Evidence map›Paper›PMID 41207998›Full record

ArticleMolecular biomedicine2025

Clinical-grade AI model for molecular subtyping of endometrial cancer: a multi-center cohort study in China.

Peng Qi, Tianliang Yao, Hu Li, Jingnan Zhu, Jianye Li, Xuezhen Luo, Qizhi He, Yiran Li

Abstract readMulticenter Study
PubMed Full text at PMC Publisher
In one paragraph

Article in Molecular biomedicine, 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.

Peng Qi *Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai, 201210, China.
Tianliang Yao *Department of Control Science and Engineering, College of Electronics and Information Engineering, Tongji University, Shanghai, 201210, China.
Hu Li *Centre for Assisted Reproduction, Shanghai Key Laboratory of Maternal-Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Jingnan Zhu *Centre for Assisted Reproduction, Shanghai Key Laboratory of Maternal-Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China.
Jianye LiPingdingshan Maternal and Child Health Hospital, Henan, 467000, China.
Xuezhen LuoDepartment of Gynecology, Obstetrics and Gynecology Hospital of Fudan University, Shanghai, 200011, China. xuezhenluo2013@163.com.
Qizhi HeDepartment of Pathology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China. qizhihe@tongji.edu.cn.
Yiran LiCentre for Assisted Reproduction, Shanghai Key Laboratory of Maternal-Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, 200092, China. liyiran2007@gmail.com.ORCID 0000-0002-4658-3876

Funding

National Key Research and Development Program of China 2023YFB4705200National Natural Science Foundation of China 62273257Shanghai Health System Outstanding Talents Program 20234Z0019
6 · The paper itself

Abstract

Accurate molecular subtyping is essential for guiding precision treatment and prognostic stratification in endometrial cancer (EC). However, current methods, based on Sanger sequencing and immunohistochemistry (IHC), are costly, time-intensive, and difficult to implement widely in routine clinical practice, particularly in resource-limited settings. To overcome these challenges, we developed a deep-learning pipeline that directly infers EC molecular subtypes from routine hematoxylin-and-eosin (H&E) whole-slide images (WSIs). The framework integrates super-resolution enhancement (SRResGAN), transformer-based lesion segmentation (MedSAM), and a ResNet-101 classifier for molecular subtype prediction, with an LSTM module for survival modeling. This retrospective study included 393 Chinese patients diagnosed between 2010 and 2018, all with ≥ 5 years of follow-up. Molecular subtypes-POLE

Indexed as

Endometrial NeoplasmsAdultAgedBiomarkers, TumorChinaCohort StudiesDeep LearningFemaleHumansImmunohistochemistryMiddle AgedNeoplasm GradingPrognosisRetrospective StudiesBiomarkers, TumorArtificial intelligenceDeep learningDigital pathologyEndometrial cancerMolecular subtypingWhole slide imaging

Identifiers

PMID41207998
PMCPMC12597869

What Socratic holds

Textmetadata
LicenceCC BY
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 Socratic graph.