Current Oncology Reports

, 19:85 | Cite as

What’s New in Imaging for Gynecologic Cancer?

  • Sairah R. KhanEmail author
  • Mubarik Arshad
  • Kathryn Wallitt
  • Victoria Stewart
  • Nishat Bharwani
  • Tara D. Barwick
Gynecologic Cancers (NS Reed, Section Editor)
Part of the following topical collections:
  1. Topical Collection on Gynecologic Cancers


Magnetic resonance imaging (MRI) is the optimal modality for local staging of gynecological tumors. Advances in functional MRI with diffusion-weighted and dynamic contrast-enhanced sequences provide more detailed information regarding tumor cellularity, vascularity, and viability. Fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) now has an established role in imaging for gynecological cancers, particularly staging of locally advanced cervical cancers and pre-salvage exenterative therapy in relapsed gynecologic tumors. Novel PET tracers, targeting other aspects of tumor biology, are being evaluated although none are currently in routine clinical use. New PET/MR scanners have the potential to combine the strengths of both modalities in one sitting. This review covers advances in gynecologic imaging concentrating on cervical, endometrial, and ovarian cancers.


Positron emission tomography/computed tomography (PET/CT) Magnetic resonance imaging (MRI) Diffusion-weighted imaging (DWI) Dynamic contrast enhanced MRI (DCE-MRI) Functional imaging PET/MR 


Compliance with Ethical Standards

Conflict of Interest

Sairah R. Khan, Mubarik Arshad, Kathryn Wallitt, Victoria Stewart, Nishat Bharwani, and Tara D. Barwick 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.


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

  1. 1.
    Ferlay J, Soerjomataram II, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2014;136(5):E359–86.PubMedCrossRefGoogle Scholar
  2. 2.
    Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D. Global cancer statistics. CA Cancer J Clin. 2011;61(2):69–90.PubMedCrossRefGoogle Scholar
  3. 3.
    Micco M, Vargas HA, Burger IA, Kollmeier MA, Goldman DA, Park KJ, et al. Combined pre-treatment MRI and 18F-FDG PET/CT parameters as prognostic biomarkers in patients with cervical cancer. Eur J Radiol. 2014;83(7):1169–76.PubMedCrossRefGoogle Scholar
  4. 4.
    Somoye G, Harry V, Semple S, Plataniotis G, Scott N, Gilbert FJ, et al. Early diffusion weighted magnetic resonance imaging can predict survival in women with locally advanced cancer of the cervix treated with combined chemo-radiation. Eur Radiol. 2012;22(11):2319–27.PubMedCrossRefGoogle Scholar
  5. 5.
    Liu Y, Bai R, Sun H, Liu H, Zhao X, Li Y. Diffusion-weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. Clin Radiol. 2009;64(11):1067–74.PubMedCrossRefGoogle Scholar
  6. 6.
    Harry VN, Semple SI, Gilbert FJ, Parkin DE. Diffusion-weighted magnetic resonance imaging in the early detection of response to chemoradiation in cervical cancer. Gynecol Oncol. 2008;111(2):213–20.PubMedCrossRefGoogle Scholar
  7. 7.
    Kim HS, Kim CK, Park BK, Huh SJ, Kim B. Evaluation of therapeutic response to concurrent chemoradiotherapy in patients with cervical cancer using diffusion-weighted MR imaging. J Magn Reson Imaging. 2013;37(1):187–93.PubMedCrossRefGoogle Scholar
  8. 8.
    Kuang F, Yan Z, Wang J, Rao Z. The value of diffusion-weighted MRI to evaluate the response to radiochemotherapy for cervical cancer. Magn Reson Imaging. 2014;32(4):342–9.PubMedCrossRefGoogle Scholar
  9. 9.
    Makino H, Kato H, Furui T, Morishige K-I, Kanematsu M. Predictive value of diffusion-weighted magnetic resonance imaging during chemoradiotherapy for uterine cervical cancer. J Obstet Gynaecol Res. 2014;40(4):1098–104.PubMedCrossRefGoogle Scholar
  10. 10.
    Zahra MA, Tan LT, Priest AN, Graves MJ, Arends M, Crawford RAF, et al. Semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging measurements predict radiation response in cervix cancer. Int J Radiat Oncol Biol Phys. 2009;74(3):766–73.PubMedCrossRefGoogle Scholar
  11. 11.
    Kidd EA, Siegel BA, Dehdashti F, Grigsby PW. The standardized uptake value for F-18 fluorodeoxyglucose is a sensitive predictive biomarker for cervical cancer treatment response and survival. Cancer. 2007;110(8):1738–44.PubMedCrossRefGoogle Scholar
  12. 12.
    Xue F, Lin LL, Dehdashti F, Miller TR, Siegel BA, Grigsby PW. F-18 fluorodeoxyglucose uptake in primary cervical cancer as an indicator of prognosis after radiation therapy. Gynecol Oncol. 2006;101:147–51.PubMedCrossRefGoogle Scholar
  13. 13.
