Evidence map›Paper›PMID 40813736›Full record

ArticleInsights into imaging2025

Preoperative ternary classification using DCE-MRI radiomics and machine learning for HCC, ICC, and HIPT.

Peng Xie, Zhong-Jian Liao, Lu Xie, Junyuan Zhong, Xiaodong Zhang, Wei Yuan, Yujin Yin, Tianxian Chen, Huizhen Lv, Xinglin Wen and 2 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Insights into imaging, 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. 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

12 authors.

Peng Xie *Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Zhong-Jian Liao *Department of Nuclear Medicine, Ganzhou People's Hospital, Ganzhou, China.
Lu XieFuzhou Medical College of Nanchang University, Fuzhou, China.
Junyuan ZhongGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Xiaodong ZhangGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Wei YuanGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Yujin YinGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Tianxian ChenGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Huizhen LvGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Xinglin WenGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Xiaochun WangGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China.
Ling ZhangGanzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Department of Medical Imaging, Ganzhou People's Hospital, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, China. zhangling_smu@163.com.ORCID http://orcid.org/0000-0002-2942-8022

Funding

the science and technology plan project of Ganzhou 2022--RC1339the science and technology plan project of Ganzhou 2023LNS17497
6 · The paper itself

Abstract

objectivesThis study develops a machine learning model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) radiomics and clinical data to preoperatively differentiate hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and hepatic inflammatory pseudotumor (HIPT), addressing limitations of conventional diagnostics. MATERIALS AND

methodsThis retrospective study included 280 patients (HCC = 160, ICC = 80, HIPT = 40) who underwent DCE-MRI from 2008 to 2024 at three hospitals. Radiomics features and clinical data were extracted and analyzed using LASSO regression and machine learning algorithms (Logistic Regression, Random Forest, and Extreme Gradient Boosting), with class weighting (HCC:ICC:HIPT = 1:2:4) to address class imbalance. Models were compared using macro-average Area Under the Curve (AUC), accuracy, recall, and precision.

resultsThe fusion model, integrating radiomics and clinical features, achieved an AUC of 0.933 (95% CI: 0.91-0.95) and 84.5% accuracy, outperforming radiomics-only (AUC = 0.856, 72.6%) and clinical-only (AUC = 0.795, 66.7%) models (p < 0.05). Rim enhancement is a key model feature for distinguishing HCC from ICC and HIPT, while hepatic lobe atrophy distinguishes ICC and HIPT from HCC.

conclusionThis study developed a novel preoperative imaging-based model to differentiate HCC, ICC, and HIPT. The fusion model performed exceptionally well, demonstrating superior accuracy in ICC identification, significantly outperforming traditional diagnostic methods (e.g., radiology and biomarkers) and single-modality machine learning models (p < 0.05). This noninvasive approach enhances diagnostic precision and supports personalized treatment planning in liver disease management. CRITICAL RELEVANCE STATEMENT: This study develops a novel preoperative imaging-based machine learning model to differentiate hepatocellular carcinoma (HCC), intrahepatic cholangiocarcinoma (ICC), and hepatic inflammatory pseudotumor (HIPT), improving diagnostic accuracy and advancing personalized treatment strategies in clinical radiology. KEY POINTS: A machine learning model integrates DCE-MRI radiomics and clinical data for liver lesion differentiation. The fusion model outperforms single-modality models with 0.933 AUC and 84.5% accuracy. This model provides a noninvasive, reliable tool for personalized liver disease diagnosis and treatment planning.

Indexed as

Hepatocellular carcinomaInflammatory pseudotumorIntrahepatic cholangiocarcinomaMachine learningRadiomics

Identifiers

PMID40813736
PMCPMC12354431

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.