for rare candidate susceptibility genes. Nat Commun 11 (1):1640. https://doi.org/10.1038/s41467-020-15461-z

    PubMed  PubMed Central  Google Scholar 

  18. Masoodi T, Siraj S, Siraj AK, Azam S, Qadri Z, Parvathareddy SK, Tulbah A, Al-Dayel F, AlHusaini H, AlOmar O, Al-Badawi IA, Alkuraya FS, Al-Kuraya KS (2020) Genetic heterogeneity and evolutionary history of high-grade ovarian carcinoma and matched distant metastases. Br J Cancer 122 (8):1219-1230. https://doi.org/10.1038/s41416-020-0763-4

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Cooke SL, Ng CK, Melnyk N, Garcia MJ, Hardcastle T, Temple J, Langdon S, Huntsman D, Brenton JD (2010) Genomic analysis of genetic heterogeneity and evolution in high-grade serous ovarian carcinoma. Oncogene 29 (35):4905-4913. https://doi.org/10.1038/onc.2010.245

    CAS  PubMed  PubMed Central  Google Scholar 

  20. Shah RH, Scott SN, Brannon AR, Levine DA, Lin O, Berger MF (2015) Comprehensive mutation profiling by next-generation sequencing of effusion fluids from patients with high-grade serous ovarian carcinoma. Cancer Cytopathol 123 (5):289-297. https://doi.org/10.1002/cncy.21522

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Fumagalli C, Rappa A, Casadio C, Betella I, Colombo N, Barberis M, Guerini-Rocco E (2020) Next-generation sequencing-based BRCA testing on cytological specimens from ovarian cancer ascites reveals high concordance with tumour tissue analysis. J Clin Pathol 73 (3):168-171. https://doi.org/10.1136/jclinpath-2019-206127

    CAS  PubMed  Google Scholar 

  22. Mandilaras V, Garg S, Cabanero M, Tan Q, Pastrello C, Burnier J, Karakasis K, Wang L, Dhani NC, Butler MO, Bedard PL, Siu LL, Clarke B, Shaw PA, Stockley T, Jurisica I, Oza AM, Lheureux S (2019) TP53 mutations in high grade serous ovarian cancer and impact on clinical outcomes: a comparison of next generation sequencing and bioinformatics analyses. Int J Gynecol Cancer. https://doi.org/10.1136/ijgc-2018-000087

  23. Ross JS, Ali SM, Wang K, Palmer G, Yelensky R, Lipson D, Miller VA, Zajchowski D, Shawver LK, Stephens PJ (2013) Comprehensive genomic profiling of epithelial ovarian cancer by next generation sequencing-based diagnostic assay reveals new routes to targeted therapies. Gynecol Oncol 130 (3):554-559. https://doi.org/10.1016/j.ygyno.2013.06.019

    CAS  PubMed  Google Scholar 

  24. Rizzo S, De Piano F, Buscarino V, Pagan E, Bagnardi V, Zanagnolo V, Colombo N, Maggioni A, Del Grande M, Del Grande F, Bellomi M, Aletti G (2020) Pre-operative evaluation of epithelial ovarian cancer patients: Role of whole body diffusion weighted imaging MR and CT scans in the selection of patients suitable for primary debulking surgery. A single-centre study. Eur J Radiol 123:108786. https://doi.org/10.1016/j.ejrad.2019.108786

    PubMed  Google Scholar 

  25. Li M, Tan J, Zhang Y, Ai C, Wang H, Zhang H, Jin Y, Chen Y (2020) Assessing CT imaging features combined with CEA and CA125 levels to identify endometriosis-associated ovarian cancer. Abdom Radiol (NY). https://doi.org/10.1007/s00261-020-02571-x

  26. Silverman PM, Osborne M, Dunnick NR, Bandy LC (1988) CT prior to second-look operation in ovarian cancer. AJR Am J Roentgenol 150 (4):829-832. https://doi.org/10.2214/ajr.150.4.829

    CAS  PubMed  Google Scholar 

  27. Togashi K (2003) Ovarian cancer: the clinical role of US, CT, and MRI. Eur Radiol 13 Suppl 4:L87-104. https://doi.org/10.1007/s00330-003-1964-y

    PubMed  Google Scholar 

  28. Burger IA, Goldman DA, Vargas HA, Kattan MW, Yu C, Kou L, Andikyan V, Chi DS, Hricak H, Sala E (2015) Incorporation of postoperative CT data into clinical models to predict 5-year overall and recurrence free survival after primary cytoreductive surgery for advanced ovarian cancer. Gynecol Oncol 138 (3):554-559. https://doi.org/10.1016/j.ygyno.2015.06.010

    PubMed  PubMed Central  Google Scholar 

  29. Forstner R, Hricak H, Occhipinti KA, Powell CB, Frankel SD, Stern JL (1995) Ovarian cancer: staging with CT and MR imaging. Radiology 197 (3):619-626. https://doi.org/10.1148/radiology.197.3.7480729

    CAS  PubMed  Google Scholar 

  30. Fultz PJ, Jacobs CV, Hall WJ, Gottlieb R, Rubens D, Totterman SM, Meyers S, Angel C, Del Priore G, Warshal DP, Zou KH, Shapiro DE (1999) Ovarian cancer: comparison of observer performance for four methods of interpreting CT scans. Radiology 212 (2):401-410. https://doi.org/10.1148/radiology.212.2.r99au19401

