Abstract #2422

Magnetic resonance imaging radiomics to differentiate ovarian sex cord-stromal tumors and primary epithelial ovarian cancers

Cheng Meiying¹, Tan Shifang¹, Ren Tian², Zhu Zitao³, Wang Kaiyu⁴, Zhang Lingjie¹, Meng Lingsong¹, Yang Xuhong⁵, Yang Zhexuan¹, and Zhao Xin¹

¹Department of Radiology, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ²Department of Information, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China, ³Wuhan University, Wuhan, China, ⁴MR Research China, GE Healthcare, Beijing, China, ⁵Huiying Medical Technology, Beijing, China

Synopsis

Keywords: Machine Learning/Artificial Intelligence, UrogenitalOvarian sex cord-stromal tumors (SCSTs) are rare nonepithelial neoplasms that usually are benign or at early stages, but sometimes they are confused with malignant tumors such as epithelial ovarian cancers (EOCs). We constructed five models including clinical model, conventional MR model, traditional model, radiomics model and mixed model based on logistic regression classifier to distinguish SCSTs and EOCs. The performance of each model was evaluated. The radiomics approach showed excellent prediction results, and the mixed model stood out among all the models.

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