    Lee YY, Choi CH, Kim CJ, Kang H, Kim TJ, Lee JW, et al. The prognostic significance of the SUVmax (maximum standardized uptake value for F-18 fluorodeoxyglucose) of the cervical tumor in PET imaging for early cervical cancer: preliminary results. Gynecol Oncol. 2009;115(1):65–8.PubMedCrossRefGoogle Scholar
  14. 14.
    Pan LL, Cheng JY, Zhou M, Yao ZF, Zhang YJ. The SUVmax (maximum standardized uptake value for F-18 fluorodeoxyglucose) and serum squamous cell carcinoma antigen (SCC-ag) function as prognostic biomarkers in patients with primary cervical cancer. J Cancer Res Clin Oncol. 2012;138:239–46.PubMedCrossRefGoogle Scholar
  15. 15.
    Miller TR, Grigsby PW. Measurement of tumor volume by PET to evaluate prognosis in patients with advanced cervical cancer treated by radiation therapy. Int J Radiat Oncol Biol Phys. 2002;53(2):353–9.PubMedCrossRefGoogle Scholar
  16. 16.
    Chung HH, Kim JW, Han KH, Eo JS, Kang KW, Park N-H, et al. Prognostic value of metabolic tumor volume measured by FDG-PET/CT in patients with cervical cancer. Gynecol Oncol Elsevier Inc. 2010;120(2):270–4.CrossRefGoogle Scholar
  17. 17.
    Chong GO, Jeong SY, Park S-H, Lee YH, Lee S-W, Hong DG, et al. Comparison of the prognostic value of F-18 pet metabolic parameters of primary tumors and regional lymph nodes in patients with locally advanced cervical cancer who are treated with concurrent chemoradiotherapy. PLoS One. 2015;10(9):e0137743.PubMedPubMedCentralCrossRefGoogle Scholar
  18. 18.
    Kidd EA, Siegel BA, Dehdashti F, Rader JS, Mutch DG, Powell MA, et al. Lymph node staging by positron emission tomography in cervical cancer: relationship to prognosis. J Clin Oncol. 2010;28(12):2108–13.PubMedCrossRefGoogle Scholar
  19. 19.
    Onal C, Reyhan M, Parlak C, Guler OC, Oymak E, C. O, et al. Prognostic value of pretreatment 18F-fluorodeoxyglucose uptake in patients with cervical cancer treated with definitive chemoradiotherapy. Int J Gynecol Cancer. 2013;23(6):1104–10.PubMedCrossRefGoogle Scholar
  20. 20.
    Kidd EA, Siegel BA, Dehdashti F, Grigsby PW. Pelvic lymph node F-18 fluorodeoxyglucose uptake as a prognostic biomarker in newly diagnosed patients with locally advanced cervical cancer. Cancer. 2010;116(6):1469–75.PubMedCrossRefGoogle Scholar
  21. 21.
    Onal C, Guler OC, Reyhan M, Yapar AF. Prognostic value of 18F-fluorodeoxyglucose uptake in pelvic lymph nodes in patients with cervical cancer treated with definitive chemoradiotherapy. Gynecol Oncol. 2015;137(1):40–6.PubMedCrossRefGoogle Scholar
  22. 22.
    Kidd EA, Thomas M, Siegel BA, Dehdashti F, Grigsby PW. Changes in cervical cancer FDG uptake during chemoradiation and association with response. Int J Radiat Oncol Biol Phys. 2013;85(1):116–22.PubMedCrossRefGoogle Scholar
  23. 23.
    Bjurberg M, Kjellén E, Ohlsson T, Bendahl P-O, Brun E. Prediction of patient outcome with 2-deoxy-2-[18F]fluoro-D-glucose-positron emission tomography early during radiotherapy for locally advanced cervical cancer. Int J Gynecol Cancer. 2009;19(9):1600–5.PubMedCrossRefGoogle Scholar
  24. 24.
    Scottish Intercollegiate Guidelines Network. Guideline 9. Management of cervical cancer. 2008.Google Scholar
  25. 25.
    Boss EA, Massuger LF, Pop LA, Verhoef LC, Huisman HJ, Boonstra H, et al. Post-radiotherapy contrast enhancement changes in fast dynamic MRI of cervical carcinoma. J Magn Reson Imaging. 2001;13(4):600–6.PubMedCrossRefGoogle Scholar
  26. 26.
    Schwarz JK, Siegel BA, Dehdashti F, Grigsby PW. Metabolic response on post-therapy FDG-PET predicts patterns of failure after radiotherapy for cervical cancer. Int J Radiat Oncol Biol Phys Elsevier Inc. 2012;83(1):185–90.CrossRefGoogle Scholar
  27. 27.
    Grigsby PW, Siegel BA, Dehdashti F, Rader J, Zoberi I. Posttherapy [18F] fluorodeoxyglucose positron emission tomography in carcinoma of the cervix: response and outcome. J Clin Oncol. 2004;22(11):2167–71.PubMedCrossRefGoogle Scholar
  28. 28.
    Beriwal S, Kannan N, Sukumvanich P, Richard SD, Kelley JL, Edwards RP, et al. Complete metabolic response after definitive radiation therapy for cervical cancer: patterns and factors predicting for recurrence. Gynecol Oncol Elsevier Inc. 2012;127(2):303–6.CrossRefGoogle Scholar
  29. 29.