    CAS  PubMed  Google Scholar 

  31. Kurtz AB, Tsimikas JV, Tempany CM, Hamper UM, Arger PH, Bree RL, Wechsler RJ, Francis IR, Kuhlman JE, Siegelman ES, Mitchell DG, Silverman SG, Brown DL, Sheth S, Coleman BG, Ellis JH, Kurman RJ, Caudry DJ, McNeil BJ (1999) Diagnosis and staging of ovarian cancer: comparative values of Doppler and conventional US, CT, and MR imaging correlated with surgery and histopathologic analysis--report of the Radiology Diagnostic Oncology Group. Radiology 212 (1):19-27. https://doi.org/10.1148/radiology.212.1.r99jl3619

    CAS  PubMed  Google Scholar 

  32. Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, Zegers CM, Gillies R, Boellard R, Dekker A, Aerts HJ (2012) Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 48 (4):441-446. https://doi.org/10.1016/j.ejca.2011.11.036

    PubMed  PubMed Central  Google Scholar 

  33. Rizzo S, Botta F, Raimondi S, Origgi D, Buscarino V, Colarieti A, Tomao F, Aletti G, Zanagnolo V, Del Grande M, Colombo N, Bellomi M (2018) Radiomics of high-grade serous ovarian cancer: association between quantitative CT features, residual tumour and disease progression within 12 months. Eur Radiol 28 (11):4849-4859. https://doi.org/10.1007/s00330-018-5389-z

    PubMed  Google Scholar 

  34. Kumar V, Gu Y, Basu S, Berglund A, Eschrich SA, Schabath MB, Forster K, Aerts HJ, Dekker A, Fenstermacher D, Goldgof DB, Hall LO, Lambin P, Balagurunathan Y, Gatenby RA, Gillies RJ (2012) Radiomics: the process and the challenges. Magn Reson Imaging 30 (9):1234-1248. https://doi.org/10.1016/j.mri.2012.06.010

    PubMed  PubMed Central  Google Scholar 

  35. Wei W, Rong Y, Liu Z, Zhou B, Tang Z, Wang S, Dong D, Zang Y, Guo Y, Tian J (2018) Radiomics: a Novel CT-Based Method of Predicting Postoperative Recurrence in Ovarian Cancer. Conf Proc IEEE Eng Med Biol Soc 2018:4130-4133. https://doi.org/10.1109/EMBC.2018.8513351

    Google Scholar 

  36. Nougaret S, Tardieu M, Vargas HA, Reinhold C, Vande Perre S, Bonanno N, Sala E, Thomassin-Naggara I (2019) Ovarian cancer: An update on imaging in the era of radiomics. Diagn Interv Imaging 100 (10):647-655. https://doi.org/10.1016/j.diii.2018.11.007

    CAS  PubMed  Google Scholar 

  37. Lubner MG (2019) Reflections on radiogenomics and oncologic radiomics. Abdom Radiol (NY) 44 (6):1959. https://doi.org/10.1007/s00261-019-02047-7

    Google Scholar 

  38. Taghavi M, Trebeschi S, Simoes R, Meek DB, Beckers RCJ, Lambregts DMJ, Verhoef C, Houwers JB, van der Heide UA, Beets-Tan RGH, Maas M (2020) Machine learning-based analysis of CT radiomics model for prediction of colorectal metachronous liver metastases. Abdom Radiol (NY). https://doi.org/10.1007/s00261-020-02624-1

  39. van Griethuysen JJM, Lambregts DMJ, Trebeschi S, Lahaye MJ, Bakers FCH, Vliegen RFA, Beets GL, Aerts H, Beets-Tan RGH (2020) Radiomics performs comparable to morphologic assessment by expert radiologists for prediction of response to neoadjuvant chemoradiotherapy on baseline staging MRI in rectal cancer. Abdom Radiol (NY) 45 (3):632-643. https://doi.org/10.1007/s00261-019-02321-8

    Google Scholar 

  40. Andreotti RF, Timmerman D, Benacerraf BR, Bennett GL, Bourne T, Brown DL, Coleman BG, Frates MC, Froyman W, Goldstein SR, Hamper UM, Horrow MM, Hernanz-Schulman M, Reinhold C, Strachowski LM, Glanc P (2018) Ovarian-Adnexal Reporting Lexicon for Ultrasound: A White Paper of the ACR Ovarian-Adnexal Reporting and Data System Committee. J Am Coll Radiol 15 (10):1415-1429. https://doi.org/10.1016/j.jacr.2018.07.004

    PubMed  Google Scholar 

  41. Thomassin-Naggara I, Aubert E, Rockall A, Jalaguier-Coudray A, Rouzier R, Darai E, Bazot M (2013) Adnexal masses: development and preliminary validation of an MR imaging scoring system. Radiology 267 (2):432-443. https://doi.org/10.1148/radiol.13121161

    PubMed  Google Scholar 

  42. Patel-Lippmann KK, Sadowski EA, Robbins JB, Paroder V, Barroilhet L, Maddox E, McMahon T, Sampene E, Wasnik AP, Blaty AD, Maturen KE (2020) Comparison of International Ovarian Tumor Analysis Simple Rules to Society of Radiologists in Ultrasound Guidelines for Detection of Malignancy in Adnexal Cysts. AJR Am J Roentgenol 214 (3):694-700. https://doi.org/10.2214/AJR.18.20630

    PubMed  Google Scholar 

  43. Abramowicz JS, Timmerman D (2017) Ovarian mass-differentiating benign from malignant: the value of the International Ovarian Tumor Analysis ultrasound rules. Am J Obstet Gynecol 217 (6):652-660. https://doi.org/10.1016/j.ajog.2017.07.019

