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| Name | Class |
|---|---|
| Shanghai 10th People's Hospital | OTHER |
| Jinling Hospital, China | OTHER |
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Post-hepatectomy liver failure (PHLF) is the leading cause of morbidity and mortality following major hepatectomy. Existing prediction models fail to capture the dynamic liver regeneration and perioperative changes, limiting their predictive accuracy. We aimed to develop a machine learning (ML) modelling system (PILOT architecture) integrating liver regeneration biomarkers with time-phased perioperative clinical data to accurately predict PHLF risk.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Extensive hepatectomy |
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| Measure | Description | Time Frame |
|---|---|---|
| Postoperative liver failure | 1-5 days after surgery |
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Inclusion Criteria:
Extensive hepatectomy in our hospital（≥ three Hepatic segment）
Exclusion Criteria:
Serious basic diseases Intolerable surgery Refuse to perform ICG test before operation
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Patients with Extensive Hepatology in our hospital
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Department of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China | Shanghai | 200092 | China |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 41399475 | Derived | Shen H, Yuan T, Si A, Shen Y, Liu J, Jin L, Xie Z, Zhang H, Wei W, Dai Y, Jiang T, He C, Zhang S, Hu Y, Huang S, Yang Z, Chen Y, Zhang X, Shen F, Qi X, Li J. Liver regeneration-associated machine learning architecture integrating time-phased predictions for post-hepatectomy liver failure. EClinicalMedicine. 2025 Nov 28;90:103661. doi: 10.1016/j.eclinm.2025.103661. eCollection 2025 Dec. |
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