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Combination of clinical and MRI features in diagnosing ovarian granulosa cell tumor: A comparison with other ovarian sex cord-gonadal stromal tumors

医学 颗粒细胞 间质细胞 卵巢 肿瘤科 妇科 病理 内科学
作者
Jing Wang,Shuyang Yao,Youmin Guo
出处
期刊:European Journal of Radiology [Elsevier]
卷期号:158: 110593-110593
标识
DOI:10.1016/j.ejrad.2022.110593
摘要

PurposeTo evaluate the combination of magnetic resonance imaging (MRI) findings and clinical features in diagnosing ovarian granulosa cell tumor (OGCT) and comparing OGCTs with other ovarian sex cord-gonadal stromal tumors (OSGTs).MethodsWomen who underwent MRI and were surgically confirmed with OSGTs between January 2015 and January 2022 were included in the study. Histology was used as a primary method of diagnosis. T1WI, T2WI, and DWI MR scans were performed for all patients. All MR images were reviewed by two radiologists. The clinic baseline characteristics of all patients were recorded.ResultsA total of 58 patients were enrolled, with 21 OGCTs found in 20 patients and 39 other OSGTs found in 38 patients. In terms of clinical, the proportion of vaginal discharge/bleeding and menstrual abnormalities were significantly higher in OGCTs than in the control group. A multivariate analysis of the combined clinical MRI revealed that symptomatic, T2 signals of the solid component, Honeycomb-sign, Swiss cheese-sign, and ADC values were independent features for discriminating between OGCTs and other OSGTs. Clinical features, MRI features, and a combined model were established; the areas under the curve of the three models in predicting OGCTs and other OSGTs were 0.694, 0.852, and 0.927, respectively. The DeLong test showed that the combined model had the highest efficiency in predicting OGCTs (p < 0.05), which was significantly different from the AUC of the other two models (p < 0.05).ConclusionsCombining clinic and MRI findings helps differentiate OGCTs from other OSGTs. These results help optimize clinical management and indicate that radiologists should focus on clinical information to help improve diagnostic accuracy.
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