    PubMed  Google Scholar 

  44. Timmerman D, Van Calster B, Testa A, Savelli L, Fischerova D, Froyman W, Wynants L, Van Holsbeke C, Epstein E, Franchi D, Kaijser J, Czekierdowski A, Guerriero S, Fruscio R, Leone FPG, Rossi A, Landolfo C, Vergote I, Bourne T, Valentin L (2016) Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group. Am J Obstet Gynecol 214 (4):424-437. https://doi.org/10.1016/j.ajog.2016.01.007

    PubMed  Google Scholar 

  45. Dakhly DMR, Gaafar HM, Sediek MM, Ibrahim MF, Momtaz M (2019) Diagnostic value of the International Ovarian Tumor Analysis (IOTA) simple rules versus pattern recognition to differentiate between malignant and benign ovarian masses. Int J Gynaecol Obstet 147 (3):344-349. https://doi.org/10.1002/ijgo.12970

    PubMed  Google Scholar 

  46. Timmerman D, Testa AC, Bourne T, Ferrazzi E, Ameye L, Konstantinovic ML, Van Calster B, Collins WP, Vergote I, Van Huffel S, Valentin L, International Ovarian Tumor Analysis G (2005) Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group. J Clin Oncol 23 (34):8794-8801. https://doi.org/10.1200/JCO.2005.01.7632

    Google Scholar 

  47. Sladkevicius P, Valentin L (2013) Intra- and interobserver agreement when describing adnexal masses using the International Ovarian Tumor Analysis terms and definitions: a study on three-dimensional ultrasound volumes. Ultrasound Obstet Gynecol 41 (3):318-327. https://doi.org/10.1002/uog.12289

    CAS  PubMed  Google Scholar 

  48. Levine D, Brown DL, Andreotti RF, Benacerraf B, Benson CB, Brewster WR, Coleman B, Depriest P, Doubilet PM, Goldstein SR, Hamper UM, Hecht JL, Horrow M, Hur HC, Marnach M, Patel MD, Platt LD, Puscheck E, Smith-Bindman R (2010) Management of asymptomatic ovarian and other adnexal cysts imaged at US: Society of Radiologists in Ultrasound Consensus Conference Statement. Radiology 256 (3):943-954. https://doi.org/10.1148/radiol.10100213

    PubMed  PubMed Central  Google Scholar 

  49. Levine D, Brown DL, Andreotti RF, Benacerraf B, Benson CB, Brewster WR, Coleman B, DePriest P, Doubilet PM, Goldstein SR, Hamper UM, Hecht JL, Horrow M, Hur HC, Marnach M, Patel MD, Platt LD, Puscheck E, Smith-Bindman R, Society of Radiologists in U (2010) Management of asymptomatic ovarian and other adnexal cysts imaged at US Society of Radiologists in Ultrasound consensus conference statement. Ultrasound Q 26 (3):121-131. https://doi.org/10.1097/RUQ.0b013e3181f09099

    Google Scholar 

  50. Amor F, Vaccaro H, Alcazar JL, Leon M, Craig JM, Martinez J (2009) Gynecologic imaging reporting and data system: a new proposal for classifying adnexal masses on the basis of sonographic findings. J Ultrasound Med 28 (3):285-291. https://doi.org/10.7863/jum.2009.28.3.285

    PubMed  Google Scholar 

  51. Andreotti RF, Timmerman D, Strachowski LM, Froyman W, Benacerraf BR, Bennett GL, Bourne T, Brown DL, Coleman BG, Frates MC, Goldstein SR, Hamper UM, Horrow MM, Hernanz-Schulman M, Reinhold C, Rose SL, Whitcomb BP, Wolfman WL, Glanc P (2020) O-RADS US Risk Stratification and Management System: A Consensus Guideline from the ACR Ovarian-Adnexal Reporting and Data System Committee. Radiology 294 (1):168-185. https://doi.org/10.1148/radiol.2019191150

    PubMed  Google Scholar 

  52. Mohaghegh P, Rockall AG (2012) Imaging strategy for early ovarian cancer: characterization of adnexal masses with conventional and advanced imaging techniques. Radiographics 32 (6):1751-1773. https://doi.org/10.1148/rg.326125520

    PubMed  Google Scholar 

  53. Forstner R, Thomassin-Naggara I, Cunha TM, Kinkel K, Masselli G, Kubik-Huch R, Spencer JA, Rockall A (2017) ESUR recommendations for MR imaging of the sonographically indeterminate adnexal mass: an update. Eur Radiol 27 (6):2248-2257. https://doi.org/10.1007/s00330-016-4600-3

    PubMed  Google Scholar 

  54. Thomassin-Naggara I, Poncelet E, Jalaguier-Coudray A, Guerra A, Fournier LS, Stojanovic S, Millet I, Bharwani N, Juhan V, Cunha TM, Masselli G, Balleyguier C, Malhaire C, Perrot NF, Sadowski EA, Bazot M, Taourel P, Porcher R, Darai E, Reinhold C, Rockall AG (2020) Ovarian-Adnexal Reporting Data System Magnetic Resonance Imaging (O-RADS MRI) Score for Risk Stratification of Sonographically Indeterminate Adnexal Masses. JAMA Netw Open 3 (1):e1919896. https://doi.org/10.1001/jamanetworkopen.2019.19896

    PubMed  PubMed Central  Google Scholar 

  55. Coakley FV, Choi PH, Gougoutas CA, Pothuri B, Venkatraman E, Chi D, Bergman A, Hricak H (2002) Peritoneal metastases: detection with spiral CT in patients with ovarian cancer. Radiology 223 (2):495-499

    PubMed  Google Scholar 

  56. Tempany CM, Zou KH, Silverman SG, Brown DL, Kurtz AB, McNeil BJ (2000) Staging of advanced ovarian cancer: comparison of imaging modalities--report from the Radiological Diagnostic Oncology Group. Radiology 215 (3):761-767

