Skip to main content
Log in

The clinical value of dynamic contrast-enhanced MRI in differential diagnosis of malignant and benign ovarian lesions

  • Research Article
  • Published:
Tumor Biology

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is widely used in preoperative diagnosis of various tumors. We investigated the clinical value of DCE-MRI in differential diagnosis of malignant and benign ovarian lesions. The study involved 48 subjects with surgical pathology-confirmed ovarian tumors with solid components. Early dynamic phase enhancement performances of the ovarian lesions in patients were assessed, including the enhancement pattern, time-signal intensity curve (TIC), signal intensity rate at the initial 60 s (SI60), time to peak within 200 s (TTP200), and slope ratio. There were significant differences in enhancement patterns between benign and malignant ovarian tumors (P < 0.05). A total of 30 malignant tumors (30/31) displayed type I TIC, 8 benign tumors (8/13) showed type III TIC, and significant differences were found in TIC type between malignant and benign ovarian lesions (P < 0.01). Benign ovarian tumors showed lower SI60 (%) and slope ratio, as well as significantly prolonged TTP20, compared to malignant ovarian tumors (all P < 0.01). The microvessel count (MVC) of malignant tumors was significantly higher than that of benign tumors (P < 0.05). Receiver operating characteristic (ROC) curve analyses revealed that DCE-MRI provided an optimal diagnostic performance with threshold values of SI60 at 83.40 %, TTP200 at 77.65 s, and slope ratio at 4.12. These findings revealed that DCE-MRI provides critical information required for differential diagnosis of malignant and benign ovarian lesions.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2013. CA Cancer J Clin. 2013;63(1):11–30.

    Article  PubMed  Google Scholar 

  2. Chornokur G, Amankwah EK, Schildkraut JM, Phelan CM. Global ovarian cancer health disparities. Gynecol Oncol. 2013;129(1):258–64.

    Article  PubMed  Google Scholar 

  3. Lowe KA, Chia VM, Taylor A, O’Malley C, Kelsh M, Mohamed M, et al. An international assessment of ovarian cancer incidence and mortality. Gynecol Oncol. 2013;130(1):107–14.

    Article  PubMed  Google Scholar 

  4. Chiang YC, Chen CA, Chiang CJ, Hsu TH, Lin MC, You SL, et al. Trends in incidence and survival outcome of epithelial ovarian cancer: 30-year national population-based registry in Taiwan. J Gynecol Oncol. 2013;24(4):342–51.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Wong KH, Mang OW, Au KH, Law SC. Incidence, mortality, and survival trends of ovarian cancer in Hong Kong, 1997 to 2006: a population-based study. Hong Kong Med J. 2012;18(6):466–74.

    CAS  PubMed  Google Scholar 

  6. Lutz AM, Willmann JK, Drescher CW, Ray P, Cochran FV, Urban N, et al. Early diagnosis of ovarian carcinoma: is a solution in sight? Radiology. 2011;259(2):329–45.

    Article  PubMed  Google Scholar 

  7. Gentry-Maharaj A, Menon U. Screening for ovarian cancer in the general population. Best Pract Res Clin Obstet Gynaecol. 2012;26(2):243–56.

    Article  PubMed  Google Scholar 

  8. Cesario S. Advances in the early detection of ovarian cancer: how to hear the whispers early. Nurs Womens Health. 2010;14(3):222–34.

    Article  PubMed  Google Scholar 

  9. Medeiros LR, Rosa DD, da Rosa MI, Bozzetti MC. Accuracy of ultrasonography with color Doppler in ovarian tumor: a systematic quantitative review. Int J Gynecol Cancer. 2009;19(7):1214–20.

    Article  PubMed  Google Scholar 

  10. Nam EJ, Yun MJ, Oh YT, Kim JW, Kim JH, Kim S, et al. Diagnosis and staging of primary ovarian cancer: correlation between PET/CT, Doppler US, and CT or MRI. Gynecol Oncol. 2010;116(3):389–94.

