{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T17:39:52Z","timestamp":1788802792433,"version":"build-2803163510"},"reference-count":28,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T00:00:00Z","timestamp":1737331200000},"content-version":"vor","delay-in-days":19,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003819","name":"Natural Science Foundation of Hubei Province","doi-asserted-by":"publisher","award":["2019CFB433"],"award-info":[{"award-number":["2019CFB433"]}],"id":[{"id":"10.13039\/501100003819","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Hengrui Hepatobiliary and Pancreatic Malignant Tumor Research Fund-Youth Research Fund","award":["CXPJJH11800001-2018306"],"award-info":[{"award-number":["CXPJJH11800001-2018306"]}]},{"name":"Key project of science and technology in Hubei Province","award":["2018ACA137"],"award-info":[{"award-number":["2018ACA137"]}]},{"name":"General Project of Health Commission of Hubei Province","award":["WJ2021M108"],"award-info":[{"award-number":["WJ2021M108"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>Peritoneal metastasis (PM) after the rupture of hepatocellular carcinoma (HCC) is a critical issue that negatively affects patient prognosis. Machine learning models have shown great potential in predicting clinical outcomes; however, the optimal model for this specific problem remains unclear.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Clinical data were collected and analyzed from 522 patients with ruptured HCC who underwent surgery at 7 different medical centers. Patients were assigned to the training, validation, and test groups in a random manner, with a distribution ratio of 7:1.5:1.5. Overall, 78 (14.9%) patients experienced postoperative PM. Five different types of models, including logistic regression, support vector machines, classification trees, random forests, and deep learning (DL) models, were trained using these data and evaluated based on their receiver operating characteristic curve and area under the curve (AUC) values and F1 scores.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The DL models achieved the highest AUC values (10-fold training cohort: 0.943, validation set: 0.928, and test set: 0.892) and F1 scores (10-fold training set: 0.917, validation cohort: 0.908, and test set:0.899) The results of the analysis indicate that tumor size, timing of hepatectomy, alpha-fetoprotein levels, and microvascular invasion are the most important predictive factors closely associated with the incidence of postoperative PM.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>The DL model outperformed all other machine learning models in predicting postoperative PM after the rupture of HCC based on clinical data. This model provides valuable information for clinicians to formulate individualized treatment plans that can improve patient outcomes.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/oncolo\/oyae341","type":"journal-article","created":{"date-parts":[[2025,1,20]],"date-time":"2025-01-20T15:24:46Z","timestamp":1737386686000},"source":"Crossref","is-referenced-by-count":14,"title":["Machine learning models for predicting postoperative peritoneal metastasis after hepatocellular carcinoma rupture: a multicenter cohort study in China"],"prefix":"10.1093","volume":"30","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8826-6310","authenticated-orcid":false,"given":"Feng","family":"Xia","sequence":"first","affiliation":[{"name":"Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology , Wuhan, Hubei ,","place":["People\u2019s Republic of 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China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Ren","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary Surgery, Affiliated Hospital of Qinghai University , Xining, Qinghai ,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongliang","family":"Mei","sequence":"additional","affiliation":[{"name":"Department of General Surgery, General Hospital of Central Theater Command , Wuhan, Hubei ,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology , Wuhan, Hubei ,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology , Wuhan, Hubei ,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bixiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology , Wuhan, Hubei ,","place":["People\u2019s Republic of China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8088-0615","authenticated-orcid":false,"given":"Peng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology , Wuhan, Hubei 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