Evidence map›Paper›PMID 40369381›Full record

ArticleInsights into imaging2025

MRI-based quantification of intratumoral heterogeneity for intrahepatic mass-forming cholangiocarcinoma grading: a multicenter study.

Liyong Zhuo, Wenjing Chen, Lihong Xing, Xiaomeng Li, Zijun Song, Jinghui Dong, Yanyan Zhang, Hongjun Li, Jingjing Cui, Yuxiao Han and 4 more

Abstract read
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 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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

14 authors.

Liyong Zhuo *Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China.
Wenjing Chen *Department of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, People's Republic of China.
Lihong Xing *Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
Xiaomeng LiDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
Zijun SongDepartment of Critical Care Medicine, Baoding First Central Hospital, Baoding, People's Republic of China.
Jinghui DongDepartment of Radiology, The Fifth Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China.
Yanyan ZhangDepartment of Radiology, Beijing You'an Hospital, Beijing, People's Republic of China.
Hongjun LiDepartment of Radiology, Beijing You'an Hospital, Beijing, People's Republic of China.
Jingjing CuiDepartment of Research and Development, United Imaging Intelligence (Beijing) Co., Ltd., Beijing, People's Republic of China.
Yuxiao HanDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
Jiawei HaoDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
Jianing WangDepartment of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China.
Xiaoping Yin *Department of Radiology, Affiliated Hospital of Hebei University, Baoding, People's Republic of China. yinxiaoping78@sina.com.
Caiying Li *Department of Medical Imaging, The Second Hospital of Hebei Medical University, Shijiazhuang, People's Republic of China. 26300150@hebmu.edu.cn.ORCID http://orcid.org/0009-0002-7230-8736

Funding

Baoding Science and Technology Plan 2241ZF117Hebei Major Science and Technology Support Plan 242W7706ZHebei Province High-level Talent Funding Project B20231008Hebei Province Natural Science Foundation H2024201034
6 · The paper itself

Abstract

objectiveThis study aimed to develop a quantitative approach to measure intratumor heterogeneity (ITH) using MRI scans and predict the pathological grading of intrahepatic mass-forming cholangiocarcinoma (IMCC).

methodsPreoperative MRI scans from IMCC patients were retrospectively obtained from five academic medical centers, covering the period from March 2018 to April 2024. Radiomic features were extracted from the whole tumor and its subregions, which were segmented using K-means clustering. An ITH index was derived from a habitat model integrating output probabilities of the subregions-based models. Significant variables from clinical laboratory-imaging features, radiomics, and the habitat model were integrated into a predictive model, and its performance was evaluated using the area under the receiver operating characteristic curve (AUC).

resultsThe final training and internal validation datasets included 197 patients (median age, 59 years [IQR, 52-65 years]); the external validation dataset included 43 patients (median age, 58.5 years [IQR, 52.25-69.75 years]). The habitat model achieved AUCs of 0.847 (95% CI: 0.783, 0.911) in the training set and 0.753 (95% CI: 0.595, 0.911) in the internal validation set. Furthermore, the combined model, integrating imaging variables, the habitat model, and radiomics model, demonstrated improved predictive performance, with AUCs of 0.895 (95% CI: 0.845, 0.944) in the training dataset, 0.790 (95% CI: 0.65, 0.931) in the internal validation dataset, and 0.815 (95% CI: 0.68, 0.951) in the external validation dataset.

conclusionThe combined model based on MRI-derived quantification of ITH, along with clinical, laboratory, radiological, and radiomic features, showed good performance in predicting IMCC grading. CRITICAL RELEVANCE STATEMENT: This model, integrating MRI-derived intrahepatic mass-forming cholangiocarcinoma (IMCC) classification metrics with quantitative radiomic analysis of intratumor heterogeneity (ITH), demonstrates enhanced accuracy in tumor grade prediction, advancing risk stratification for clinical decision-making in IMCC management. KEY POINTS: Grading of intrahepatic mass-forming cholangiocarcinoma (IMCC) is important for risk stratification, clinical decision-making, and personalized therapeutic optimization. Quantitative intratumor heterogeneity can accurately predict the pathological grading of IMCC. This combined model provides higher diagnostic accuracy.

Indexed as

CholangiocarcinomaHabitat imagingMagnetic resonance imagingNeoplasm gradingRadiomics

Identifiers

PMID40369381
PMCPMC12078897

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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.