{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T22:39:23Z","timestamp":1779403163907,"version":"3.53.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686615","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,21]]},"abstract":"<jats:p>Liver cancer is a leading cause of cancer mortality; hepatocellular carcinoma (HCC), its predominant form, requires accurate survival prediction to guide prognosis and treatment decisions. We propose a multimodal framework for discrete-time survival prediction using histopathological and clinical data from the TCGA-LIHC cohort. Patch-level features extracted from Whole-Slide Images using the UNI2-h foundation model were aggregated within each slide through a Graph Attention Network, while clinical variables were encoded with a sentence transformer. The resulting embeddings were fused through intermediate fusion strategies and processed by a multi-layer perceptron trained using a negative discrete-time log-likelihood loss to handle censored data. Across two clinically meaningful survival intervals, [0, 1) and [1, 5] years, multimodal fusion outperformed unimodal base-lines, with concatenation achieving the highest overall performance (AUROC = 0.818). These findings highlight the complementary prognostic value of histology and clinical data for robust and interpretable survival modeling in HCC.<\/jats:p>","DOI":"10.3233\/shti260182","type":"book-chapter","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:57:35Z","timestamp":1779400655000},"source":"Crossref","is-referenced-by-count":0,"title":["Multimodal Graph-Based Model for Discrete-Time Survival Prediction in Liver Cancer"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-3879-876X","authenticated-orcid":false,"given":"Hafsa","family":"Akebli","sequence":"first","affiliation":[{"name":"University of Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0144-3802","authenticated-orcid":false,"given":"Vincenzo","family":"Della Mea","sequence":"additional","affiliation":[{"name":"University of Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9191-3280","authenticated-orcid":false,"given":"Kevin","family":"Roitero","sequence":"additional","affiliation":[{"name":"University of Udine, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Opening the Personal Gate between Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260182","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:57:36Z","timestamp":1779400656000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260182"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,21]]},"ISBN":["9781643686615"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260182","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,21]]}}}