{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["12(10)"],"submitter":["Xia X"],"pubmed_abstract":["The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = "],"journal":["Diagnostics (Basel, Switzerland)"],"pagination":["2446"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC9600299"],"repository":["biostudies-literature"],"pubmed_title":["Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer."],"pmcid":["PMC9600299"],"pubmed_authors":["Xia X","Nie S","Gou Q","Li D","Du W","Wang Y","Tan Q"],"additional_accession":[]},"is_claimable":false,"name":"Radiomics Based on Nomogram Predict Pelvic Lymphnode Metastasis in Early-Stage Cervical Cancer.","description":"The accurate prediction of the status of PLNM preoperatively plays a key role in treatment strategy decisions in early-stage cervical cancer. The aim of this study was to develop and validate a radiomics-based nomogram for the preoperative prediction of pelvic lymph node metastatic status in early-stage cervical cancer. One hundred fifty patients were enrolled in this study. Radiomics features were extracted from T2-weighted MRI imaging (T2WI). Based on the selected features, a support vector machine (SVM) algorithm was used to build the radiomics signature. The radiomics-based nomogram was developed incorporating radiomics signature and clinical risk factors. In the training cohort (AUC = 0.925, accuracy = 81.6%, sensitivity = 70.3%, and specificity = 92.0%) and the testing cohort (AUC = ","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022 Oct","modification":"2025-04-03T21:29:29.755Z","creation":"2025-04-03T21:29:29.755Z"},"accession":"S-EPMC9600299","cross_references":{"pubmed":["36292135"],"doi":["10.3390/diagnostics12102446"]}}