{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T01:03:41Z","timestamp":1755219821803,"version":"3.43.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"type":"electronic","value":"9781643686080"}],"license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"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":[[2025,8,7]]},"abstract":"<jats:p>Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide, ranking fourth in cancer-related mortality. Prognostic risk prediction for HCC patients can optimize treatment strategies, assess therapeutic efficacy, and ultimately improve post-operative survival rates. Pathological slides are considered the gold standard for cancer diagnosis and prognosis, playing a crucial role in prognostic risk stratification. However, in addition to pathological slides, clinical text information also contains significant prognostic value that cannot be ignored.To address this, we propose a text-driven multimodal fusion prognostic GCN. The core of the model is to integrate imaging and textual features by constructing a patient graph driven by text, overcoming the significant disparity between the two modalities. Validation results on the FAH-ZJUMS dataset show that our method improves performance by 21% compared to imaging-only prognostic models. These results demonstrate the great potential of this multimodal fusion approach in handling complex medical information.<\/jats:p>","DOI":"10.3233\/shti250907","type":"book-chapter","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:46Z","timestamp":1754566486000},"source":"Crossref","is-referenced-by-count":0,"title":["Utilization of Whole Slide Pathological Images and Tumor Clinical Data: A Hybrid Graph Convolutional Network Model for Hepatocellular Carcinoma Prognosis Prediction"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-7130-3372","authenticated-orcid":false,"given":"Boyang","family":"Deng","sequence":"first","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6791-8217","authenticated-orcid":false,"given":"Yu","family":"Tian","sequence":"additional","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0516-8008","authenticated-orcid":false,"given":"Qi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5858-6353","authenticated-orcid":false,"given":"Tianshu","family":"Zhou","sequence":"additional","affiliation":[{"name":"Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou 311100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-9428-2433","authenticated-orcid":false,"given":"Yangyang","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8688-4707","authenticated-orcid":false,"given":"Shang","family":"Yao","sequence":"additional","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0143-3353","authenticated-orcid":false,"given":"Tingbo","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou 310003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1064-637X","authenticated-orcid":false,"given":"Jingsong","family":"Li","sequence":"additional","affiliation":[{"name":"Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou 310027, China"},{"name":"Research Center for Data Hub and Security, Zhejiang Lab, Hangzhou 311100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","MEDINFO 2025 \u2014 Healthcare Smart \u00d7 Medicine Deep"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI250907","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T11:34:46Z","timestamp":1754566486000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI250907"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,7]]},"ISBN":["9781643686080"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti250907","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"type":"print","value":"0926-9630"},{"type":"electronic","value":"1879-8365"}],"subject":[],"published":{"date-parts":[[2025,8,7]]}}}