Evidence map›Paper›PMID 39709501›Full record

ArticleNPJ precision oncology2024

Deep learning for endometrial cancer subtyping and predicting tumor mutational burden from histopathological slides.

Ching-Wei Wang, Nabila Puspita Firdi, Yu-Ching Lee, Tzu-Chiao Chu, Hikam Muzakky, Tzu-Chien Liu, Po-Jen Lai, Tai-Kuang Chao

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Article in NPJ precision oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 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

8 authors.

Ching-Wei WangGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. cweiwang@mail.ntust.edu.tw.ORCID http://orcid.org/0000-0001-9992-6863
Nabila Puspita FirdiGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Yu-Ching LeeGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.ORCID http://orcid.org/0000-0002-9233-1223
Tzu-Chiao ChuGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Hikam MuzakkyGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Tzu-Chien LiuGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Po-Jen LaiGraduate Institute of Biomedical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan.
Tai-Kuang ChaoInstitute of Pathology and Parasitology, National Defense Medical Center, Taipei, Taiwan. chaotai.kuang@msa.hinet.net.ORCID http://orcid.org/0000-0001-8219-6382

Funding

Tri-Service General Hospital (Sānjun Zongyīyuàn) TSGH-A-112018Tri-Service General Hospital (Sānjun Zongyīyuàn) TSGH-A-114007
6 · The paper itself

Abstract

Endometrial cancer (EC) diagnosis traditionally relies on tumor morphology and nuclear grade, but personalized therapy demands a deeper understanding of tumor mutational burden (TMB), i.e., a key biomarker for immune checkpoint inhibition and immunotherapy response. Traditional TMB prediction methods, such as sequencing exomes or whole genomes, are costly and often unavailable in clinical settings. We present the first TR-MAMIL deep learning framework to predict TMB status and classify the EC cancer subtype directly from H&E-stained WSIs, enabling effective personalized immunotherapy planning and prognostic refinement of EC patients. Our models were evaluated on a large dataset from The Cancer Genome Atlas. TR-MAMIL performed exceptionally well in classifying aggressive and non-aggressive EC, as well as predicting TMB, outperforming seven state-of-the-art approaches. It also performed well in classifying normal and abnormal p53 mutations in EC using H&E WSIs. Kaplan-Meier analysis further demonstrated TR-MAMIL's ability to differentiate patients with longer survival in the aggressive EC.

Identifiers

PMID39709501
PMCPMC11663217

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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.