Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma - Li, Cuiping; Wang, Hongfei; Chen, Yulan; Zhu, Chao; Gao, Yankun; Wang, Xia; Dong, Jiangning; Wu, Xingwang (Frontiers in Oncology, 2022)
Objective: To compare the performance of clinical factors, FS-T2WI, DWI, T1WI+C based radiomics, and a combined clinic-radiomics model in predicting the type of serous ovarian carcinomas (SOCs). Methods: In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (P < 0.05, and with the area under the curve (AUC) > 0.7) was retained to establish a clinical model. The radiomics model included FS-T2WI, DWI, and T1WI+C, and also, a multisequences model was established. A total of 1316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions and cross-validations were performed to reduce valueless features, then radiomics signatures were developed respectively. Nomogram models using clinical factors combined with radiomics features were developed in the training cohort. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the clinical model in identifying low- and high-grade SOC. Results: The AUC of the clinical model and multisequences radiomics model in the training and validation cohorts was 0.90 and 0.89, 0.91 and 0.86, respectively. By incorporating clinical factors and multi-radiomics signature, the AUC of the radiomic-clinical nomogram in the training and validation cohorts was 0.98 and 0.95. The model comparison results show that the AUC of the combined model is higher than that of the uncombined models (P= 0.05, 0.002). Conclusion: The nomogram models of clinical factors combined with MRI multisequences radiomics signatures can help identifying low- and high-grade SOCs and provide a more comprehensive, effective method to evaluate preoperative risk stratification for SOCs.