{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T20:19:32Z","timestamp":1788293972586,"version":"build-2803163510"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T00:00:00Z","timestamp":1773273600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shihezi University Research Projec","award":["ZZZC2023049"],"award-info":[{"award-number":["ZZZC2023049"]}]},{"name":"National Science and Technology Major Project for Noncommunicable Chronic Diseases","award":["2023ZD0502001"],"award-info":[{"award-number":["2023ZD0502001"]}]},{"name":"First Affiliated Hospital of Shihezi University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Cancers"],"abstract":"<jats:p>Background\/Objectives: Post-hepatectomy liver failure is one of the most serious complications after liver resection for hepatocellular carcinoma and is associated with high morbidity and mortality. Traditional clinical scoring systems and statistical models provide limited predictive accuracy. This study aimed to develop and internally validate a deep learning model for predicting post-hepatectomy liver failure using multicenter clinical data. Methods: A retrospective cohort of 498 patients from six centers undergoing curative-intent liver resection for hepatocellular carcinoma was analyzed. Preoperative biochemical parameters, intraoperative surgical variables, and tumor-related characteristics were incorporated into a deep neural network, with logistic regression as a baseline comparator. Data splitting was performed before preprocessing, and imputation\/scaling parameters were fitted on the training set only to prevent information leakage. Discrimination was assessed using the area under the receiver operating characteristic curve (AUC) and precision\u2013recall (PR) curves; calibration was assessed using calibration plots and Brier score; and clinical utility was assessed using decision curve analysis (DCA). A sensitivity analysis using a preoperative-only feature set (excluding intraoperative variables) was also conducted. SHapley Additive exPlanations were used to determine variable importance. Results: The deep learning model achieved AUCs of 0.914, 0.892, and 0.906 in the training, validation, and test sets, outperforming logistic regression (0.782, 0.757, and 0.773). Key predictors included ALBI and MELD scores, prothrombin time, intraoperative blood loss, and resection extent. Calibration and decision curve analysis further supported the robustness and clinical utility of the model. Conclusions: The deep learning model provides improved predictive performance for post-hepatectomy liver failure compared with logistic regression in an internally validated multicenter cohort and may support perioperative risk stratification and surgical planning.<\/jats:p>","DOI":"10.3390\/cancers18060926","type":"journal-article","created":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:46:31Z","timestamp":1773326791000},"page":"926","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Development and Internal Multicenter Validation of a Deep Learning Model for Predicting Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma: A Multicenter Study"],"prefix":"10.3390","volume":"18","author":[{"given":"Qian","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Hepatobiliary Surgery, The First Affiliated Hospital of Shihezi University, Shihezi 832000, China"},{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Guo","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhicheng","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xulin","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chang","family":"Shu","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Yan","sequence":"additional","affiliation":[{"name":"Department of Ultrasound in Medicine, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou 310009, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhancheng","family":"Qiu","sequence":"additional","affiliation":[{"name":"Department of General Surgery, West China Hospital, Sichuan University, Chengdu 610041, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Hepatic Surgery, Zhongshan People\u2019s Hospital Affiliated to Guangdong Medical University, Zhongshan 528403, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhenheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary Surgery, Fujian Medical University Union Hospital, Fuzhou 350001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiyuan","family":"Huang","sequence":"additional","affiliation":[{"name":"Department of General Surgery, General Hospital of Central Theater Command, Wuhan 430000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoping","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1609-7260","authenticated-orcid":false,"given":"Bixiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Hepatobiliary and Pancreatic Surgery, Tongji Hospital, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430023, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1016\/j.hbpd.2023.03.001","article-title":"Post-Hepatectomy Liver Failure: A Timeline Centered Review","volume":"22","author":"Bekheit","year":"2023","journal-title":"Hepatobiliary Pancreat. Dis. Int."