Quantitative analysis of ovarian cysts and tumors by using T2 star mapping

J Obstet Gynaecol Res. 2020 Jan;46(1):140-146. doi: 10.1111/jog.14157. Epub 2019 Nov 19.

Abstract

Aim: The aim of this study was to investigate the efficacy of T2 star (T2*) mapping in diagnosing ovarian cysts/ tumors.

Methods: Pelvic magnetic resonance examinations including T2*WI were performed before surgery in 35 patients. The region of interest, consisted of a 10 mm2 diameter circle, was set as much as possible inside ovarian tumors/cysts to measure T2*values, and mean T2* values were compared in ovarian cyst/tumor types, retrospectively. Diagnoses of 40 ovarian cysts/tumors were determined by pathological reports, in which 17 were endometriomas, 13 were mature cystic teratomas, 6 were mucinous cystadenomas and 4 were serous cystadenomas.

Results: The average T2* values of endometrioma was 56.8 ± 8.7 ms (mean ± SEM), which was significantly lower than that of mucinous cystadenoma (334.2 ± 58.5 ms, mean ± SEM) or serous cystadenoma (237.0 ± 45.4 ms, mean ± SEM). There was no difference in T2* values between endometrioma and mature cystic teratoma (64.1 ± 22.6 ms, mean ± SEM). Receiver operating characteristics curve analysis revealed that optimal cut-off value for differential diagnosis of endometrioma and mucinous or serous cystadenoma was 149.2 ms as T2* value, which has an area under the curve of 0.95 (sensitivity = 92.4%, specificity = 78.6%).

Conclusion: T2* values were useful to diagnose various types of ovarian cyst/tumor.

Keywords: T2 star-weighted MR imaging; endometrioma; endometriosis; mucinous cystadenoma.

Publication types

  • Evaluation Study

MeSH terms

  • Adult
  • Area Under Curve
  • Cystadenoma, Serous / diagnosis*
  • Dermoid Cyst / diagnosis*
  • Diagnosis, Differential
  • Endometriosis / diagnosis*
  • Female
  • Humans
  • Magnetic Resonance Imaging / methods
  • Magnetic Resonance Imaging / statistics & numerical data*
  • Middle Aged
  • Ovarian Cysts / diagnosis*
  • Ovarian Neoplasms / diagnosis*
  • Reproducibility of Results
  • Retrospective Studies
  • Sensitivity and Specificity