biostudies-literatureUnknown12Li C<h4>Objective</h4>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).<h4>Methods</h4>In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (<i>P</i> < 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 multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical faFrontiers in oncology816982https://www.ebi.ac.uk/biostudies/studies/S-EPMC9211758biostudies-literatureNomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma.PMC9211758Li CDong JGao YChen YWang HWang XZhu CWu XfalseNomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma.<h4>Objective</h4>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).<h4>Methods</h4>In this retrospective analysis, 138 SOC patients were confirmed by histology. Significant clinical factors (<i>P</i> < 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 multisequence model was established. A total of 1,316 radiomics features of each sequence were extracted; the univariate and multivariate logistic regressions, cross-validations were performed to reduce valueless features and then radiomics signatures were developed. Nomogram models using clinical fa2022-01-01T00:00:00Z20222025-04-26T23:29:10.861Z2025-04-06T17:33:28.151ZS-EPMC92117583574783810.3389/fonc.2022.816982