{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Luo Y"],"funding":["Special Fund for Tumor Minimally Invasive Diagnosis and Treatment Translational Medicine Innovation Platform","National Natural Science Foundation of China","Nurture Program of Zhuhai People's Hospital"],"pagination":["81"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12390902"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["9(1)"],"pubmed_abstract":["The onset of liver-related events (LREs) in fibrosis indicates a poor prognosis and worsens patients' quality of life, making the prediction and early detection of LREs crucial. The aim of this study was to develop a radiomics model using liver magnetic resonance imaging (MRI) to predict LRE risk in patients undergoing antiviral treatment for chronic fibrosis caused by hepatitis B virus (HBV). Patients with HBV-associated liver fibrosis and liver stiffness measurements ≥ 10 kPa were included. Feature selection and dimensionality reduction techniques identified discriminative features from three MRI sequences. Radiomics models were built using eight machine learning techniques and evaluated for performance. Shapley additive explanation and permutation importance techniques were applied to i"],"journal":["European radiology experimental"],"pubmed_title":["MRI-based machine-learning radiomics of the liver to predict liver-related events in hepatitis B virus-associated fibrosis."],"pmcid":["PMC12390902"],"funding_grant_id":["22207040","2021PY-02","2021PY-01"],"pubmed_authors":["Ren H","Liu X","Luo Q","Li Y","Wu Q","Luo Y","Wang X","Yang Q","Zhang S","Xu W","Wu Y"],"additional_accession":[]},"is_claimable":false,"name":"MRI-based machine-learning radiomics of the liver to predict liver-related events in hepatitis B virus-associated fibrosis.","description":"The onset of liver-related events (LREs) in fibrosis indicates a poor prognosis and worsens patients' quality of life, making the prediction and early detection of LREs crucial. The aim of this study was to develop a radiomics model using liver magnetic resonance imaging (MRI) to predict LRE risk in patients undergoing antiviral treatment for chronic fibrosis caused by hepatitis B virus (HBV). Patients with HBV-associated liver fibrosis and liver stiffness measurements ≥ 10 kPa were included. Feature selection and dimensionality reduction techniques identified discriminative features from three MRI sequences. Radiomics models were built using eight machine learning techniques and evaluated for performance. Shapley additive explanation and permutation importance techniques were applied to i","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-10T04:53:19.331Z","creation":"2026-04-08T01:32:19.915Z"},"accession":"S-EPMC12390902","cross_references":{"pubmed":["40864355"],"doi":["10.1186/s41747-025-00602-0"]}}