    CAS  PubMed  Google Scholar 

  57. Forstner R, Sala E, Kinkel K, Spencer JA ESUR guidelines: ovarian cancer staging and follow-up. Eur Radiol 20 (12):2773–2780

  58. Javitt MC (2007) ACR Appropriateness Criteria on staging and follow-up of ovarian cancer. J Am Coll Radiol 4 (9):586-589

    PubMed  Google Scholar 

  59. Qayyum A, Coakley FV, Westphalen AC, Hricak H, Okuno WT, Powell B (2005) Role of CT and MR imaging in predicting optimal cytoreduction of newly diagnosed primary epithelial ovarian cancer. Gynecol Oncol 96 (2):301-306

    PubMed  Google Scholar 

  60. Fujii S, Matsusue E, Kanasaki Y, Kanamori Y, Nakanishi J, Sugihara S, Kigawa J, Terakawa N, Ogawa T (2008) Detection of peritoneal dissemination in gynecological malignancy: evaluation by diffusion-weighted MR imaging. Eur Radiol 18 (1):18-23

    PubMed  Google Scholar 

  61. Low RN, Barone RM, Lacey C, Sigeti JS, Alzate GD, Sebrechts CP (1997) Peritoneal tumor: MR imaging with dilute oral barium and intravenous gadolinium-containing contrast agents compared with unenhanced MR imaging and CT. Radiology 204 (2):513-520

    CAS  PubMed  Google Scholar 

  62. Low RN, Semelka RC, Worawattanakul S, Alzate GD, Sigeti JS (1999) Extrahepatic abdominal imaging in patients with malignancy: comparison of MR imaging and helical CT, with subsequent surgical correlation. Radiology 210 (3):625-632

    CAS  PubMed  Google Scholar 

  63. Eisenhauer EA, Therasse P, Bogaerts J, Schwartz LH, Sargent D, Ford R, Dancey J, Arbuck S, Gwyther S, Mooney M, Rubinstein L, Shankar L, Dodd L, Kaplan R, Lacombe D, Verweij J (2009) New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer 45 (2):228–247

  64. Kyriazi S, Collins DJ, Messiou C, Pennert K, Davidson RL, Giles SL, Kaye SB, Desouza NM Metastatic ovarian and primary peritoneal cancer: assessing chemotherapy response with diffusion-weighted MR imaging--value of histogram analysis of apparent diffusion coefficients. Radiology 261 (1):182–192

  65. Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: Images Are More than Pictures, They Are Data. Radiology 278 (2):563-577. https://doi.org/10.1148/radiol.2015151169

    PubMed  Google Scholar 

  66. Hu T, Wang S, Huang L, Wang J, Shi D, Li Y, Tong T, Peng W (2018) A clinical-radiomics nomogram for the preoperative prediction of lung metastasis in colorectal cancer patients with indeterminate pulmonary nodules. Eur Radiol. https://doi.org/10.1007/s00330-018-5539-3

  67. Ortiz-Ramon R, Larroza A, Ruiz-Espana S, Arana E, Moratal D (2018) Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study. Eur Radiol. https://doi.org/10.1007/s00330-018-5463-6

  68. She Y, Zhang L, Zhu H, Dai C, Xie D, Xie H, Zhang W, Zhao L, Zou L, Fei K, Sun X, Chen C (2018) The predictive value of CT-based radiomics in differentiating indolent from invasive lung adenocarcinoma in patients with pulmonary nodules. Eur Radiol. https://doi.org/10.1007/s00330-018-5509-9

  69. Tan X, Ma Z, Yan L, Ye W, Liu Z, Liang C (2018) Radiomics nomogram outperforms size criteria in discriminating lymph node metastasis in resectable esophageal squamous cell carcinoma. Eur Radiol. https://doi.org/10.1007/s00330-018-5581-1

  70. Wang J, Wu CJ, Bao ML, Zhang J, Wang XN, Zhang YD (2017) 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 27 (10):4082-4090. https://doi.org/10.1007/s00330-017-4800-5

    PubMed  Google Scholar 

  71. Matzner-Lober E, Suehs CM, Dohan A, Molinari N (2018) Thoughts on entering correlated imaging variables into a multivariable model: Application to radiomics and texture analysis. Diagn Interv Imaging 99 (5):269-270. https://doi.org/10.1016/j.diii.2018.04.011

    CAS  PubMed  Google Scholar 

  72. Soyer P (2018) Agreement and observer variability. Diagn Interv Imaging 99 (2):53-54. https://doi.org/10.1016/j.diii.2018.01.009

    CAS  PubMed  Google Scholar 

  73. Lubner MG, Smith AD, Sandrasegaran K, Sahani DV, Pickhardt PJ (2017) CT Texture Analysis: Definitions, Applications, Biologic Correlates, and Challenges. Radiographics 37 (5):1483-1503. https://doi.org/10.1148/rg.2017170056

    PubMed  Google Scholar 

  74. Nougaret S, Tibermacine H, Tardieu M, Sala E (2019) Radiomics: an Introductory Guide to What It May Foretell. Curr Oncol Rep 21 (8):70. https://doi.org/10.1007/s11912-019-0815-1

    PubMed  Google Scholar 

  75. Veeraraghavan H, Dashevsky BZ, Onishi N, Sadinski M, Morris E, Deasy JO, Sutton EJ (2018) Appearance Constrained Semi-Automatic Segmentation from DCE-MRI is Reproducible and Feasible for Breast Cancer Radiomics: A Feasibility Study. Sci Rep 8 (1):4838. https://doi.org/10.1038/s41598-018-22980-9