    Onal C, Reyhan M, Guler OC, Yapar AF. Treatment outcomes of patients with cervical cancer with complete metabolic responses after definitive chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2014;41(7):1336–42.PubMedCrossRefGoogle Scholar
  30. 30.
    El Naqa I, Grigsby P, 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.CrossRefGoogle Scholar
  31. 31.
    Yang F, Thomas MA, Dehdashti F, Grigsby PW. Temporal analysis of intratumoral metabolic heterogeneity characterized by textural features in cervical cancer. Eur J Nucl Med Mol Imaging. 2013;40(5):716–27.PubMedPubMedCentralCrossRefGoogle Scholar
  32. 32.
    Liu FY, Chao A, Lai CH, Chou HH, Yen TC. Metabolic tumor volume by 18 F-FDG PET/CT is prognostic for stage IVB endometrial carcinoma. Gynecol Oncol. 2012;125(3):566–71.PubMedCrossRefGoogle Scholar
  33. 33.
    Forstner R, Thomassin-Naggara I, Cunha TM, Kinkel K, Masselli G, Kubik-Huch R, et al. ESUR recommendations for MR imaging of the sonographically indeterminate adnexal mass: an update. Eur Radiol. 2017;27(6):2248–57.PubMedCrossRefGoogle Scholar
  34. 34.
    Husby JA, Reitan BC, Biermann M, Trovik J, Bjørge L, Magnussen IJ, et al. Metabolic tumor volume on 18F-FDG PET/CT improves preoperative identification of high-risk endometrial carcinoma patients. J Nucl Med. 2015;56(8):1191–8.PubMedCrossRefGoogle Scholar
  35. 35.
    Walentowicz-Sadlecka M, Malkowski B, Walentowicz P, Sadlecki P, Marszalek A, Pietrzak T, et al. The preoperative maximum standardized uptake value measured by 18F-FDG PET/CT as an independent prognostic factor of overall survival in endometrial cancer patients. Biomed Res Int. 2014;2014:234813.PubMedPubMedCentralCrossRefGoogle Scholar
  36. 36.
    Kitajima K, Kita M, Suzuki K, Senda M, Nakamoto Y, Sugimura K. Prognostic significance of SUVmax (maximum standardized uptake value) measured by [18F]FDG PET/CT in endometrial cancer. Eur J Nucl Med Mol Imaging. 2012;39(5):840–5.PubMedCrossRefGoogle Scholar
  37. 37.
    Shim SH, Kim DY, Lee DY, Lee SW, Park JY, Lee J, et al. Metabolic tumour volume and total lesion glycolysis, measured using preoperative 18F-FDG PET/CT, predict the recurrence of endometrial cancer. BJOG Int J Obstet Gynaecol. 2014;121(9):1097–106.CrossRefGoogle Scholar
  38. 38.
    Chung HH, Lee I, Kim HS, Kim JW, Park N-H, Song YS, et al. Prognostic value of preoperative metabolic tumor volume measured by 18F-FDG PET/CT and MRI in patients with endometrial cancer. Gynecol Oncol. 2013;130(3):446–51.PubMedCrossRefGoogle Scholar
  39. 39.
    Stahl A, Wieder H, Piert M, Wester HJ, Senekowitsch-Schmidtke R, Schwaiger M. Positron emission tomography as a tool for translational research in oncology. Mol Imaging Biol. 2004;6:214–24.Google Scholar
  40. 40.
    Lakhani A, Khan SR, Bharwani N, Stewart V, Rockall AG, Khan SBT. FDG PET/CT pitfalls in gynecologic and genitourinary oncologic imaging. Radiographics. 2017;37(2):577–94.PubMedCrossRefGoogle Scholar
  41. 41.
    Chung HH, Kwon HW, Kang KW, Park N-H, Song Y-S, Chung J-K, et al. Prognostic value of preoperative metabolic tumor volume and total lesion glycolysis in patients with epithelial ovarian cancer. Ann Surg Oncol. 2012;19(6):1966–72.PubMedCrossRefGoogle Scholar
  42. 42.
    Avril N, Sassen S, Schmalfeldt B, Naehrig J, Rutke S, Weber WA, et al. Prediction of response to neoadjuvant chemotherapy by sequential F-18-fluorodeoxyglucose positron emission tomography in patients with advanced-stage ovarian cancer. J Clin Oncol. 2005;23(30):7445–53.PubMedCrossRefGoogle Scholar
  43. 43.
    Vargas HA, Burger IA, Goldman DA, Miccò M, Sosa RE, Weber W, et al. Volume-based quantitative FDG PET/CT metrics and their association with optimal debulking and progression-free survival in patients with recurrent ovarian cancer undergoing secondary cytoreductive surgery. Eur Radiol. 2015;25(11):3348–53.PubMedPubMedCentralCrossRefGoogle Scholar
  44. 44.
    Kim C-Y, Jeong SY, Chong GO, Son SH, Jung J, Kim D-H, et al. Quantitative metabolic parameters measured on F-18 FDG PET/CT predict survival after relapse in patients with relapsed epithelial ovarian cancer. Gynecol Oncol. 2015;136(3):498–504.PubMedCrossRefGoogle Scholar
  45. 45.
