ArticleCell reports. Medicine2021
Predicting endometrial cancer subtypes and molecular features from histopathology images using multi-resolution deep learning models.
Article in Cell reports. Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 86 papers, 3 of them syntheses that pooled 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.
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.
Who cites it
86 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Radiomics and artificial intelligence-based prediction of tumor response in digestive system neoplasm: a systematic review and meta-analysis.Frontiers in medicine · 2026Pooled it
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Artificial Intelligence for Predicting Microsatellite Instability Based on Tumor Histomorphology: A Systematic Review.International journal of molecular sciences · 2022Pooled it
- Spatial Mapping of the Precancer-to-Cancer Transition in Breast and Prostate.Cancer discovery · 2026Article
- Artificial Intelligence and Digital Pathology for Molecular Classification of Endometrial Cancer.International journal of molecular sciences · 2026Review
- EMMA-STRAT: a multi-omics based machine learning framework for stratification of endometrial carcinoma molecular subtypes and MSI status.BioData mining · 2026Article
- Artificial intelligence as a pragmatic adjunct to molecular classification in endometrial cancer.Ewha medical journal · 2026Article
- Bridging traditional and deep learning methods in H&E histological image normalization: a comprehensive review and introducing a novel framework for comparative analyses.Journal of advanced research · 2026Review
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Artificial intelligence-driven integration of multi-biofluid omics and clinical phenotype enables stratification of endometrial cancer.Cell reports. Medicine · 2026Article
- AI-assisted histomorphological stratification of endometrial cancer: real-world validation of foundation models for molecular subtyping.NPJ precision oncology · 2026Article
- AI-Driven Breast Cancer Diagnosis: A Systematic Review of Imaging Modalities, Deep Learning, and Explainability.Cancers · 2026Review
- Meta-encoder: a unified integration framework for multiple pathological foundation models in cancer detection.Nature communications · 2026Article
- Real-world benchmarking and validation of foundation model transformers for endometrial cancer subtyping from histopathology.NPJ precision oncology · 2026Article
- Review
- High-efficacy and affordable hyperspectral pancreatic tissue image analysis using near-infrared spectroscopy.Journal of pathology informatics · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- An interpretable deep learning model for predicting endometrial cancer molecular subtypes from H&E-stained slides.NPJ precision oncology · 2026Article
- Artificial intelligence for early endometrial cancer diagnosis using multimodal clinical data: integrating deep learning, explainability, and data privacy.Frontiers in artificial intelligence · 2026Article
- Article
26 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The determination of endometrial carcinoma histological subtypes, molecular subtypes, and mutation status is critical for the diagnostic process, and directly affects patients' prognosis and treatment. Sequencing, albeit slower and more expensive, can provide additional information on molecular subtypes and mutations that can be used to better select treatments. Here, we implement a customized multi-resolution deep convolutional neural network, Panoptes, that predicts not only the histological subtypes but also the molecular subtypes and 18 common gene mutations based on digitized H&E-stained pathological images. The model achieves high accuracy and generalizes well on independent datasets. Our results suggest that Panoptes, with further refinement, has the potential for clinical application to help pathologists determine molecular subtypes and mutations of endometrial carcinoma without sequencing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.