Evidence map›Paper›PMID 41388069›Full record

ArticleNPJ precision oncology2025

Mucin phenotype-based deep learning framework for intestinal metaplasia-carcinogenesis progression prediction.

Xiaoyang Wu, Fang Wang, Weiyou Dai, Chuxuan Ni, Liping Sun, Yuehua Gong, Nannan Dong, Zeyang Wang, Liang Li, Qian Xu and 4 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ precision oncology, 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

14 authors.

Xiaoyang WuTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Fang WangTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Weiyou DaiCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China.
Chuxuan NiTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Liping SunTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Yuehua GongTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Nannan DongTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Zeyang WangTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Liang LiTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Qian XuTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Jingjing JingTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Shixuan ShenTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China.
Huakang TuCenter of Clinical Big Data and Analytics of the Second Affiliated Hospital and School of Public Health, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China. huakangtu@zju.edu.cn.
Yuan YuanTumor Etiology and Screening Department of Cancer Institute, and Key Laboratory of Cancer Etiology and Prevention in Liaoning Education Department, the First Hospital of China Medical University, Shenyang, Liaoning, China. yuanyuan@cmu.edu.cn.

Funding

National Key Research and Development Program of China 2022YFC2505100National Natural Science Foundation of China 82574197National Science and Technology Major Project 2023ZD0501400
6 · The paper itself

Abstract

This study decodes spatiotemporal mucin dynamics in gastric carcinogenesis, revealing gastric-type markers (MUC5AC/MUC6) decline progressively while intestinal-type markers (MUC2/CD10) peak in gastric intestinal metaplasia (GIM) before decreasing in gastric cancer (GC). We developed MPMR, a dual-function UNI-pretrained Vision Transformer (ViT) model, which directly predicts four mucin markers from H&E whole-slide images with near-perfect accuracy (AUC: 0.921-0.997) and generates interpretable simulated staining heatmaps via adversarial learning. Integrating these outputs with clinical variables, the MPMR-IMCP risk model significantly outperformed clinical-only models (ΔAUC = 0.050), enabling both phenotype analysis and GIM risk stratification without specialized staining. Validated in longitudinal cohorts from Chinese GC high-incidence regions, this framework offers an efficient solution for monitoring GIM malignant transformation.

Identifiers

PMID41388069
PMCPMC12820390

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.