    Martoni AA, Fanti S, Zamagni C, Rosati M, De Iaco P, D’Errico Grigioni A, et al. [18F]FDG-PET/CT monitoring early identifies advanced ovarian cancer patients who will benefit from prolonged neo-adjuvant chemotherapy. Q J Nucl Med Mol imaging. 2011;55(1):81–90.PubMedGoogle Scholar
  46. 46.
    Hameeduddin A, Sahdev A. Diffusion-weighted imaging and dynamic contrast-enhanced MRI in assessing response and recurrent disease in gynaecological malignancies. Cancer Imaging. 2015;15:3.PubMedPubMedCentralCrossRefGoogle Scholar
  47. 47.
    Sala E, Rockall AG, Freeman SJ, Mitchell DG, Reinhold C. The added role of MR imaging in treatment stratification of patients with gynecologic malignancies: what the radiologist needs to know. Radiology. 2013;266(3):717–40.PubMedCrossRefGoogle Scholar
  48. 48.
    Mohaghegh P, Rockall AG. Imaging strategy for early ovarian cancer: characterization of adnexal masses with conventional and advanced imaging techniques. Radiographics. 2012;32(6):1751–73.PubMedCrossRefGoogle Scholar
  49. 49.
    Naganawa S, Sato C, Kumada H, Ishigaki T, Miura S, Takizawa O. Apparent diffusion coefficient in cervical cancer of the uterus: comparison with the normal uterine cervix. Eur Radiol. 2005;15(1):71–8.PubMedCrossRefGoogle Scholar
  50. 50.
    Zhang Y, Liang BL, Gao L, Ye RX, Shen J, Zhong JL. Diffusion weighted imaging features of normal uterine cervix and cervical carcinoma. Ai Zheng. 2007;26(5):508–12.PubMedGoogle Scholar
  51. 51.
    Payne GS, Schmidt M, Morgan VA, Giles S, Bridges J, Ind T, et al. Evaluation of magnetic resonance diffusion and spectroscopy measurements as predictive biomarkers in stage 1 cervical cancer. Gynecol Oncol. 2010;116(2):246–52.PubMedCrossRefGoogle Scholar
  52. 52.
    Liu Y, Liu H, Bai X, Ye Z, Sun H, Bai R, et al. Differentiation of metastatic from non-metastatic lymph nodes in patients with uterine cervical cancer using diffusion-weighted imaging. Gynecol Oncol. 2011;122(1):19–24.PubMedCrossRefGoogle Scholar
  53. 53.
    Nakai G, Matsuki M, Inada Y, Tatsugami F, Tanikake M, Narabayashi I, et al. Detection and evaluation of pelvic lymph nodes in patients with gynecologic malignancies using body diffusion-weighted magnetic resonance imaging. J Comput Assist Tomogr. 2008;32(5):764–8.PubMedCrossRefGoogle Scholar
  54. 54.
    Park SO, Kim JK, Kim KA, Park BW, Kim N, Cho G, et al. Relative apparent diffusion coefficient: determination of reference site and validation of benefit for detecting metastatic lymph nodes in uterine cervical cancer. J Magn Reson Imaging. 2009;29(2):383–90.PubMedCrossRefGoogle Scholar
  55. 55.
    Schreuder SM, Lensing R, Stoker J, Bipat S. Monitoring treatment response in patients undergoing chemoradiotherapy for locally advanced uterine cervical cancer by additional diffusion-weighted imaging: a systematic review. J Magn Reson Imaging. 2015;42(3):572–94.PubMedCrossRefGoogle Scholar
  56. 56.
    Wu B, Huang X, Peng W, Gu Y, Yang T, Mao J, et al. Value of MR diffusion-weighted imaging in diagnosis and outcome prediction for uterine cervical cancer. Zhonghua Zhong Liu Za Zhi. 2014;36(2):115–9.PubMedGoogle Scholar
  57. 57.
    Mayr NA, Yuh WTC, Magnotta VA, Ehrhardt JC, Wheeler JA, Sorosky JI, et al. Tumor perfusion studies using fast magnetic resonance imaging technique in advanced cervical cancer: a new noninvasive predictive assay. Int J Radiat Oncol Biol Phys. 1996;36(3):623–33.PubMedCrossRefGoogle Scholar
  58. 58.
    Mayr NA, Wang JZ, Zhang D, Grecula JC, Lo SS, Jaroura D, et al. Longitudinal changes in tumor perfusion pattern during the radiation therapy course and its clinical impact in cervical cancer. Int J Radiat Oncol Biol Phys. 2010;77(2):502–8.PubMedCrossRefGoogle Scholar
  59. 59.
    Lund KV, Simonsen TG, Hompland T, Kristensen GB, Rofstad EK. Short-term pretreatment DCE-MRI in prediction of outcome in locally advanced cervical cancer. Radiother Oncol. 2015;115(3):379–85.PubMedCrossRefGoogle Scholar
  60. 60.
