Evidence map›Paper›PMID 42597372›Full record

ArticleFrontiers in oncology2026

Comparative multiphase CT radiomics for differentiating isolated hepatic metastasis from intrahepatic cholangiocarcinoma: a two-center study of native, subtraction, peritumoral, and fusion models.

Yanyan Zhou, Qianlian Wu, Hejia Zhang, Peng Chen, Chengmeng Zhang, Jian Shen, Junmei Wang, Chuanxian Liu, Zhimin Ding, Liangshan Li

Abstract read
In one paragraph

Article in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Yanyan ZhouDepartment of Radiology, Changxing Hospital of Traditional Chinese Medicine, Huzhou, China.
Qianlian WuDepartment of Radiology, Yijishan Hospital Affiliated to Wannan Medical University, Wuhu, China.
Hejia ZhangSchool of Medicine, Linyi University, Linyi, China.
Peng ChenDepartment of Radiology, Huzhou Central Hospital, Huzhou, China.
Chengmeng ZhangDepartment of Radiology, Huzhou Central Hospital, Huzhou, China.
Jian ShenDepartment of Radiology, Huzhou Central Hospital, Huzhou, China.
Junmei WangDepartment of Radiology, Yijishan Hospital Affiliated to Wannan Medical University, Wuhu, China.
Chuanxian LiuDepartment of Radiology, Jiaxing Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.
Zhimin Ding *Department of Radiology, Yijishan Hospital Affiliated to Wannan Medical University, Wuhu, China.
Liangshan Li *Department of Radiology, Jiaxing Hospital of Traditional Chinese Medicine Affiliated to Zhejiang Chinese Medical University, Jiaxing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Isolated hepatic metastasis (IHM) and intrahepatic cholangiocarcinoma (IMCC) may show overlapping appearances on multiphasic contrast-enhanced computed tomography (CT). A quantitative imaging approach may help improve noninvasive differentiation. This study aims to develop and externally validate multiphasic contrast-enhanced CT radiomics models for differentiating IHM from IMCC and to evaluate the diagnostic value of native, subtraction, peritumoral, and fusion information. Methods: This retrospective two-center study included patients with pathologically confirmed IHM or IMCC who underwent multiphasic liver CT at Huzhou Central Hospital or Yijishan Hospital Affiliated to Wannan Medical University between January 2023 and December 2025. The final cohort included 227 patients. Center 1 was used as the training cohort (n=115; IHM, n=68; IMCC, n=47), and center 2 was used as the external validation cohort (n=112; IHM, n=60; IMCC, n=52). Clinical baseline variables included sex, age, and serum tumor markers. Radiomics features were extracted using PyRadiomics from unenhanced CT images (C), arterial phase CT images (A), venous phase CT images (V), and subtraction images M=A-C, N=V-C, and P=A-V. Ten prespecified models were constructed to represent native-image, subtraction-image, peritumoral, and fusion information levels. Feature selection was performed in the training cohort using median imputation, variance filtering, and ANOVA F-test-based SelectKBest. XGBoost classifiers were tuned using five-fold stratified cross-validation in the training cohort and evaluated once in the external validation cohort. Results: In the training cohort, apparent AUCs ranged from 0.864 to 0.978. The subtraction-total model based on M+N+P intratumoral features showed the highest numerical external validation performance, with an AUC of 0.835 (0.755-0.907), accuracy of 0.759, sensitivity of 0.800, and specificity of 0.712. The intra-plus-peritumoral model achieved an external validation AUC of 0.800 (0.713-0.882). The plain unenhanced model achieved an AUC of 0.821 (0.739-0.900), whereas the native C+A+V model and ultimate fusion model achieved AUCs of 0.809 (0.725-0.888) and 0.800 (0.714-0.884), respectively. Pairwise DeLong tests among XGBoost models did not show statistically significant differences after multiple-comparison correction. Conclusion: Multiphase contrast-enhanced CT radiomics can differentiate IHM from IMCC. Subtraction-based intratumoral models demonstrated the highest numerical external validation performance, although the advantage was not statistically significant after multiple-comparison correction. These findings indicate the potential discriminatory value of enhancement-difference information.

Indexed as

computed tomographyexternal validationintrahepatic cholangiocarcinomaliver metastasisradiomicssubtraction imagingXGBoost

Identifiers

PMID42597372
PMCPMC13467766

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.