{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wang T"],"funding":["AI for Science Foundation of Fudan University","Shanghai Explorer Program","Key Project of National Natural Science Foundation of China","National Natural Science Foundation of China"],"pagination":["27"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12944185"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["12(2)"],"pubmed_abstract":["<b>Background/Objectives</b>: To investigate and develop a non-invasive parametric radiomics model derived from dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) kinetics for predicting breast cancer molecular subtypes (HR+/HER2-, HER2+ and triple-negative breast cancer). <b>Methods</b>: This multicenter retrospective study enrolled 935 female patients with histologically confirmed breast cancer who underwent pretreatment breast DCE-MRI from August 2017 to July 2022. Based on the wash-in rate (WIR) and the area under the TIC, the original multiphase DCE-MRI images were converted into two types of parametric images. Radiomics features were extracted from TIC-WIR and TIC-Area images and analyzed using low variance filtering, the elimination of highly correlated features, and"],"journal":["Tomography (Ann Arbor, Mich.)"],"pubmed_title":["Dynamic Contrast-Enhanced MRI Kinetic Curve-Driven Parametric Radiomics for Predicting Breast Cancer Molecular Subtypes: A Multicenter and Interpretable Study."],"pmcid":["PMC12944185"],"funding_grant_id":["FudanX24AI069","82271957 and 82572174","24TS1411300","82430061"],"pubmed_authors":["Zhang D","Gu Y","Wang S","Wang T","Sun S","You C","Gong J","Lin L","Zhou J"],"additional_accession":[]},"is_claimable":false,"name":"Dynamic Contrast-Enhanced MRI Kinetic Curve-Driven Parametric Radiomics for Predicting Breast Cancer Molecular Subtypes: A Multicenter and Interpretable Study.","description":"<b>Background/Objectives</b>: To investigate and develop a non-invasive parametric radiomics model derived from dynamic contrast-enhanced MRI (DCE-MRI) time-intensity curve (TIC) kinetics for predicting breast cancer molecular subtypes (HR+/HER2-, HER2+ and triple-negative breast cancer). <b>Methods</b>: This multicenter retrospective study enrolled 935 female patients with histologically confirmed breast cancer who underwent pretreatment breast DCE-MRI from August 2017 to July 2022. Based on the wash-in rate (WIR) and the area under the TIC, the original multiphase DCE-MRI images were converted into two types of parametric images. Radiomics features were extracted from TIC-WIR and TIC-Area images and analyzed using low variance filtering, the elimination of highly correlated features, and","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-11T03:16:17.068Z","creation":"2026-07-11T03:11:51.318Z"},"accession":"S-EPMC12944185","cross_references":{"pubmed":["41745704"],"doi":["10.3390/tomography12020027"]}}