Diagnostics (May 2024)

MR Relaxometry for Discriminating Malignant Ovarian Cystic Tumors: A Prospective Multicenter Cohort Study

  • Naoki Kawahara,
  • Hiroshi Kobayashi,
  • Tomoka Maehana,
  • Kana Iwai,
  • Yuki Yamada,
  • Ryuji Kawaguchi,
  • Junko Takahama,
  • Nagaaki Marugami,
  • Hirotaka Nishi,
  • Yosuke Sakai,
  • Hirokuni Takano,
  • Toshiyuki Seki,
  • Kota Yokosu,
  • Yukihiro Hirata,
  • Koyo Yoshida,
  • Takafumi Ujihira,
  • Fuminori Kimura

DOI
https://doi.org/10.3390/diagnostics14111069
Journal volume & issue
Vol. 14, no. 11
p. 1069

Abstract

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Background: Endometriosis-associated ovarian cancer (EAOC) is a well-known type of cancer that arises from ovarian endometrioma (OE). OE contains iron-rich fluid in its cysts due to repeated hemorrhages in the ovaries. However, distinguishing between benign and malignant tumors can be challenging. We conducted a retrospective study on magnetic resonance (MR) relaxometry of cyst fluid to distinguish EAOC from OE and reported that this method showed good accuracy. The purpose of this study is to evaluate the accuracy of a non-invasive method in re-evaluating pre-surgical diagnosis of malignancy by a prospective multicenter cohort study. Methods: After the standard diagnosis process, the R2 values were obtained using a 3T system. Data on the patients were then collected through the Case Report Form (CRF). Between December 2018 and March 2023, six hospitals enrolled 109 patients. Out of these, 81 patients met the criteria required for the study. Results: The R2 values calculated using MR relaxometry showed good discriminating ability with a cut-off of 15.74 (sensitivity 80.6%, specificity 75.0%, AUC = 0.750, p < 0.001) when considering atypical or borderline tumors as EAOC. When atypical and borderline cases were grouped as OE, EAOC could be distinguished with a cut-off of 16.87 (sensitivity 87.0%, specificity 61.1%). Conclusions: MR relaxometry has proven to be an effective tool for discriminating EAOC from OE. Regular use of this method is expected to provide significant insights for clinical practice.

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