    Yuh WTC, Mayr NA, Jarjoura D, Wu D, Grecula JC, Lo SS, et al. Predicting control of primary tumor and survival by DCE MRI during early therapy in cervical cancer. Investig Radiol. 2009;44(6):343–50.CrossRefGoogle Scholar
  61. 61.
    Schwarz JK, Siegel BA, Dehdashti F, Grigsby PW. Association of posttherapy positron emission tomography with tumor response and survival in cervical carcinoma. JAMA. 2007;298(19):2289–95.PubMedCrossRefGoogle Scholar
  62. 62.
    Royal College of Radiologists. Evidence-based indications for the use of PET-CT in the United Kingdom. 2012.Google Scholar
  63. 63.
    Salem A, Salem A-F, Al-Ibraheem A, Lataifeh I, Almousa A, Jaradat I. Evidence for the use of PET for radiation therapy planning in patients with cervical cancer: a systematic review. Hematol Oncol Stem Cell Ther. 2011;4(4):173–81. Available from: PubMedCrossRefGoogle Scholar
  64. 64.
    Signorelli M, Guerra L, Montanelli L, Crivellaro C, Buda A, Dell T, et al. Preoperative staging of cervical cancer: is 18-FDG-PET/CT really effective in patients with early stage disease? Gynecol Oncol. 2011;123(2):236–40.PubMedCrossRefGoogle Scholar
  65. 65.
    Barwick TD, Taylor A, Rockall A. Functional imaging to predict tumor response in locally advanced cervical cancer. 2013; 15:549–558.Google Scholar
  66. 66.
    Yoon MS, Ahn S-J, Nah B-S, Chung W-K, Song H-C, Yoo SW, et al. Metabolic response of lymph nodes immediately after RT is related with survival outcome of patients with pelvic node-positive cervical cancer using consecutive [18F]fluorodeoxyglucose-positron emission tomography/computed tomography. Int J Radiat Oncol Biol Phys. 2012;84(4):e491–7.PubMedCrossRefGoogle Scholar
  67. 67.
    Siva S, Herschtal A, Thomas JM, Bernshaw DM, Gill S, Hicks RJ, et al. Impact of post-therapy positron emission tomography on prognostic stratification and surveillance after chemoradiotherapy for cervical cancer. Cancer. 2011;117:3981–8.Google Scholar
  68. 68.
    • Scarsbrook A, Vaidyanathan S, Chowdhury F, Swift S, Cooper R, Patel C. Efficacy of qualitative response assessment interpretation criteria at 18F-FDG PET-CT for predicting outcome in locally advanced cervical carcinoma treated with chemoradiotherapy. Eur J Nucl Med Mol Imaging. 2017;44(4):581–8. The group provides a simple 5-point visual scale when reporting FDG PET/CT post chemoradiotherapy to help predict survival outcome. PubMedCrossRefGoogle Scholar
  69. 69.
    Nakamura K, Imafuku N, Nishida T, Niwa I, Joja I, Hongo A, et al. Measurement of the minimum apparent diffusion coefficient (ADCmin) of the primary tumor and CA125 are predictive of disease recurrence for patients with endometrial cancer. Gynecol Oncol. 2012;124(2):335–9.PubMedCrossRefGoogle Scholar
  70. 70.
    • Nougaret S, Reinhold C, Alsharif SS, Addley H, Arceneau J, Molinari N, et al. Endometrial cancer: combined MR volumetry and diffusion-weighted imaging for assessment of myometrial and lymphovascular invasion and tumor grade. Radiology. 2015;276(3):797–808. Combination of endometrial tumor volume ratio and ADC can be used to predict tumor grade, lymphovascular space invasion and depth of myometrial invasion thus providing important pre-operative prognostic information. PubMedPubMedCentralCrossRefGoogle Scholar
  71. 71.
    Haldorsen IS, Gruner R, Husby JA, Magnussen IJ, Werner HM, Salvesen OO, et al. Dynamic contrast-enhanced MRI in endometrial carcinoma identifies patients at increased risk of recurrence. Eur Radiol. 2013;23(10):2916–25.PubMedCrossRefGoogle Scholar
  72. 72.
    Rockall AG. Diagnostic performance of nanoparticle-enhanced magnetic resonance imaging in the diagnosis of lymph node metastases in patients with endometrial and cervical cancer. J Clin Oncol. 2004;23(12):2813–21.CrossRefGoogle Scholar
  73. 73.
    Kakhki VRD, Shahriari S, Treglia G, Hasanzadeh M, Zakavi SR, Yousefi Z, et al. Diagnostic performance of fluorine 18 fluorodeoxyglucose positron emission tomography imaging for detection of primary lesion and staging of endometrial cancer patients: systematic review and meta-analysis of the literature. Int J Gynecol Cancer. 2013;23(9):1536–43.PubMedCrossRefGoogle Scholar
  74. 74.
    Kadkhodayan S, Shahriari S, Treglia G, Yousefi Z, Sadeghi R. Accuracy of 18-F-FDG PET imaging in the follow up of endometrial cancer patients: systematic review and meta-analysis of the literature. Gynecol Oncol. 2013;128:397–404.Google Scholar
  75. 75.
