Evidence map›Paper›PMID 40547169›Full record

ArticleWorld journal of gastrointestinal oncology2025

Preoperative model for predicting early recurrence in hepatocellular carcinoma patients using radiomics and deep learning: A multicenter study.

Yong-Hai Li, Gui-Xiang Qian, Ling Yao, Xue-Di Lei, Yu Zhu, Lei Tang, Zi-Ling Xu, Xiang-Yi Bu, Ming-Tong Wei, Jian-Lin Lu and 1 more

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Article in World journal of gastrointestinal oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

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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

11 authors.

Yong-Hai LiCheeloo College of Medicine, Shandong University, Jinan 250021, Shandong Province, China.
Gui-Xiang QianThe First People's Hospital of Hefei, The Third Affiliated Hospital of Anhui Medical University, Hefei 230001, Anhui Province, China.
Ling YaoDepartment of Anorectal, Chinese Academy of Traditional Chinese Medicine, Xiyuan Hospital, Beijing 100091, China.
Xue-Di LeiDepartment of Colorectal Surgery, Bengbu Medical University, Bengbu 233000, Anhui Province, China.
Yu ZhuDepartment of Hepatopancreatobiliary Surgery, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Taizhou 318000, Zhejiang Province, China.
Lei TangDepartment of Infectious Disease, The Second Hospital of Anhui Medical Univercity, Hefei 230001, Anhui Province, China.
Zi-Ling XuDepartment of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, Anhui Province, China.
Xiang-Yi BuDivision of Life Science and Medicine, Department of Hepatic Surgery, Anhui Provincial Hospital, The First Affiliated Hospital of University of Science and Technology of China, University of Science and Technology of China, Hefei 230001, Anhui Province, China.
Ming-Tong WeiDepartment of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, Anhui Province, China.
Jian-Lin LuDepartment of Hepatic Surgery, Anhui Provincial Hospital Affiliated to Anhui Medical University, Hefei 230001, Anhui Province, China.
Wei-Dong JiaCheeloo College of Medicine, Shandong University, Jinan 250021, Shandong Province, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundHepatocellular carcinoma (HCC) is the most common primary liver malignancy. Ablation therapy is one of the first-line treatments for early HCC. Accurately predicting early recurrence (ER) is crucial for making precise treatment plans and improving patient prognosis.

aimTo establish an intratumoral and peritumoral model for predicting ER in HCC patients following curative ablation.

methodsThis study included a total of 288 patients from three Centers. The patients were divided into a primary cohort (

resultsThe combined model, which utilizes the light gradient boosting machine learning algorithm and incorporates both intratumoral and peritumoral regions (5 mm and 10 mm), demonstrated the best predictive performance for ER following HCC ablation, achieving AUCs of 0.924 in the training set, 0.899 in the internal validation set, and 0.839 in the external validation set. Calibration and DCA curves confirmed strong calibration and clinical utility, whereas K-M curves provided risk stratification for PFS and OS in HCC patients.

conclusionThe most efficient model integrated the tumor region with the peritumoral 5 mm and 10 mm regions. This model provides a noninvasive, effective, and reliable method for predicting ER after curative ablation of HCC.

Indexed as

AblationDeep learningEarly recurrenceHepatocellular carcinomaPeritumoralRadiomics

Identifiers

PMID40547169
PMCPMC12179936

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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.