Evidence map›Paper›PMID 40232657›Full record

ArticleLa Radiologia medica2025

Enhancing hepatocellular carcinoma diagnosis in non-high-risk patients: a customized ChatGPT model integrating contrast-enhanced ultrasound.

Meng-Fei Xian, Wen-Tong Lan, Zhe Zhang, Ming-De Li, Xin-Xin Lin, Yang Huang, Hui Huang, Li-Da Chen, Qing-Hua Huang, Wei Wang

Abstract read
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In one paragraph

Article in La Radiologia medica, 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. [Research progress of large language models in tumor diagnosis: applications in textual reports and medical imaging].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
    Review
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

10 authors.

Meng-Fei Xian *Department of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Laboratory, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
Wen-Tong Lan *Department of Endoscopy Center, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
Zhe ZhangDepartment of Biomedical Engineering, College of Engineering and Applied Sciences, State University of New York at Stony Brook, Stony Brook, NY, USA.
Ming-De LiDepartment of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Laboratory, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
Xin-Xin LinDepartment of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Laboratory, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
Yang HuangDepartment of Vascular Surgery, National-Guangdong Joint Engineering Laboratory for Diagnosis and Treatment of Vascular Diseases, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510080, China.
Hui HuangDepartment of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Laboratory, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
Li-Da ChenDepartment of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Laboratory, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China.
Qing-Hua HuangSchool of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University, Xian, 710072, China. qhhuang@nwpu.edu.cn.
Wei WangDepartment of Medical Ultrasonics, Ultrasomics Artificial Intelligence X-Laboratory, Institute of Diagnostic and Interventional Ultrasound, The First Affiliated Hospital of Sun Yat-Sen University, 58 Zhongshan Road 2, Guangzhou, 510080, China. wangw73@mail.sysu.edu.cn.ORCID http://orcid.org/0000-0002-9485-583X

Funding

Basic and Applied Basic Research Foundation of Guangdong Province 2023A04J2231Guangdong Provincial Natural Science Foundation, General Program 2022A1515011148Guangzhou Basic and Applied Basic Research Project 2023A04J2230National Natural Science Foundation of China, Tianyuan Project in Mathematics 12326609
6 · The paper itself

Abstract

purposeThis study aims to improve hepatocellular carcinoma (HCC) diagnostic accuracy in non-high-risk populations by utilizing GPTs that incorporate integrated risk coefficients, and to explore its feasibility. MATERIAL AND

methodsBetween August 2016 and June 2019, patients with focal liver lesions (FLLs) in non-high-risk populations, confirmed by histopathology or clinical/imaging evidence, were retrospectively included. A logistic regression model was developed using baseline characteristics and contrast-enhanced ultrasound (CEUS) features to identify independent HCC risk factors. Three ChatGPT-based models were evaluated: ChatGPT 4o (a general-purpose model developed by OpenAI), BaseGPT (a customized model with HCC diagnostic knowledge), and RiskGPT (a further customized model integrating HCC knowledge and identified risk factors). Their intra-agreement and diagnostic performance were compared.

resultsLogistic regression identified male, obesity, HBcAb or HBeAb positivity, elevated alpha-fetoprotein, and mild washout on CEUS as associated with HCC. RiskGPT achieved the highest area under a receiver operating characteristic curve (AUC) (0.89) and demonstrated superior accuracy (90.3%) in HCC identification; significantly outperforming both ChatGPT 4o (AUC 0.79, P = 0.002; accuracy 83.1%, P = 0.02) and BaseGPT (AUC 0.81, P = 0.008; accuracy 80.6%, P = 0.002). RiskGPT demonstrated superior sensitivity compared to ChatGPT 4o (85.5% vs. 66.3%) and outperformed BaseGPT in specificity (92.7% vs. 80.6%) and positive predictive value (85.5% vs. 67.7%) (all P < 0.001). Additionally, RiskGPT showed substantial intra-consistency in diagnosing FLLs, with a κ value of 0.78.

conclusionRiskGPT improves HCC diagnostic accuracy in non-high-risk patients by integrating clinical, imaging features, and risk coefficients, demonstrating significant diagnostic potential.

Indexed as

Carcinoma, HepatocellularContrast MediaLiver NeoplasmsAdultAgedFemaleGenerative Artificial IntelligenceHumansLogistic ModelsMaleMiddle AgedRetrospective StudiesRisk FactorsUltrasonographyContrast MediaChatGPTContrast-enhanced ultrasoundFocal liver lesionsHepatocellular carcinomaLarge language models

Identifiers

PMID40232657

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.