Evidence map›Paper›PMID 41159056›Full record

ArticleJHEP reports : innovation in hepatology2025

Anatomical resection is associated with reduced early recurrences in HCC: A machine learning analysis.

Xiang-Pan Meng, Fang-Ming Chen, Tian-Yu Tang, Tianyi Xia, Qian Yu, Ben Zhao, Xiaodie Qu, Yongping Zhou, Yibing Shi, Xueying Long and 4 more

Abstract read
In one paragraph

Article in JHEP reports : innovation in hepatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the 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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

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

14 authors.

Xiang-Pan MengNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, China.
Fang-Ming ChenDepartment of Radiology, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, China.
Tian-Yu TangNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, China.
Tianyi XiaNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, China.
Qian YuNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, China.
Ben ZhaoNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, China.
Xiaodie QuDepartment of Pathology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, 299 Qingyang Road, Wuxi, China.
Yongping ZhouDepartment of Hepatobiliary Surgery, Jiangnan University Medical Center, 68 Zhongshan Road, Wuxi, China.
Yibing ShiDepartment of Radiology, The Affiliated Xuzhou Center Hospital of Southeast University, 199 Jiefang South Road, Xuzhou, China.
Xueying LongDepartment of Radiology, The Xiangya Hospital of Central South University, 87 Xiangya Road, Changsha, China.
Jitao WangDepartment of Hepatobiliary Surgery, Xingtai Institute of Cancer Control, The Affiliated Xingtai People's Hospital of Hebei Medical University, 16 Hongxing Street, Xingtai, China.
Wenbo XiaoDepartment of Radiology, The First Affiliated Hospital, College of Medicine, 79 Qingchun Road, Zhejiang University, Hangzhou, China.
Xiangming FangDepartment of Radiology, The Affiliated Wuxi People's Hospital of Nanjing Medical University, Wuxi People's Hospital, Wuxi Medical Center, Nanjing Medical University, 299 Qingyang Road, Wuxi, China.
Shenghong JuNurturing Center of Jiangsu Province for State Laboratory of AI Imaging & Interventional Radiology, Department of Radiology, Zhongda Hospital, Medical School of Southeast University, 87 Dingjiaqiao Road, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background & Aims: Anatomical resection (AR) for hepatocellular carcinoma (HCC) could improve micrometastasis eradication and reduce their recurrence, but conflicting findings suggest heterogeneous treatment effects (HTEs). This study investigated the heterogeneity in the association between AR and decreased postoperative HCC recurrence. Methods: Between January 2014 and December 2021, preoperative imaging and clinicopathologic data from 1,859 patients with HCC were retrospectively reviewed from seven hospitals in China. Using data from six centers (the development set), causal forest modeling was performed to estimate subgroups with different HTEs after propensity score matching (PSM) for multiple clinical-radiological covariates. Differences in early recurrence risk (within 2 years) and recurrence-free survival (RFS) between AR and non-AR were evaluated in identified subgroups. Data from the remaining center were treated as the validation set. Results: The development set included 1,496 patients, of whom 1,266 were identified after PSM; the validation set included 363 patients. The causal forest model identified tumor size ≤5 cm and non-smooth tumor margins on preoperative imaging as effect modifiers distinguishing those who benefited differentially from AR in the development set. AR achieved a 16.76% absolute reduction in early recurrence risk (95% CI, 3.39-29.48) compared with non-AR in patients with tumors ≤5 cm and non-smooth margins (393/1,266; 31.0%), along with improved RFS (hazard ratio [NAR Conclusions: HTEs of AR occurred in patients with single HCC. Tumor size ≤5 cm and non-smooth tumor margin might could be used to identify patients with HCC who are likely to derive benefit from AR. Impact and implications: Causal forest modeling enables the detection of HTEs of AR in patients with HCC based on multiple clinical-radiological characteristics. Radiological tumor size ≤5 cm and non-smooth tumor margins were identified as significant modifiers and should be taken into consideration when selecting between AR and non-AR. Based on these findings, over 30% of the study population who underwent non-AR might have experienced a reduced risk of early recurrence and improved RFS if AR had been performed, thereby supporting the importance of personalized selection of the optimal hepatectomy type in patients with HCC.

Indexed as

Hepatocellular carcinomaHeterogeneity of treatment effectsLiver resectionMachine learningRecurrence

Identifiers

PMID41159056
PMCPMC12557568

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Registered trials

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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.