AI Article Synopsis

  • Magnetic resonance (MR) relaxometry is a noninvasive method with high accuracy for distinguishing endometriosis-associated ovarian cancer (EAOC) from ovarian endometrioma (OE), though it has limitations in clinical practice.
  • This study analyzed data from 150 patients over 10 years, focusing on factors like patient age, tumor size, and R2 value as predictors of malignancy.
  • The newly developed endometriotic neoplasm algorithm for risk assessment (e-NARA) index demonstrated high sensitivity (85.7%) and specificity (87.0%), proving to be a reliable tool for evaluating the risk of malignant transformation in endometriomas.

Article Abstract

Background: Magnetic resonance (MR) relaxometry provides a noninvasive tool to discriminate endometriosis-associated ovarian cancer (EAOC) from ovarian endometrioma (OE) with high accuracy. However, this method has a limitation in discriminating malignancy in clinical use because the R2 value depends on the device manufacturer and repeated imaging is unrealistic. The current study aimed to reassess the diagnostic accuracy of MR relaxometry and investigate a more powerful tool to distinguish EAOC from OE.

Methods: This retrospective study was conducted at our institution from December, 2012, to May, 2022. A total of 150 patients were included in this study. Patients with benign ovarian tumors ( = 108) mainly received laparoscopic surgery, and cases with suspected malignancy ( = 42) underwent laparotomy. Information from a chart review of the patients' medical records was collected.

Results: A multiple regression analysis revealed that the age, the tumor diameter, and the R2 value were independent malignant predicting factors. The endometriotic neoplasm algorithm for risk assessment (e-NARA) index provided high accuracy (sensitivity, 85.7%; specificity, 87.0%) to discriminate EAOC from OE.

Conclusions: The e-NARA index is a reliable tool to assess the probability of malignant transformation of endometrioma.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9687708PMC
http://dx.doi.org/10.3390/biomedicines10112683DOI Listing

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