{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12"],"submitter":["Li C"],"pubmed_abstract":["<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 fa"],"journal":["Frontiers in oncology"],"pagination":["816982"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9211758"],"repository":["biostudies-literature"],"pubmed_title":["Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma."],"pmcid":["PMC9211758"],"pubmed_authors":["Li C","Dong J","Gao Y","Chen Y","Wang H","Wang X","Zhu C","Wu X"],"additional_accession":[]},"is_claimable":false,"name":"Nomograms of Combining MRI Multisequences Radiomics and Clinical Factors for Differentiating High-Grade From Low-Grade Serous Ovarian Carcinoma.","description":"<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 fa","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-26T23:29:10.861Z","creation":"2025-04-06T17:33:28.151Z"},"accession":"S-EPMC9211758","cross_references":{"pubmed":["35747838"],"doi":["10.3389/fonc.2022.816982"]}}