The Endometriotic Neoplasm Algorithm for Risk Assessment (e-NARA) Index Sheds Light on the Discrimination of Endometriosis-Associated Ovarian Cancer from Ovarian Endometrioma

Overview

Journal Biomedicines

Specialty Biomedical Engineering

Date 2022 Nov 11

PMID 36359203

Authors

Naoki Kawahara

Ryuji Kawaguchi

Tomoka Maehana

Shoichiro Yamanaka

Yuki Yamada

Hiroshi Kobayashi

Fuminori Kimura

Affiliations

Soon will be listed here.

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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