{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li X"],"funding":["Chongqing medical scientific research project","Chongqing medical scientific research project (Joint project of Chongqing Health Commission and Science and Technology Bureau)"],"pagination":["111"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11977955"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["25(1)"],"pubmed_abstract":["<h4>Objectives</h4>To explore the value of a T1 mapping-based radiomic model for evaluating liver function.<h4>Methods</h4>From September 2020 to October 2022, 163 patients were retrospectively recruited and categorized into normal liver function group, chronic liver disease group without cirrhosis, Child‒Pugh class A group, and Child‒Pugh class B and C group. Patients were randomly split into training and testing sets. Radiomic features were extracted from T1 mapping images taken both pre- and post-contrast injection, as well as during the hepatobiliary phase (HBP). Radiomic models were constructed to stratify chronic liver disease, cirrhosis and decompensated cirrhosis. Model performance was assessed with receiver operating characteristic curve analysis, and decision curve analysis.<h4>R"],"journal":["BMC medical imaging"],"pubmed_title":["Radiomic analysis using T1 mapping in gadoxetic acid disodium-enhanced MRI for liver function assessment."],"pmcid":["PMC11977955"],"funding_grant_id":["2022ZDXM026","2023QNXM029"],"pubmed_authors":["Ai G","Li X","Liu Y","Qiao X","He X","Chen W","Guo D","Fan Q","Wang Y","Chen T"],"additional_accession":[]},"is_claimable":false,"name":"Radiomic analysis using T1 mapping in gadoxetic acid disodium-enhanced MRI for liver function assessment.","description":"<h4>Objectives</h4>To explore the value of a T1 mapping-based radiomic model for evaluating liver function.<h4>Methods</h4>From September 2020 to October 2022, 163 patients were retrospectively recruited and categorized into normal liver function group, chronic liver disease group without cirrhosis, Child‒Pugh class A group, and Child‒Pugh class B and C group. Patients were randomly split into training and testing sets. Radiomic features were extracted from T1 mapping images taken both pre- and post-contrast injection, as well as during the hepatobiliary phase (HBP). Radiomic models were constructed to stratify chronic liver disease, cirrhosis and decompensated cirrhosis. Model performance was assessed with receiver operating characteristic curve analysis, and decision curve analysis.<h4>R","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-06T03:04:27.009Z","creation":"2025-07-06T03:04:27.009Z"},"accession":"S-EPMC11977955","cross_references":{"pubmed":["40197206"],"doi":["10.1186/s12880-025-01658-5"]}}