    Article  PubMed  Google Scholar 

  11. Yuan Y, Gu ZX, Tao XF, Liu SY. Computer tomography, magnetic resonance imaging, and positron emission tomography or positron emission tomography/computer tomography for detection of metastatic lymph nodes in patients with ovarian cancer: a meta-analysis. Eur J Radiol. 2012;81(5):1002–6.

    Article  PubMed  Google Scholar 

  12. Medeiros LR, Freitas LB, Rosa DD, Silva FR, Silva LS, Birtencourt LT, et al. Accuracy of magnetic resonance imaging in ovarian tumor: a systematic quantitative review. Am J Obstet Gynecol. 2011;204(1):67–e1-10.

    Article  PubMed  Google Scholar 

  13. Bazot M, Darai E, Nassar-Slaba J, Lafont C, Thomassin-Naggara I. Value of magnetic resonance imaging for the diagnosis of ovarian tumors: a review. J Comput Assist Tomogr. 2008;32(5):712–23.

    Article  PubMed  Google Scholar 

  14. Sala E, Rockall A, Rangarajan D, Kubik-Huch RA. The role of dynamic contrast-enhanced and diffusion weighted magnetic resonance imaging in the female pelvis. Eur J Radiol. 2010;76(3):367–85.

    Article  PubMed  Google Scholar 

  15. Heye T, Davenport MS, Horvath JJ, Feuerlein S, Breault SR, Bashir MR, et al. Reproducibility of dynamic contrast-enhanced MR imaging. Part I. Perfusion characteristics in the female pelvis by using multiple computer-aided diagnosis perfusion analysis solutions. Radiology. 2013;266(3):801–11.

    Article  PubMed  Google Scholar 

  16. Yankeelov TE, Gore JC. Dynamic contrast enhanced magnetic resonance imaging in oncology: theory, data acquisition, analysis, and examples. Curr Med Imaging Rev. 2009;3(2):91–107.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Cuenod CA, Balvay D. Perfusion and vascular permeability: basic concepts and measurement in DCE-CT and DCE-MRI. Diagn Interv Imaging. 2013;94(12):1187–204.

    Article  CAS  PubMed  Google Scholar 

  18. Tofts PS. T1-weighted DCE imaging concepts: modelling, acquisition and analysis. Signal. 2010;500(450):400.

    Google Scholar 

  19. Kyriazi S, Kaye SB, deSouza NM. Imaging ovarian cancer and peritoneal metastases—current and emerging techniques. Nat Rev Clin Oncol. 2010;7(7):381–93.

    Article  PubMed  Google Scholar 

  20. Chen W, Giger ML, Bick U, Newstead GM. Automatic identification and classification of characteristic kinetic curves of breast lesions on DCE-MRI. Med Phys. 2006;33(8):2878–87.

    Article  PubMed  Google Scholar 

  21. Do RK, Rusinek H, Taouli B. Dynamic contrast-enhanced MR imaging of the liver: current status and future directions. Magn Reson Imaging Clin N Am. 2009;17(2):339–49.

    Article  PubMed  Google Scholar 

  22. Moon M, Cornfeld D, Weinreb J. Dynamic contrast-enhanced breast MR imaging. Magn Reson Imaging Clin N Am. 2009;17(2):351–62.

    Article  PubMed  Google Scholar 

  23. Thomassin-Naggara I, Cuenod CA, Darai E, Marsault C, Bazot M. Dynamic contrast-enhanced MR imaging of ovarian neoplasms: current status and future perspectives. Magn Reson Imaging Clin N Am. 2008;16(4):661–72. ix.

    Article  PubMed  Google Scholar 

  24. Thomassin-Naggara I, Darai E, Nassar-Slaba J, Cortez A, Marsault C, Bazot M. Value of dynamic enhanced magnetic resonance imaging for distinguishing between ovarian fibroma and subserous uterine leiomyoma. J Comput Assist Tomogr. 2007;31(2):236–42.

