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    The purpose of this study is to compare ultrasonographic ovarian mass scoring systems in pregnant women. This multicenter study included women with an ovarian mass during pregnancy who were evaluated using ultrasound and underwent surgery in 11 referral hospitals. The ovarian mass was evaluated and scored using three different scoring systems(International Ovarian Tumor Analysis Assessment of Different NEoplasias in the adnexa[IOTA ADNEX], Sassone, and Lerner). The final diagnosis was made histopathologically. Receiver operating characteristic(ROC) curves were generated for each scoring system. During the study period, 236 pregnant women underwent surgery for an ovarian mass, including 223 women(94.5%) with a benign ovarian mass and 13 women(5.5%) with a malignant ovarian mass. Among 10 ultrasound image findings, six findings were different between benign and ovarian masses(maximal diameter of mass, maximal diameter of solid mass, wall thickness of mass, inner wall structure, thickness of septations, and papillarity). In all three scoring systems, the ovarian mass scores were significantly higher in malignant masses than in benign masses, with the highest area under the ROC curve(AUROC) in the Sassone scoring system(AUROC: 0.831 for Sassone, 0.710 for Lerner vs 0.709 for IOTA ADNEX; p < 0.05, between the Sassone and Lerner/ IOTA ADNEX). A combined model was developed with the six different ultrasound findings, and the AUROC of the combined model was 0.883(p = not significant between the combined model and Sassone). In pregnant women, malignant ovarian tumors can be predicted with high accuracy using either the Sassone scoring system or the combined model. Copyright © 2021. Published by Elsevier Inc.


    Se Jin Lee, Young-Han Kim, Mi-Young Lee, Hyun Sun Ko, Soo-Young Oh, Hyun-Joo Seol, Jong Woon Kim, Ki Hoon Ahn, Sunghun Na, Won Joon Seong, Hee Seung Kim, Chan-Wook Park, Joong Shin Park, Jong Kwan Jun, Hye-Sung Won, Moon Young Kim, Han Sung Hwang, Seung Mi Lee. Ultrasonographic evaluation of ovarian mass for predicting malignancy in pregnant women. Gynecologic oncology. 2021 Nov;163(2):385-391

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    PMID: 34561098

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