    PubMed  PubMed Central  Google Scholar 

  76. [76] Choi Y, Nam Y, Lee YS, Kim J, Ahn KJ, Jang J, Shin NY, Kim BS, Jeon SS (2020) IDH1 mutation prediction using MR-based radiomics in glioblastoma: comparison between manual and fully automated deep learning-based approach of tumor segmentation. Eur J Radiol 128:109031. https://doi.org/10.1016/j.ejrad.2020.109031

    PubMed  Google Scholar 

  77. van Heeswijk MM, Lambregts DM, van Griethuysen JJ, Oei S, Rao SX, de Graaff CA, Vliegen RF, Beets GL, Papanikolaou N, Beets-Tan RG (2016) Automated and Semiautomated Segmentation of Rectal Tumor Volumes on Diffusion-Weighted MRI: Can It Replace Manual Volumetry? Int J Radiat Oncol Biol Phys 94 (4):824-831. https://doi.org/10.1016/j.ijrobp.2015.12.017

    PubMed  Google Scholar 

  78. Fung YL, Ng KET, Vogrin SJ, Meade C, Ngo M, Collins SJ, Bowden SC (2019) Comparative Utility of Manual versus Automated Segmentation of Hippocampus and Entorhinal Cortex Volumes in a Memory Clinic Sample. J Alzheimers Dis 68 (1):159-171. https://doi.org/10.3233/JAD-181172

    PubMed  Google Scholar 

  79. de Sitter A, Verhoeven T, Burggraaff J, Liu Y, Simoes J, Ruggieri S, Palotai M, Brouwer I, Versteeg A, Wottschel V, Ropele S, Rocca MA, Gasperini C, Gallo A, Yiannakas MC, Rovira A, Enzinger C, Filippi M, De Stefano N, Kappos L, Frederiksen JL, Uitdehaag BMJ, Barkhof F, Guttmann CRG, Vrenken H, Group MS (2020) Reduced accuracy of MRI deep grey matter segmentation in multiple sclerosis: an evaluation of four automated methods against manual reference segmentations in a multi-center cohort. J Neurol. https://doi.org/10.1007/s00415-020-10023-1

  80. Smits LP, van Wijk DF, Duivenvoorden R, Xu D, Yuan C, Stroes ES, Nederveen AJ (2016) Manual versus Automated Carotid Artery Plaque Component Segmentation in High and Lower Quality 3.0 Tesla MRI Scans. PLoS One 11 (12):e0164267. https://doi.org/10.1371/journal.pone.0164267

  81. Elhalawani H, Lin TA, Volpe S, Mohamed ASR, White AL, Zafereo J, Wong AJ, Berends JE, AboHashem S, Williams B, Aymard JM, Kanwar A, Perni S, Rock CD, Cooksey L, Campbell S, Yang P, Nguyen K, Ger RB, Cardenas CE, Fave XJ, Sansone C, Piantadosi G, Marrone S, Liu R, Huang C, Yu K, Li T, Yu Y, Zhang Y, Zhu H, Morris JS, Baladandayuthapani V, Shumway JW, Ghosh A, Pohlmann A, Phoulady HA, Goyal V, Canahuate G, Marai GE, Vock D, Lai SY, Mackin DS, Court LE, Freymann J, Farahani K, Kaplathy-Cramer J, Fuller CD (2018) Machine Learning Applications in Head and Neck Radiation Oncology: Lessons From Open-Source Radiomics Challenges. Front Oncol 8:294. https://doi.org/10.3389/fonc.2018.00294

    PubMed  PubMed Central  Google Scholar 

  82. Pfaehler E, Zwanenburg A, de Jong JR, Boellaard R (2019) RaCaT: An open source and easy to use radiomics calculator tool. PLoS One 14 (2):e0212223. https://doi.org/10.1371/journal.pone.0212223

    CAS  PubMed  PubMed Central  Google Scholar 

  83. Gotz M, Nolden M, Maier-Hein K (2019) MITK Phenotyping: An open-source toolchain for image-based personalized medicine with radiomics. Radiother Oncol 131:108-111. https://doi.org/10.1016/j.radonc.2018.11.021

    PubMed  Google Scholar 

  84. Vallieres M, Zwanenburg A, Badic B, Cheze Le Rest C, Visvikis D, Hatt M (2018) Responsible Radiomics Research for Faster Clinical Translation. J Nucl Med 59 (2):189-193. https://doi.org/10.2967/jnumed.117.200501

    PubMed  PubMed Central  Google Scholar 

  85. Berenguer R, Pastor-Juan MDR, Canales-Vazquez J, Castro-Garcia M, Villas MV, Mansilla Legorburo F, Sabater S (2018) Radiomics of CT Features May Be Nonreproducible and Redundant: Influence of CT Acquisition Parameters. Radiology 288 (2):407-415. https://doi.org/10.1148/radiol.2018172361

    PubMed  Google Scholar 

  86. Kuo MD, Jamshidi N (2014) Behind the numbers: Decoding molecular phenotypes with radiogenomics--guiding principles and technical considerations. Radiology 270 (2):320-325. https://doi.org/10.1148/radiol.13132195

    PubMed  Google Scholar 

  87. Parmar C, Grossmann P, Bussink J, Lambin P, Aerts H (2015) Machine Learning methods for Quantitative Radiomic Biomarkers. Sci Rep 5:13087. https://doi.org/10.1038/srep13087