    Thomassin-Naggara I, Soualhi N, Balvay D, Darai E, Cuenod CA. Quantifying tumor vascular heterogeneity with DCE-MRI in complex adnexal masses: a preliminary study. J Magn Reson Imaging. 2017;
  76. 76.
    Bernardin L, Dilks P, Liyanage S, Miquel ME, Sahdev A, Rockall A. Effectiveness of semi-quantitative multiphase dynamic contrast-enhanced MRI as a predictor of malignancy in complex adnexal masses: radiological and pathological correlation. Eur Radiol. 2012;22(4):880–90.PubMedCrossRefGoogle Scholar
  77. 77.
    Thomassin-Naggara I, Aubert E, Rockall A, Jalaguier-Coudray A, Rouzier R, Darai E, et al. Adnexal masses: development and preliminary validation of an MR imaging scoring system. Radiology. 2013;267(2):432–43.PubMedCrossRefGoogle Scholar
  78. 78.
    • Ruiz M, Labauge P, Louboutin A, Limot O, Fauconnier A, Huchon C. External validation of the MR imaging scoring system for the management of adnexal masses. Eur J Obstet Gynecol Reprod Biol. 2016;205:115–9. External validation on 148 patients showing that the ADNEX MR imaging scoring system can accurately stratify adnexal masses into high/low risk of malignancy groups thereby allowing appropriate pre-operative counselling and surgical planning. PubMedCrossRefGoogle Scholar
  79. 79.
    Michielsen K, Vergote I, Op de Beeck K, Amant F, Leunen K, Moerman P, et al. Whole-body MRI with diffusion-weighted sequence for staging of patients with suspected ovarian cancer: a clinical feasibility study in comparison to CT and FDG-PET/CT. Eur Radiol. 2014;24(4):889–901.PubMedCrossRefGoogle Scholar
  80. 80.
    Kyriazi S, Nye E, Stamp G, Collins DJ, Kaye SB, de Souza NM. Value of diffusion-weighted imaging for assessing site-specific response of advanced ovarian cancer to neoadjuvant chemotherapy: correlation of apparent diffusion coefficients with epithelial and stromal densities on histology. Cancer Biomark. 2010;7(4):201–10.PubMedGoogle Scholar
  81. 81.
    Mitchell CL, O’Connor JP, Jackson A, Parker GJ, Roberts C, Watson Y, et al. Identification of early predictive imaging biomarkers and their relationship to serological angiogenic markers in patients with ovarian cancer with residual disease following cytotoxic therapy. Ann Oncol. 2010;21(10):1982–9.PubMedCrossRefGoogle Scholar
  82. 82.
    Sala E, Kataoka MY, Priest AN, Gill AB, McLean MA, Joubert I, et al. Advanced ovarian cancer: multiparametric MR imaging demonstrates response- and metastasis-specific effects. Radiology. 2012;263(1):149–59.PubMedCrossRefGoogle Scholar
  83. 83.
    Kitajima K, Murakami K, Yamasaki E, Kaji Y, Fukasawa I, Inaba N, et al. Diagnostic accuracy of integrated FDG-PET/contrast-enhanced CT in staging ovarian cancer: comparison with enhanced CT. Eur J Nucl Med Mol Imaging. 2008;35:1912–20.Google Scholar
  84. 84.
    Meads C, Auguste P, Davenport C, Małysiak S, Sundar S, Kowalska M, et al. Positron emission tomography/computerised tomography imaging in detecting and managing recurrent cervical cancer: systematic review of evidence, elicitation of subjective probabilities and economic modelling. Health Technol Assess (Winchester, England). 2013;17:1–323.Google Scholar
  85. 85.
    Fulham MJ, Carter J, Baldey A, Hicks RJ, Ramshaw JE, Gibson M. The impact of PET-CT in suspected recurrent ovarian cancer: a prospective multi-centre study as part of the Australian PET Data Collection Project. Gynecol Oncol. 2009;112(3):462–8.PubMedCrossRefGoogle Scholar
  86. 86.
    Gu P, Pan L-L, Wu S-Q, Sun L, Huang G. CA 125, PET alone, PET-CT, CT and MRI in diagnosing recurrent ovarian carcinoma: a systematic review and meta-analysis. Eur J Radiol. 2009;71(1):164–74.PubMedCrossRefGoogle Scholar
  87. 87.
    Höckel M, Vorndran B, Schienger K, Baußmann E, Knapstein PG, Hockel M, et al. Tumor oxygenation: a new predictive parameter in locally advanced cancer of the uterine cervix. Gynecol Oncol. 1993;51(2):141–9.PubMedCrossRefGoogle Scholar
  88. 88.
    Fyles AW, Milosevic M, Pintilie M, Hill RP. Cervix cancer oxygenation measured following external radiation therapy. In: International Journal of Radiation Oncology Biology Physics; 1998. p. 751–3.Google Scholar
  89. 89.
    Hockel M, Schlenger K, Aral B, Mitze M, Schaffer U, Vaupel P. Association between tumor hypoxia and malignant progression in advanced cancer of the uterine cervix. Cancer Res. 1996;56(19):4509–15.PubMedGoogle Scholar
  90. 90.
    Challapalli A. New radiotracers in gynecological cancer: beyond 18F-FDG. Q J Nucl Med Mol Imaging. 2016;60(2):139–53.PubMedGoogle Scholar
  91. 91.
