Keywords: Machine Learning/Artificial Intelligence, Radiomics, Ovarian carcinoma, Residual tumor predictionResidual tumor (RT) status is associated with the prognosis and survival rate of patients with high-grade serous ovarian carcinoma (HGSOC). However, current RT status prediction approach through laparoscopy has disadvantages of invasiveness, high cost and incidence of tumor metastases. In this study, we proposed a radiomic-clinical nomogram, based on multiple-sequence MRI combined with score of abdominal metastases and clinical markers, for preoperative prediction of RT status. We demonstrated that the radiomic-clinical nomogram had satisfactory prediction performance in all cohorts (AUC = 0.900-0.936). The clinical application value of the nomogram was further confirmed by decision curves.
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