{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15"],"submitter":["Fu H"],"pubmed_abstract":["<h4>Background</h4>Microvascular invasion (MVI) is a critical determinant of poor prognosis in hepatocellular carcinoma (HCC). Accurate preoperative prediction of MVI is essential for optimizing surgical and therapeutic strategies. This study aims to develop a combined model integrating intratumoral, peritumoral, and clinical features from ultrasound-based radiomics for MVI prediction.<h4>Methods</h4>Ultrasound images of 119 patients with pathologically confirmed HCC were analyzed. A total of 1,414 radiomics features were extracted from intratumoral and peritumoral regions. Feature selection was performed using intraclass correlation coefficient (ICC) analysis, t-tests, and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression, Random Forest, and other mac"],"journal":["Frontiers in oncology"],"pagination":["1566105"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12433787"],"repository":["biostudies-literature"],"pubmed_title":["Integrating intratumoral, peritumoral, and clinical features in an ultrasound-based radiomics model: contributions and synergies for predicting microvascular invasion in hepatocellular carcinoma."],"pmcid":["PMC12433787"],"pubmed_authors":["Xu L","Qian H","Huang Y","Fu H","Zhou D","Hou C","Yu J","Lu B"],"additional_accession":[]},"is_claimable":false,"name":"Integrating intratumoral, peritumoral, and clinical features in an ultrasound-based radiomics model: contributions and synergies for predicting microvascular invasion in hepatocellular carcinoma.","description":"<h4>Background</h4>Microvascular invasion (MVI) is a critical determinant of poor prognosis in hepatocellular carcinoma (HCC). Accurate preoperative prediction of MVI is essential for optimizing surgical and therapeutic strategies. This study aims to develop a combined model integrating intratumoral, peritumoral, and clinical features from ultrasound-based radiomics for MVI prediction.<h4>Methods</h4>Ultrasound images of 119 patients with pathologically confirmed HCC were analyzed. A total of 1,414 radiomics features were extracted from intratumoral and peritumoral regions. Feature selection was performed using intraclass correlation coefficient (ICC) analysis, t-tests, and least absolute shrinkage and selection operator (LASSO) regression. Logistic regression, Random Forest, and other mac","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-06-01T15:47:22.205Z","creation":"2026-04-08T13:50:59.714Z"},"accession":"S-EPMC12433787","cross_references":{"pubmed":["40958856"],"doi":["10.3389/fonc.2025.1566105"]}}