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"216","DOI":"10.1016\/j.ejso.2020.09.001","article-title":"Post Hepatectomy Liver Failure (PHLF)\u2014Recent Advances in Prevention and Clinical Management","volume":"47","author":"Deshpande","year":"2021","journal-title":"Eur. J. Surg. Oncol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"4254","DOI":"10.3748\/wjg.v30.i39.4254","article-title":"Current Strategies for Predicting Post-Hepatectomy Liver Failure and a New Ultrasound-Based Nomogram","volume":"30","author":"Gao","year":"2024","journal-title":"World J. Gastroenterol."},{"key":"ref_4","first-page":"281","article-title":"Prediction and Prevention of Post-Hepatectomy Liver Failure: Where Do We Stand?","volume":"9","author":"Hanafy","year":"2021","journal-title":"J. Clin. Transl. Hepatol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"24","DOI":"10.3348\/kjr.2023.0507","article-title":"Evaluation and Prediction of Post-Hepatectomy Liver Failure Using Imaging Techniques: Value of Gadoxetic Acid-Enhanced Magnetic Resonance Imaging","volume":"25","author":"Sofue","year":"2024","journal-title":"Korean J. Radiol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"100347","DOI":"10.1016\/j.jhepr.2021.100347","article-title":"ALBI Grade: Evidence for an Improved Model for Liver Functional Estimation in Patients with Hepatocellular Carcinoma","volume":"3","author":"Demirtas","year":"2021","journal-title":"JHEP Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3734","DOI":"10.12998\/wjcc.v7.i22.3734","article-title":"End-Stage Liver Disease Score and Future Liver Remnant Volume Predict Post-Hepatectomy Liver Failure in Hepatocellular Carcinoma","volume":"7","author":"Kong","year":"2019","journal-title":"World J. Clin. Cases"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2868","DOI":"10.1245\/s10434-020-08239-6","article-title":"Post Hepatectomy Liver Failure Risk Calculator for Preoperative and Early Postoperative Period Following Major Hepatectomy","volume":"27","author":"Liu","year":"2020","journal-title":"Ann. Surg. Oncol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.12998\/wjcc.v9.i8.1793","article-title":"Risk Factors for Post-Hepatectomy Liver Failure in 80 Patients","volume":"9","author":"Xing","year":"2021","journal-title":"World J. Clin. Cases"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Madden, W.G., Jin, W., Lopman, B., Zufle, A., Dalziel, B., Metcalf, E.C.J., Grenfell, B.T., and Lau, M.S.Y. (2024). Deep Neural Networks for Endemic Measles Dynamics: Comparative Analysis and Integration with Mechanistic Models. PLoS Comput. Biol., 20.","DOI":"10.1101\/2024.05.28.24307979"},{"key":"ref_11","first-page":"14","article-title":"Introduction to Machine Learning, Neural Networks, and Deep Learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1161\/CIRCULATIONAHA.115.001593","article-title":"Machine Learning in Medicine","volume":"132","author":"Deo","year":"2015","journal-title":"Circulation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"oyae341","DOI":"10.1093\/oncolo\/oyae341","article-title":"Machine Learning Models for Predicting Postoperative Peritoneal Metastasis after Hepatocellular Carcinoma Rupture: A Multicenter Cohort Study in China","volume":"30","author":"Xia","year":"2025","journal-title":"Oncol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1007\/s10877-022-00908-z","article-title":"A Machine Learning Approach to Predicting Early and Late Postoperative Reintubation","volume":"37","author":"Koretsky","year":"2023","journal-title":"J. Clin. Monit. Comput."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e212240","DOI":"10.1001\/jamanetworkopen.2021.2240","article-title":"Use of Machine Learning to Develop and Evaluate Models Using Preoperative and Intraoperative Data to Identify Risks of Postoperative Complications","volume":"4","author":"Xue","year":"2021","journal-title":"JAMA Netw. Open"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1097\/RLU.0000000000001913","article-title":"Prediction of Posthepatectomy Liver Failure Proposed by the International Study Group of Liver Surgery: Residual Liver Function Estimation With 99mTc-Galactosyl Human Serum Albumin Scintigraphy","volume":"43","author":"Mizutani","year":"2018","journal-title":"Clin. Nucl. Med."