    Article  PubMed  Google Scholar 

  25. Pannu HK, Ma W, Zabor EC, Moskowitz CS, Barakat RR, Hricak H. Enhancement of ovarian malignancy on clinical contrast enhanced MRI studies. ISRN Obstet Gynecol. 2013;2013:979345.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Glas J, Seiderer J, Bues S, Stallhofer J, Fries C, Olszak T, et al. IRGM variants and susceptibility to inflammatory bowel disease in the German population. PLoS One. 2013;8(1):e54338.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Chase DM, Sill MW, Monk BJ, Chambers MD, Darcy KM, Han ES, et al. Changes in tumor blood flow as measured by dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) may predict activity of single agent bevacizumab in recurrent epithelial ovarian (EOC) and primary peritoneal cancer (PPC) patients: an exploratory analysis of a gynecologic oncology group phase II study. Gynecol Oncol. 2012;126(3):375–80.

    Article  PubMed  Google Scholar 

  28. Priest AN, Gill AB, Kataoka M, McLean MA, Joubert I, Graves MJ, et al. Dynamic contrast-enhanced MRI in ovarian cancer: initial experience at 3 tesla in primary and metastatic disease. Magn Reson Med. 2010;63(4):1044–9.

    Article  PubMed  Google Scholar 

  29. Welti J, Loges S, Dimmeler S, Carmeliet P. Recent molecular discoveries in angiogenesis and antiangiogenic therapies in cancer. J Clin Invest. 2013;123(8):3190–200.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Yang J, Kim JH, Im GH, Heo H, Yoon S, Lee J, et al. Evaluation of antiangiogenic effects of a new synthetic candidate drug KR-31831 on xenografted ovarian carcinoma using dynamic contrast enhanced MRI. Korean J Radiol. 2011;12(5):602–10.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Dilks P, Narayanan P, Reznek R, Sahdev A, Rockall A. Can quantitative dynamic contrast-enhanced MRI independently characterize an ovarian mass? Radiology. 2008;20(9):2176–83. 32.

    Google Scholar 

  32. Thomassin-Naggara I, Bazot M, Darai E, Callard P, Thomassin J, Cuenod CA. Epithelial ovarian tumors: value of dynamic contrast-enhanced MR imaging and correlation with tumor angiogenesis. Radiology. 2008;248(1):148–59.

    Article  PubMed  Google Scholar 

  33. 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.

    Article  PubMed  Google Scholar 

  34. Thomassin-Naggara I, Darai E, Cuenod CA, Rouzier R, Callard P, Bazot M. Dynamic contrast-enhanced magnetic resonance imaging: a useful tool for characterizing ovarian epithelial tumors. J Magn Reson Imaging. 2008;28(1):111–20.

    Article  PubMed  Google Scholar 

  35. Inan N, Arslan A, Akansel G, Anik Y, Balci NC, Demirci A. Dynamic contrast enhanced MRI in the differential diagnosis of adrenal adenomas and malignant adrenal masses. Eur J Radiol. 2008;65(1):154–62.

    Article  PubMed  Google Scholar 

  36. Fukunaga T, Fujii S, Inoue C, Kato A, Chikumi J, Kaminou T, et al. Accuracy of semiquantitative dynamic contrast-enhanced mri for differentiating type II from type I endometrial carcinoma. J Magn Reson Imaging 2014. doi:10.1002/jmri.24730

  37. Chang YC, Huang YH, Huang CS, Chang PK, Chen JH, Chang RF. Classification of breast mass lesions using model-based analysis of the characteristic kinetic curve derived from fuzzy c-means clustering. Magn Reson Imaging. 2012;30(3):312–22.

    Article  PubMed  Google Scholar 

  38. Onxley JD, Yoo DS, Muradyan N, MacFall JR, Brizel DM, Craciunescu OI. Comprehensive population-averaged arterial input function for dynamic contrast-enhanced vmagnetic resonance imaging of head and neck cancer. Int J Radiat Oncol Biol Phys. 2014;89(3):658–65.