    Schuetz M, Schmid MP, Pötter R, Kommata S, Georg D, Lukic D, et al. Evaluating repetitive 18F-fluoroazomycin-arabinoside (18FAZA) PET in the setting of MRI guided adaptive radiotherapy in cervical cancer. Acta Oncol. 2010;49(7):941–7.PubMedCrossRefGoogle Scholar
  92. 92.
    Vercellino LS, Groheux D, Delord M, Thoury A, Delpech Y, Tyslski P, et al. Hypoxia imaging of uterine cervix carcinoma with 18F-FETNIM PET. Eur J Nucl Med Mol Imaging. 2011;38(11):S120.Google Scholar
  93. 93.
    Dehdashti F, Grigsby PW, Mintun MA, Lewis JS, Siegel BA, Welch MJ. Assessing tumor hypoxia in cervical cancer by positron emission tomography with 60Cu-ATSM: relationship to therapeutic response—a preliminary report. Int J Radiat Oncol Biol Phys. 2003;55(5):1233–8.PubMedCrossRefGoogle Scholar
  94. 94.
    Dehdashti F, Grigsby PW, Lewis JS, Laforest R, Siegel BA, Welch MJ. Assessing tumor hypoxia in cervical cancer by PET with 60Cu-labeled diacetyl-bis(N4-methylthiosemicarbazone). J Nucl Med. 2008;49(2):201–5.PubMedCrossRefGoogle Scholar
  95. 95.
    Grigsby PW, Malyapa RS, Higashikubo R, Schwarz JK, Welch MJ, Huettner PC, et al. Comparison of molecular markers of hypoxia and imaging with 60Cu-ATSM in cancer of the uterine cervix. Mol Imaging Biol. 2007;9(5):278–83.PubMedCrossRefGoogle Scholar
  96. 96.
    McGuire SM, Menda Y, Boles Ponto LL, Gross B, Juweid M, Bayouth JE. A methodology for incorporating functional bone marrow sparing in IMRT planning for pelvic radiation therapy. Radiother Oncol. 2011;99(1):49–54.PubMedCrossRefGoogle Scholar
  97. 97.
    Wyss JC, Carmona R, Karunamuni RA, Pritz J, Hoh CK, Mell LK. [18F]Fluoro-2-deoxy-2-d-glucose versus 3'deoxy-3'-[(18)F]fluorothymidine for defining hematopoietically active pelvic bone marrow in gynecologic patients. Radiother Oncol. 2016;118(1):72–8.PubMedCrossRefGoogle Scholar
  98. 98.
    Richard SD, Bencherif B, Edwards RP, Elishaev E, Krivak TC, Mountz JM, et al. Noninvasive assessment of cell proliferation in ovarian cancer using [18F] 3’deoxy-3-fluorothymidine positron emission tomography/computed tomography imaging. Nucl Med Biol. 2011;38(4):485–91.PubMedCrossRefGoogle Scholar
  99. 99.
    Tsuyoshi H, Morishita F, Orisaka M, Okazawa H, Yoshida Y. 18F-fluorothymidine PET is a potential predictive imaging biomarker of the response to gemcitabine-based chemotherapeutic treatment for recurrent ovarian cancer: preliminary results in three patients. Clin Nucl Med. 2013;38(7):560–3.PubMedCrossRefGoogle Scholar
  100. 100.
    Ramírez de Molina A, Gutiérrez R, Ramos MA, Silva JM, Silva J, Bonilla F, et al. Increased choline kinase activity in human breast carcinomas: clinical evidence for a potential novel antitumor strategy. Oncogene. 2002;21(27):4317–22.PubMedCrossRefGoogle Scholar
  101. 101.
    Torizuka T, Kanno T, Futatsubashi M, Okada H, Yoshikawa E, Nakamura F, et al. Imaging of gynecologic tumors: comparison of (11)C-choline PET with (18)F-FDG PET. J Nucl Med. 2003;44(7):1051–6.PubMedGoogle Scholar
  102. 102.
    Sofue K, Tateishi U, Sawada M, Maeda T, Terauchi T, Kano D, et al. Role of carbon-11 choline PET/CT in the management of uterine carcinoma: initial experience. Ann Nucl Med. 2009;23(3):235–43.PubMedCrossRefGoogle Scholar
  103. 103.
    Linden HM, Stekhova SA, Link JM, Gralow JR, Livingston RB, Ellis GK, et al. Quantitative fluoroestradiol positron emission tomography imaging predicts response to endocrine treatment in breast cancer. J Clin Oncol. 2006;24(18):2793–9.PubMedCrossRefGoogle Scholar
  104. 104.
    Tsujikawa T, Yoshida Y, Kudo T, Kiyono Y, Kurokawa T, Kobayashi M, et al. Functional images reflect aggressiveness of endometrial carcinoma: estrogen receptor expression combined with 18F-FDG PET. J Nucl Med. 2009;50(10):1598–604.PubMedCrossRefGoogle Scholar
  105. 105.