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Kakino R, Nakamura M, Mitsuyoshi T, Shintani T, Kokubo M, Negoro Y, Fushiki M, Ogura M, Itasaka S, Yamauchi C, Otsu S, Sakamoto T, Sakamoto M, Araki N, Hirashima H, Adachi T, Matsuo Y, Mizowaki T (2020) Application and limitation of radiomics approach to prognostic prediction for lung stereotactic body radiotherapy using breath-hold CT images with random survival forest: A multi-institutional study. Med Phys. https://doi.org/10.1002/mp.14380

  89. Antonacci Y, Toppi J, Mattia D, Pietrabissa A, Astolfi L (2019) Single-trial Connectivity Estimation through the Least Absolute Shrinkage and Selection Operator. Conf Proc IEEE Eng Med Biol Soc 2019:6422-6425. https://doi.org/10.1109/EMBC.2019.8857909

    Google Scholar 

  90. Currie G, Iqbal B, Kiat H (2019) Intelligent Imaging: Radiomics and Artificial Neural Networks in Heart Failure. J Med Imaging Radiat Sci 50 (4):571-574. https://doi.org/10.1016/j.jmir.2019.08.006

    PubMed  Google Scholar 

  91. Xu L, Yang P, Liang W, Liu W, Wang W, Luo C, Wang J, Peng Z, Xing L, Huang M, Zheng S, Niu T (2019) A radiomics approach based on support vector machine using MR images for preoperative lymph node status evaluation in intrahepatic cholangiocarcinoma. Theranostics 9 (18):5374-5385. https://doi.org/10.7150/thno.34149

    PubMed  PubMed Central  Google Scholar 

  92. Radovic M, Ghalwash M, Filipovic N, Obradovic Z (2017) Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics 18 (1):9. https://doi.org/10.1186/s12859-016-1423-9

    PubMed  PubMed Central  Google Scholar 

  93. Delzell DAP, Magnuson S, Peter T, Smith M, Smith BJ (2019) Machine Learning and Feature Selection Methods for Disease Classification With Application to Lung Cancer Screening Image Data. Front Oncol 9:1393. https://doi.org/10.3389/fonc.2019.01393

    PubMed  PubMed Central  Google Scholar 

  94. Kearns M, Ron D (1999) Algorithmic stability and sanity-check bounds for leave-one-out cross-validation. Neural Comput 11 (6):1427-1453. https://doi.org/10.1162/089976699300016304

    CAS  PubMed  Google Scholar 

  95. Conrad M, Rizki MM (1980) Computational illustration of the bootstrap effect. Biosystems 13 (1-2):57-64. https://doi.org/10.1016/0303-2647(80)90005-2

    CAS  PubMed  Google Scholar 

  96. Blum A, Wang P, Zenklusen JC (2018) SnapShot: TCGA-Analyzed Tumors. Cell 173 (2):530. https://doi.org/10.1016/j.cell.2018.03.059

    CAS  PubMed  Google Scholar 

  97. Liu J, Lichtenberg T, Hoadley KA, Poisson LM, Lazar AJ, Cherniack AD, Kovatich AJ, Benz CC, Levine DA, Lee AV, Omberg L, Wolf DM, Shriver CD, Thorsson V, Cancer Genome Atlas Research N, Hu H (2018) An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics. Cell 173 (2):400–416 e411. https://doi.org/10.1016/j.cell.2018.02.052

  98. Tothill RW, Tinker AV, George J, Brown R, Fox SB, Lade S, Johnson DS, Trivett MK, Etemadmoghadam D, Locandro B, Traficante N, Fereday S, Hung JA, Chiew YE, Haviv I, Australian Ovarian Cancer Study G, Gertig D, DeFazio A, Bowtell DD (2008) Novel molecular subtypes of serous and endometrioid ovarian cancer linked to clinical outcome. Clin Cancer Res 14 (16):5198-5208. https://doi.org/10.1158/1078-0432.CCR-08-0196

    Google Scholar 

  99. Verhaak RG, Tamayo P, Yang JY, Hubbard D, Zhang H, Creighton CJ, Fereday S, Lawrence M, Carter SL, Mermel CH, Kostic AD, Etemadmoghadam D, Saksena G, Cibulskis K, Duraisamy S, Levanon K, Sougnez C, Tsherniak A, Gomez S, Onofrio R, Gabriel S, Chin L, Zhang N, Spellman PT, Zhang Y, Akbani R, Hoadley KA, Kahn A, Kobel M, Huntsman D, Soslow RA, Defazio A, Birrer MJ, Gray JW, Weinstein JN, Bowtell DD, Drapkin R, Mesirov JP, Getz G, Levine DA, Meyerson M, Cancer Genome Atlas Research N (2013) Prognostically relevant gene signatures of high-grade serous ovarian carcinoma. J Clin Invest 123 (1):517-525. https://doi.org/10.1172/JCI65833

    PubMed  Google Scholar 

  100. Vargas HA, Wassberg C, Fox JJ, Wibmer A, Goldman DA, Kuk D, Gonen M, Larson SM, Morris MJ, Scher HI, Hricak H (2015) Response. Radiology 274 (2):625

    PubMed  Google Scholar 

  101. Vargas HA, Huang EP, Lakhman Y, Ippolito JE, Bhosale P, Mellnick V, Shinagare AB, Anello M, Kirby J, Fevrier-Sullivan B, Freymann J, Jaffe CC, Sala E (2017) Radiogenomics of High-Grade Serous Ovarian Cancer: Multireader Multi-Institutional Study from the Cancer Genome Atlas Ovarian Cancer Imaging Research Group. Radiology 285 (2):482-492. https://doi.org/10.1148/radiol.2017161870

