Evidence map›Paper›PMID 41399475›Full record

ArticleEClinicalMedicine2025

Liver regeneration-associated machine learning architecture integrating time-phased predictions for post-hepatectomy liver failure.

Hao Shen, Tao Yuan, Anfeng Si, Yihang Shen, Jinhuan Liu, Lv Jin, Zhihao Xie, Huayi Zhang, Wenxin Wei, Yizhe Dai and 11 more

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in EClinicalMedicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05779098 (A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data), which is not on this map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT05779098 completednot on this map

A Machine Learning Architecture to Predict Post-Hepatectomy Liver Failure Using Liver Regeneration Biomarkers and Time-Phased Data

TypeobservationalSponsorShen FengRan2023 to 2025Enrolled1,071ConditionsLiver Failure After Operative Procedure
3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

21 authors.

Hao ShenDepartment of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
Tao YuanDepartment of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
Anfeng SiDepartment of General Surgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Yihang ShenSchool of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, China.
Jinhuan LiuThe First Clinical Medical College, Wenzhou Medical University, Wenzhou, Zhejiang, China.
Lv JinClinical Research Institute, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Zhihao XieClinical Research Institute, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Huayi ZhangSchool of Life Sciences, Shanghai University, Shanghai, China.
Wenxin WeiDepartment of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Yizhe DaiDepartment of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Tao JiangDepartment of General Surgery, Jinling Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing, China.
Chenxiang HeDepartment of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
Shichao ZhangClinical Research Institute, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Yuheng HuDepartment of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
Shengyu HuangDepartment of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.
Zhishi YangDepartment of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Yao ChenInternational Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Xiaofeng ZhangDepartment of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Feng ShenClinical Research Institute, Eastern Hepatobiliary Surgery Hospital and National Centre for Liver Cancer, Naval Medical University, Shanghai, China.
Xiaolong QiState Key Laboratory of Digital Medical Engineering, Department of Radiology, Zhongda Hospital, Southeast University, Nanjing, China.
Jun LiDepartment of Hepatobiliary and Pancreatic Surgery, Tenth People's Hospital of Tongji University, School of Medicine, Tongji University, Shanghai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Post-hepatectomy liver failure (PHLF) is the leading cause of morbidity and mortality following major hepatectomy. Existing prediction models inadequately 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-associated biomarkers with time-phased perioperative data for PHLF prediction. Methods: This retrospective multicentre study included 1071 patients undergoing major hepatectomy at three centres (2019-2024), divided into training (n = 623) and two external validation cohorts (n = 206 and 242). Fifty-five perioperative variables, including novel liver regeneration-associated biomarkers (GATA3, RAMP2, VEGFA, PEDF), were categorised into three time-phased datasets (preoperative, intraoperative, postoperative). Thirteen ML algorithms were evaluated across these datasets, with gradient-based feature reduction strategies applied to optimise the PILOT models. This study is registered with ClinicalTrials.gov (NCT05779098). Findings: PILOT-Pre, PILOT-Intra (LightGBM with 10 and 15 features, respectively), and PILOT-Post (XGBoost with 20 features) models showed superior discrimination in training (AUCs: 0.754 [95% CI: 0.717-0.790], 0.787 [0.728-0.846], 0.904 [0.883-0.924]) and validation cohorts (AUCs: 0.740-0.895) compared to traditional models (AUCs: 0.502-0.644; all Interpretation: The PILOT architecture integrates liver regeneration-associated biomarkers with time-phased data to accurately predict PHLF within the first 6 h postoperatively. Based on consistency analysis of predictions of PILOT-Pre and PILOT-Intra models, this framework enables early risk stratification, thereby providing a practical tool for personalised perioperative management. Funding: This research was funded by the projects from National Natural Science Foundation of China (82403243), Program for National Postdoctoral Researchers Funding of China (GZC20231943), and Shanghai Municipal Commission of Science and Technology (23Y11905900).

Indexed as

BiomarkersLiver regenerationMachine learningPost-hepatectomy liver failureRisk prediction

Identifiers

PMID41399475
PMCPMC12702303

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.