Evidence map›Paper›PMID 41210998›Full record

ArticleiScience2025

Dual-channel deep learning captures intratumoural heterogeneity on CECT for preoperative risk stratification of thymic epithelial tumors.

Xin Pan, Wencan Wang, Xinyu Li, Lingyu Zhang, Shiwen Li, Xuhui Zhao, Qian Zhang, Huajie Yue, Haomiao Ma, Li Liang and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 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

16 authors.

Xin PanDepartment of Intensive Care Unit, Sixth Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Wencan WangDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Xinyu LiDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Lingyu ZhangDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Shiwen LiDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Xuhui ZhaoDepartment of Medical Imaging, Shanxi Provincial People's Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Qian ZhangDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Huajie YueDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Haomiao MaDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Li LiangDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Yanhong HaoDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Hua ZhangDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.
Chunhai YuDepartment of Medical Imaging, Shanxi Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi 030013, China.
Jianxin ZhangDepartment of Medical Imaging, Shanxi Cancer Hospital, Shanxi Hospital Affiliated to Cancer Hospital, Chinese Academy of Medical Sciences, Cancer Hospital Affiliated to Shanxi Medical University, Taiyuan, Shanxi 030013, China.
Lina WangDepartment of Ultrasound Medicine, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia Autonomous 010000, China.
Lingjie WangDepartment of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi 030001, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate preoperative risk stratification is critical for treating thymic epithelial tumors (TETs). This study developed a deep learning framework that combines a dual-channel convolutional neural network (CNN) with an adaptive dynamic clustering algorithm. The model was trained on contrast-enhanced CT (CECT) images from 336 multicenter TET patients. It first automates the segmentation of tumor subregions. Then, it constructs dual-channel input data containing the largest cross-sectional ROI and its corresponding habitat masks. Using transfer learning, we trained four CNN architectures for risk stratification. The DenseNet121-based dual-channel CNN achieved an AUC of 0.74-0.76 on an external test set. This performance surpassed conventional radiomics, single-channel CNN, and radiologists' visual assessment. Our framework effectively captures intratumoral heterogeneity, improves risk stratification accuracy, and aids in rapid identification of high-risk patients. This approach can support personalized treatment planning.

Indexed as

Health sciences

Identifiers

PMID41210998
PMCPMC12590028

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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.