    PubMed  PubMed Central  Google Scholar 

  102. Vargas HA, Veeraraghavan H, Micco M, Nougaret S, Lakhman Y, Meier AA, Sosa R, Soslow RA, Levine DA, Weigelt B, Aghajanian C, Hricak H, Deasy J, Snyder A, Sala E (2017) A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. Eur Radiol 27 (9):3991-4001. https://doi.org/10.1007/s00330-017-4779-y

    PubMed  PubMed Central  Google Scholar 

  103. Meier A, Veeraraghavan H, Nougaret S, Lakhman Y, Sosa R, Soslow RA, Sutton EJ, Hricak H, Sala E, Vargas HA (2019) Association between CT-texture-derived tumor heterogeneity, outcomes, and BRCA mutation status in patients with high-grade serous ovarian cancer. Abdom Radiol (NY) 44 (6):2040-2047. https://doi.org/10.1007/s00261-018-1840-5

    Google Scholar 

  104. Lu H, Arshad M, Thornton A, Avesani G, Cunnea P, Curry E, Kanavati F, Liang J, Nixon K, Williams ST, Hassan MA, Bowtell DDL, Gabra H, Fotopoulou C, Rockall A, Aboagye EO (2019) A mathematical-descriptor of tumor-mesoscopic-structure from computed-tomography images annotates prognostic- and molecular-phenotypes of epithelial ovarian cancer. Nat Commun 10 (1):764. https://doi.org/10.1038/s41467-019-08718-9

    CAS  PubMed  PubMed Central  Google Scholar 

  105. Song XL, Ren JL, Zhao D, Wang L, Ren H, Niu J (2020) Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms. Eur Radiol. https://doi.org/10.1007/s00330-020-07112-0

  106. Zhang H, Mao Y, Chen X, Wu G, Liu X, Zhang P, Bai Y, Lu P, Yao W, Wang Y, Yu J, Zhang G (2019) Magnetic resonance imaging radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study. Eur Radiol 29 (7):3358-3371. https://doi.org/10.1007/s00330-019-06124-9

    PubMed  Google Scholar 

  107. Jian J, Li Y, Pickhardt PJ, Xia W, He Z, Zhang R, Zhao S, Zhao X, Cai S, Zhang J, Zhang G, Jiang J, Zhang Y, Wang K, Lin G, Feng F, Wu X, Gao X, Qiang J (2020) MR image-based radiomics to differentiate type Iota and type IotaIota epithelial ovarian cancers. Eur Radiol. https://doi.org/10.1007/s00330-020-07091-2

  108. Qian L, Ren J, Liu A, Gao Y, Hao F, Zhao L, Wu H, Niu G (2020) MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes. Eur Radiol. https://doi.org/10.1007/s00330-020-06993-5

  109. Wang G, Sun Y, Chen Y, Gao Q, Peng D, Lin H, Zhan Z, Liu Z, Zhuo S (2020) Rapid identification of human ovarian cancer in second harmonic generation images using radiomics feature analyses and tree-based pipeline optimization tool. J Biophotonics:e202000050. https://doi.org/10.1002/jbio.202000050

  110. Medicine NLo (2020) Proteomics MeSH Descriptor Data https://meshb.nlm.nih.gov/record/ui?ui=D040901.

  111. Zhang H, Liu T, Zhang Z, Payne SH, Zhang B, McDermott JE, Zhou J-Y, Petyuk VA, Chen L, Ray D, Sun S, Yang F, Chen L, Wang J, Shah P, Cha SW, Aiyetan P, Woo S, Tian Y, Gritsenko MA, Clauss TR, Choi C, Monroe ME, Thomas S, Nie S, Wu C, Moore RJ, Yu K-H, Tabb DL, Fenyö D, Bafna V, Wang Y, Rodriguez H, Boja ES, Hiltke T, Rivers RC, Sokoll L, Zhu H, Shih I-M, Cope L, Pandey A, Zhang B, Snyder MP, Levine DA, Smith RD, Chan DW, Rodland KD, Carr SA, Gillette MA, Klauser KR, Kuhn E, Mani DR, Mertins P, Ketchum KA, Thangudu R, Cai S, Oberti M, Paulovich AG, Whiteaker JR, Edwards NJ, McGarvey PB, Madhavan S, Wang P, Chan DW, Pandey A, Shih I-M, Zhang H, Zhang Z, Zhu H, Cope L, Whiteley GA, Skates SJ, White FM, Levine DA, Boja ES, Kinsinger CR, Hiltke T, Mesri M, Rivers RC, Rodriguez H, Shaw KM, Stein SE, Fenyo D, Liu T, McDermott JE, Payne SH, Rodland KD, Smith RD, Rudnick P, Snyder M, Zhao Y, Chen X, Ransohoff DF, Hoofnagle AN, Liebler DC, Sanders ME, Shi Z, Slebos RJC, Tabb DL, Zhang B, Zimmerman LJ, Wang Y, Davies SR, Ding L, Ellis MJC, Townsend RR (2016) Integrated Proteogenomic Characterization of Human High-Grade Serous Ovarian Cancer. Cell 166 (3):755-765. https://doi.org/10.1016/j.cell.2016.05.069

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Zhang B, Whiteaker JR, Hoofnagle AN, Baird GS, Rodland KD, Paulovich AG (2019) Clinical potential of mass spectrometry-based proteogenomics. Nat Rev Clin Oncol 16 (4):256-268. https://doi.org/10.1038/s41571-018-0135-7