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"713","DOI":"10.1016\/j.surg.2010.10.001","article-title":"Posthepatectomy Liver Failure: A Definition and Grading by the International Study Group of Liver Surgery (ISGLS)","volume":"149","author":"Rahbari","year":"2011","journal-title":"Surgery"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.neunet.2022.11.035","article-title":"Early Stopping by Correlating Online Indicators in Neural Networks","volume":"159","year":"2023","journal-title":"Neural Netw."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"108627","DOI":"10.1016\/j.jmgm.2023.108627","article-title":"Overcoming Class Imbalance in Drug Discovery Problems: Graph Neural Networks and Balancing Approaches","volume":"126","author":"Almeida","year":"2024","journal-title":"J. Mol. Graph. Model."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"120652","DOI":"10.1016\/j.saa.2021.120652","article-title":"A Variable Selection Method Based on Mutual Information and Variance Inflation Factor","volume":"268","author":"Cheng","year":"2022","journal-title":"Spectrochim. Acta A Mol. Biomol. Spectrosc."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1038\/s41467-020-20655-6","article-title":"Deep Learning Encodes Robust Discriminative Neuroimaging Representations to Outperform Standard Machine Learning","volume":"12","author":"Abrol","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1002\/jso.25184","article-title":"Comparison of the Ability of Child-Pugh Score, MELD Score, and ICG-R15 to Assess Preoperative Hepatic Functional Reserve in Patients with Hepatocellular Carcinoma","volume":"118","author":"Wang","year":"2018","journal-title":"J. Surg. Oncol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1159\/000486590","article-title":"A Comparative Study of Albumin-Bilirubin Score with Child-Pugh Score, Model for End-Stage Liver Disease Score and Indocyanine Green R15 in Predicting Posthepatectomy Liver Failure for Hepatocellular Carcinoma Patients","volume":"36","author":"Zou","year":"2018","journal-title":"Dig. Dis."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"8034","DOI":"10.1038\/s41598-024-58205-5","article-title":"ALBI Score Combined with FIB-4 Index to Predict Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma","volume":"14","author":"Tian","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1323","DOI":"10.2147\/JHC.S451025","article-title":"Utility of Machine Learning in the Prediction of Post-Hepatectomy Liver Failure in Liver Cancer","volume":"11","author":"Tashiro","year":"2024","journal-title":"J. Hepatocell. Carcinoma"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, J., Zheng, T., Liao, Y., Geng, S., Li, J., Zhang, Z., Shang, D., Liu, C., Yu, P., and Huang, Y. (2022). Machine Learning Prediction Model for Post- Hepatectomy Liver Failure in Hepatocellular Carcinoma: A Multicenter Study. Front. Oncol., 12.","DOI":"10.3389\/fonc.2022.986867"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7682","DOI":"10.1109\/TNNLS.2021.3086323","article-title":"Deep Neural Network-Embedded Stochastic Nonlinear State-Space Models and Their Applications to Process Monitoring","volume":"33","author":"Wang","year":"2022","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.asjsur.2022.05.001","article-title":"A Nomogram for Predicting Post-Hepatectomy Liver Failure in Patients with Hepatocellular Carcinoma Based on Spleen-Volume-to-Platelet Ratio","volume":"46","author":"Meng","year":"2023","journal-title":"Asian J. Surg."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1430","DOI":"10.1016\/j.dld.2019.04.006","article-title":"Platelet-Albumin-Bilirubin Grade: Risk Stratification of Liver Failure, Prognosis after Resection for Hepatocellular Carcinoma","volume":"51","author":"Lu","year":"2019","journal-title":"Dig. Liver Dis."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2640","DOI":"10.1007\/s11605-023-05834-2","article-title":"Postoperative Liver Failure: Definitions, Risk Factors, Prediction Models and Prevention Strategies","volume":"27","author":"Merath","year":"2023","journal-title":"J. Gastrointest. Surg."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00423-020-01956-2","article-title":"Post-Hepatectomy Liver Failure after Hepatic Resection for Hepatocellular Carcinoma: A Single Center Experience","volume":"406","author":"Shehta","year":"2021","journal-title":"Langenbecks Arch. Surg."}],"container-title":["Cancers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/6\/926\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:12:50Z","timestamp":1773328370000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-6694\/18\/6\/926"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,12]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,3]]}},"alternative-id":["cancers18060926"],"URL":"https:\/\/doi.org\/10.3390\/cancers18060926","relation":{},"ISSN":["2072-6694"],"issn-type":[{"value":"2072-6694","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,12]]}}}