Evidence map›Paper›PMID 40597066›Full record

ArticleBMC cancer2025

A machine learning model based on CT radiomics for preoperatively differentiating intrahepatic mass-type cholangiocarcinoma and inflammatory pseudotumours.

Xiao-Chun Wang, Jing-Hong Liang, Xiao-Yao Huang, Wen-Jian Tang, Yan-Mei He, Jun-Yuan Zhong, Ling Zhang, Lun Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Xiao-Chun Wang *Department of Medical Imaging, Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, 341000, China.
Jing-Hong Liang *Department of Medical Imaging, Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, 341000, China.
Xiao-Yao Huang *Department of Medical Imaging, Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, 341000, China.
Wen-Jian TangDepartment of Medical Imaging, Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, 341000, China.
Yan-Mei HeDepartment of Medical Imaging, Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, 341000, China.
Jun-Yuan ZhongDepartment of Medical Imaging, Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, 341000, China.
Ling ZhangDepartment of Medical Imaging, Ganzhou Institute of Medical Imaging, Ganzhou Key Laboratory of Medical Imaging and Artificial Intelligence, Medical Imaging Center, Ganzhou People's Hospital, The Affiliated Ganzhou Hospital of Nanchang University, Ganzhou Hospital-Nanfang Hospital, Southern Medical University, Ganzhou, 341000, China. zhangling_smu@163.com.
Lun LuDepartment of Radiology, Shanghai Eastern Hepatobiliary Surgery Hospital, Second Military Medical University, Shanghai, 200438, China. lunlu79083@163.com.

Funding

the Science and Technology Plan Project of Ganzhou 2022- -RC1339the Science and Technology Plan Project of Ganzhou 2022-ZD1373
6 · The paper itself

Abstract

objectiveIntrahepatic cholangiocarcinoma (ICC) and hepatic inflammatory pseudotumours (IPTs) share similar imaging features, leading to unnecessary biopsies and surgeries. Accurate preoperative differentiation is essential. Current studies using traditional imaging analysis have limited accuracy. We developed a machine learning model based on clinical and CT radiomic features to improve diagnostic accuracy.

methodsFrom May 2008 to January 2024, the data of 112 patients with ICC and 34 patients with hepatic IPTs who underwent preoperative plain and enhanced CT scans and whose diseases were confirmed by surgery and pathology were retrospectively analysed. A radiomic feature set, a clinical feature set, and a radiomic + clinical feature set were developed, and each was used to construct 14 machine learning models. The optimal hyperparameters were identified using fivefold cross-validation and a grid search. Finally, the area under the curve (AUC), accuracy, recall, precision, F1, Kappa value and other indicators were used to evaluate the performance of the models in the test sets to determine the optimal model for each feature subset.

resultsThe machine learning model constructed with the radiomic features of all the CT sequences and the fused model constructed with both clinical features + all the CT sequence radiomic features performed well (AUC = 0.91 and 0.97, respectively), whereas the performance of the machine learning model constructed with the clinical features alone was relatively poor (AUC = 0.73). In terms of model performance in identifying the two diseases, the accuracy of the fused model was better in identifying ICCs than in identifying IPTs.

conclusionA diagnostic model constructed from clinical and CT radiomic features quickly differentiated between IPT from ICC. The model may be helpful for the preoperative identification of IPTs and ICC.

Indexed as

Bile Duct NeoplasmsCholangiocarcinomaGranuloma, Plasma CellMachine LearningTomography, X-Ray ComputedAdultAgedDiagnosis, DifferentialFemaleHumansMaleMiddle AgedRadiomicsRetrospective StudiesCTICCIPTsMachine learningRadiomics

Identifiers

PMID40597066
PMCPMC12211452

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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.