    PubMed  PubMed Central  Google Scholar 

  113. Yang J-Y, Yoshihara K, Tanaka K, Hatae M, Masuzaki H, Itamochi H, Cancer Genome Atlas Research N, Takano M, Ushijima K, Tanyi JL, Coukos G, Lu Y, Mills GB, Verhaak RGW (2013) Predicting time to ovarian carcinoma recurrence using protein markers. J Clin Invest 123 (9):3740-3750. https://doi.org/10.1172/JCI68509

    Google Scholar 

  114. Integrated genomic analyses of ovarian carcinoma (2011). Nature 474 (7353):609–615. https://doi.org/10.1038/nature10166

  115. Beer L, Sahin H, Bateman NW, Blazic I, Vargas HA, Veeraraghavan H, Kirby J, Fevrier-Sullivan B, Freymann JB, Jaffe CC, Brenton J, Miccó M, Nougaret S, Darcy KM, Maxwell GL, Conrads TP, Huang E, Sala E (2020) Integration of proteomics with CT-based qualitative and radiomic features in high-grade serous ovarian cancer patients: an exploratory analysis. Eur Radiol 30 (8):4306-4316. https://doi.org/10.1007/s00330-020-06755-3

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Weon JL, Potts PR (2015) The MAGE protein family and cancer. Curr Opin Cell Biol 37:1-8. https://doi.org/10.1016/j.ceb.2015.08.002

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Yakirevich E, Sabo E, Lavie O, Mazareb S, Spagnoli G, Resnick M (2003) Expression of the MAGE-A4 and NY-ESO-1 cancer-testis antigens in serous ovarian neoplasms. Clinical cancer research : an official journal of the American Association for Cancer Research 9:6453-6460

    CAS  Google Scholar 

  118. Xu Y, Wang C, Zhang Y, Jia L, Huang J (2015) Overexpression of MAGE-A9 Is Predictive of Poor Prognosis in Epithelial Ovarian Cancer. Sci Rep 5:12104. https://doi.org/10.1038/srep12104

    CAS  PubMed  PubMed Central  Google Scholar 

  119. Nicholson LJ, Smith PR, Hiller L, Szlosarek PW, Kimberley C, Sehouli J, Koensgen D, Mustea A, Schmid P, Crook T (2009) Epigenetic silencing of argininosuccinate synthetase confers resistance to platinum-induced cell death but collateral sensitivity to arginine auxotrophy in ovarian cancer. International Journal of Cancer 125 (6):1454-1463. https://doi.org/10.1002/ijc.24546

    CAS  PubMed  Google Scholar 

  120. Vargas HA, Veeraraghavan H, Micco M, Nougaret S, Lakhman Y, Meier AA, Sosa R, Soslow RA, Levine DA, Weigelt B, Aghajanian C, Hricak H, Deasy J, Snyder A, Sala E (2017) A novel representation of inter-site tumour heterogeneity from pre-treatment computed tomography textures classifies ovarian cancers by clinical outcome. European Radiology 27 (9):3991-4001. https://doi.org/10.1007/s00330-017-4779-y

    PubMed  PubMed Central  Google Scholar 

  121. Fiore L, Rodriguez H, Shriver C (2017) Collaboration to Accelerate Proteogenomics Cancer Care: The Department of Veterans Affairs, Department of Defense, and the National Cancer Institute's Applied Proteogenomics OrganizationaL Learning and Outcomes (APOLLO) Network. Clinical Pharmacology & Therapeutics 101 (5):619-621. https://doi.org/10.1002/cpt.658

    CAS  Google Scholar 

  122. Pinker K, Chin J, Melsaether AN, Morris EA, Moy L (2018) Precision Medicine and Radiogenomics in Breast Cancer: New Approaches toward Diagnosis and Treatment. Radiology 287 (3):732-747. https://doi.org/10.1148/radiol.2018172171

    PubMed  Google Scholar 

  123. Zwanenburg A, Leger S, Vallières M, Löck S (2018) Initiative for the IBS. Image biomarker standardisation initiative. https://www.arxivorg/abs/161207003

  124. Vokes TJ, Pham A, Wilkie J, Kocherginsky M, Ma SL, Chinander M, Karrison T, Bris O, Giger ML (2008) Reproducibility and sources of variability in radiographic texture analysis of densitometric calcaneal images. J Clin Densitom 11 (2):211-220. https://doi.org/10.1016/j.jocd.2007.10.004

    PubMed  Google Scholar 

  125. Mwangi B, Tian TS, Soares JC (2014) A review of feature reduction techniques in neuroimaging. Neuroinformatics 12 (2):229-244. https://doi.org/10.1007/s12021-013-9204-3

    PubMed  PubMed Central  Google Scholar 

  126. Vallieres M, Laberge S, Diamant A, El Naqa I (2017) Enhancement of multimodality texture-based prediction models via optimization of PET and MR image acquisition protocols: a proof of concept. Phys Med Biol 62 (22):8536-8565. https://doi.org/10.1088/1361-6560/aa8a49

    PubMed  Google Scholar 

  127. Parmar C, Rios Velazquez E, Leijenaar R, Jermoumi M, Carvalho S, Mak RH, Mitra S, Shankar BU, Kikinis R, Haibe-Kains B, Lambin P, Aerts HJ (2014) Robust Radiomics feature quantification using semiautomatic volumetric segmentation. PLoS One 9 (7):e102107. https://doi.org/10.1371/journal.pone.0102107

    PubMed  PubMed Central  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. Nougaret.

Ethics declarations

Conflict of interest

The authors declared that they have no conflicts of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nougaret, S., McCague, C., Tibermacine, H. et al. Radiomics and radiogenomics in ovarian cancer: a literature review. Abdom Radiol 46, 2308–2322 (2021). https://doi.org/10.1007/s00261-020-02820-z

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00261-020-02820-z

Keywords

Navigation