    Tsujikawa T, Yoshida Y, Kiyono Y, Kurokawa T, Kudo T, Fujibayashi Y, et al. Functional oestrogen receptor α imaging in endometrial carcinoma using 16α-[18F]fluoro-17β-oestradiol PET. Eur J Nucl Med Mol Imaging. 2011;38(1):37–45.PubMedCrossRefGoogle Scholar
  106. 106.
    Yoshida Y, Kurokawa T, Tsujikawa T, Okazawa H, Kotsuji F. Positron emission tomography in ovarian cancer: 18F-deoxy-glucose and 16alpha-18F-fluoro-17beta-estradiol PET. J Ovarian Res. 2009;2(1):7.PubMedPubMedCentralCrossRefGoogle Scholar
  107. 107.
    van Kruchten M, de Vries EF, Arts HJ, Jager NM, Bongaerts AH, Glaudemans AW, et al. Assessment of estrogen receptor expression in epithelial ovarian cancer patients using 16alpha-18F-fluoro-17beta-estradiol PET/CT. J Nucl Med. 2015;56(1):50–5.PubMedCrossRefGoogle Scholar
  108. 108.
    Amit A, Person O, Keidar Z. FDG PET/CT in monitoring response to treatment in gynecological malignancies. Curr Opin Obstet Gynecol. 2013;25(1):17–22.PubMedCrossRefGoogle Scholar
  109. 109.
    • Grueneisen J, Schaarschmidt BM, Heubner M, Aktas B, Kinner S, Forsting M, et al. Integrated PET/MRI for whole-body staging of patients with primary cervical cancer: preliminary results. Eur J Nucl Med Mol Imaging. 2015;42(12):1814–24. Integrated PET/MR was shown to have a promising role in the T and N staging of cervical cancer in 27 patients. PubMedCrossRefGoogle Scholar
  110. 110.
    Beiderwellen K, Grueneisen J, Ruhlmann V, Buderath P, Aktas B, Heusch P, et al. [18F]FDG PET/MRI vs. PET/CT for whole-body staging in patients with recurrent malignancies of the female pelvis: initial results. Eur J Nucl Med Mol Imaging. 2015;42(1):56–65.PubMedCrossRefGoogle Scholar
  111. 111.
    Grueneisen J, Beiderwellen K, Heusch P, Gratz M, Schulze-Hagen A, Heubner M, et al. Simultaneous positron emission tomography/magnetic resonance imaging for whole-body staging in patients with recurrent gynecological malignancies of the pelvis: a comparison to whole-body magnetic resonance imaging alone. Investig Radiol. 2014;49(12):808–15.CrossRefGoogle Scholar
  112. 112.
    Grueneisen J, Beiderwellen K, Heusch P, Buderath P, Aktas B, Gratz M, et al. Correlation of standardized uptake value and apparent diffusion coefficient in integrated whole-body PET/MRI of primary and recurrent cervical cancer. PLoS One. 2014;9(5)Google Scholar
  113. 113.
    Brandmaier P, Purz S, Bremicker K, Höckel M, Barthel H, Kluge R, et al. Simultaneous [18F]FDG-PET/MRI: Correlation of apparent diffusion coefficient (ADC) and standardized uptake value (SUV) in primary and recurrent cervical cancer. PLoS One. 2015;10(11)Google Scholar
  114. 114.
    Shih IL, Yen RF, Chen CA, Bin CB, Wei SY, Chang WC, et al. Standardized uptake value and apparent diffusion coefficient of endometrial cancer evaluated with integrated whole-body PET/MR: correlation with pathological prognostic factors. J Magn Reson Imaging. 2015;42(6):1723–32.PubMedCrossRefGoogle Scholar
  115. 115.
    Mapelli P, Fallanca F, Incerti E, Gianolli L, Picchio M. PET/MRI in gynecological tumors. Clin Transl Imaging. 2016;4:211–20.Google Scholar
  116. 116.
    Kidd EA, Siegel BA, Dehdashti F, Grigsby PW. Pelvic lymph node F-18 fluorodeoxyglucose uptake as a prognostic biomarker in newly diagnosed patients with locally advanced cervical cancer. Cancer, 2010. (6):116, 1469–1175.Google Scholar
  117. 117.
    Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J. 2015;13:8–17.PubMedCrossRefGoogle Scholar
  118. 118.
    Rizzuto I, Stavraka C, Chatterjee J, Borley J, Hopkins TG, Gabra H, et al. Risk of ovarian cancer relapse score: a prognostic algorithm to predict relapse following treatment for advanced ovarian cancer. Int J Gynecol Cancer Lippincott Williams Wilkins. 2015;25(3):416–22.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Sairah R. Khan
    • 1
    Email author
  • Mubarik Arshad
    • 1
  • Kathryn Wallitt
    • 1
  • Victoria Stewart
    • 1
  • Nishat Bharwani
    • 2
    • 3
  • Tara D. Barwick
    • 1
    • 3
  1. 1.Department of RadiologyImperial College Healthcare NHS Trust, Charing Cross HospitalLondonUK
  2. 2.Department of Radiology, Imperial College Healthcare NHS TrustSt Mary’s HospitalLondonUK
  3. 3.Division of Cancer and SurgeryImperial CollegeLondonUK

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