    Article  PubMed  Google Scholar 

  39. Hansford BG, Karademir I, Peng Y, Jiang Y, Karczmar G, Thomas S, et al. Dynamic contrast-enhanced MR imaging features of the normal central zone of the prostate. Acad Radiol. 2014;21(5):569–77.

    Article  PubMed  Google Scholar 

  40. Park MY, Jee WH, Kim SK, Lee SY, Jung JY. Preliminary experience using dynamic MRI at 3.0 Tesla for evaluation of soft tissue tumors. Korean J Radiol. 2013;14(1):102–9.

    Article  PubMed  Google Scholar 

  41. Poncelet E, Delpierre C, Kerdraon O, Lucot JP, Collinet P, Bazot M. Value of dynamic contrast-enhanced MRI for tissue characterization of ovarian teratomas: correlation with histopathology. Clin Radiol. 2013;68(9):909–16.

    Article  CAS  PubMed  Google Scholar 

  42. Hak S, Cebulla J, Huuse EM, Davies Cde L, Mulder WJ, Larsson HB, et al. Periodicity in tumor vasculature targeting kinetics of ligand-functionalized nanoparticles studied by dynamic contrast enhanced magnetic resonance imaging and intravital microscopy. Angiogenesis. 2014;17(1):93–107.

    Article  CAS  PubMed  Google Scholar 

  43. Padhani AR, Dzik-Jurasz A. Perfusion MR imaging of extracranial tumor angiogenesis. Top Magn Reson Imaging. 2004;15(1):41–57.

    Article  PubMed  Google Scholar 

  44. Cuenod CA, Fournier L, Balvay D, Guinebretiere JM. Tumor angiogenesis: pathophysiology and implications for contrast-enhanced MRI and CT assessment. Abdom Imaging. 2006;31(2):188–93.

    Article  CAS  PubMed  Google Scholar 

  45. Padhani AR, Leach MO. Antivascular cancer treatments: functional assessments by dynamic contrast-enhanced magnetic resonance imaging. Abdom Imaging. 2005;30(3):324–41.

    Article  CAS  PubMed  Google Scholar 

  46. Tang HS, Feng YJ, Yao LQ. Angiogenesis, vasculogenesis, and vasculogenic mimicry in ovarian cancer. Int J Gynecol Cancer. 2009;19(4):605–10.

    Article  PubMed  Google Scholar 

  47. Pickles MD, Manton DJ, Lowry M, Turnbull LW. Prognostic value of pre-treatment DCE-MRI parameters in predicting disease free and overall survival for breast cancer patients undergoing neoadjuvant chemotherapy. Eur J Radiol. 2009;71(3):498–505.

    Article  PubMed  Google Scholar 

  48. Jackson A, O’Connor JP, Parker GJ, Jayson GC. Imaging tumor vascular heterogeneity and angiogenesis using dynamic contrast-enhanced magnetic resonance imaging. Clin Cancer Res. 2007;13(12):3449–59.

    Article  PubMed  Google Scholar 

  49. Yang X, Knopp MV. Quantifying tumor vascular heterogeneity with dynamic contrast-enhanced magnetic resonance imaging: a review. J Biomed Biotechnol. 2011;2011:732848.

    PubMed  PubMed Central  Google Scholar 

  50. Leach MO, Morgan B, Tofts PS, Buckley DL, Huang W, Horsfield MA, et al. Imaging vascular function for early stage clinical trials using dynamic contrast-enhanced magnetic resonance imaging. Eur Radiol. 2012;22(7):1451–64.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

We would like to acknowledge the helpful comments on this paper received from our reviewers.

Conflicts of interest

None

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bin Zhao.

Additional information

Xian Li and Jun-Li Hu are both considered as first authors.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, X., Hu, JL., Zhu, LM. et al. The clinical value of dynamic contrast-enhanced MRI in differential diagnosis of malignant and benign ovarian lesions. Tumor Biol. 36, 5515–5522 (2015). https://doi.org/10.1007/s13277-015-3219-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13277-015-3219-3

Keywords

Navigation