ArticleCommunications medicine2024
A systematic pan-cancer study on deep learning-based prediction of multi-omic biomarkers from routine pathology images.
Article in Communications medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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
28 citing papers in PubMed.
- Trial
- Beyond Surgery: Does Adjuvant Therapy Still Add Value for Patients With Pancreatic Cancer After Neoadjuvant Treatment?Annals of surgical oncology · 2026Article
- Deep Learning-Based Multi-Cancer Analysis for Predicting Disease-Free Survival Across Multiple Cancer Types.Cancers · 2026Article
- Tumours of the duodenum and the ampulla of Vater: An overview of the key criteria for their classification, pathogenesis, and molecular assessment.Virchows Archiv : an international journal of pathology · 2026Review
- World Health Organization classification of tumours of the breast 6th edition 2026.Histopathology · 2026Review
- Multi-site validation of an AI-based biomarker test for determining ER, PR, and HER2 status from H&E-stained breast cancer slides.ESMO real world data and digital oncology · 2026Article
- Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.Communications medicine · 2026Article
- PRET is a few-shot system for pan-cancer recognition without example training.Nature cancer · 2026Article
- Survival prediction for bladder cancer using multimodal data with quantum neural networks and transformer architectures.Scientific reports · 2026Article
- SlideMamba: entropy-based adaptive fusion of GNN and Mamba for enhanced representation learning in digital pathology.Scientific reports · 2026Article
- Development and validation of an interpretable prediction model using spatial patterns of tumor-infiltrating lymphocytes in H&E-stained whole-slide images for immune subtyping of lung adenocarcinoma.Frontiers in immunology · 2026Article
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Virtual immunohistochemistry for ER and PR prediction in breast cancer histopathology: a two-stage conditional generative framework.Frontiers in oncology · 2026Article
- H&E-based MSI/MMR testing with AI in colorectal cancer: a multi-centred blinded evaluation.NPJ digital medicine · 2025Article
- Artificial Intelligence Tools for Supporting Histopathologic and Molecular Characterization of Gynecological Cancers: A Review.Journal of clinical medicine · 2025Review
- Pathologist-interpretable breast cancer subtyping and stratification from AI-inferred nuclear features.bioRxiv : the preprint server for biology · 2025Article
- Predicting ROS1 and ALK fusions in NSCLC from H&E slides with a two-step vision transformer approach.NPJ precision oncology · 2025Article
- Using artificial intelligence to prioritize pathology samples: report of a test drive.Virchows Archiv : an international journal of pathology · 2025Article
- Machine learning for prediction of 30-day mortality in patients with advanced cancer: comparing pan-cancer and single-cancer models.ESMO real world data and digital oncology · 2025Article
- Corr-A-Net: Interpretable Attention-Based Correlated Feature Learning framework for predicting of HER2 Score in Breast Cancer from H&E Images.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe objective of this comprehensive pan-cancer study is to evaluate the potential of deep learning (DL) for molecular profiling of multi-omic biomarkers directly from hematoxylin and eosin (H&E)-stained whole slide images.
methodsA total of 12,093 DL models predicting 4031 multi-omic biomarkers across 32 cancer types were trained and validated. The study included a broad range of genetic, transcriptomic, and proteomic biomarkers, as well as established prognostic markers, molecular subtypes, and clinical outcomes.
resultsHere we show that 50% of the models achieve an area under the curve (AUC) of 0.644 or higher. The observed AUC for 25% of the models is at least 0.719 and exceeds 0.834 for the top 5%. Molecular profiling with image-based histomorphological features is generally considered feasible for most of the investigated biomarkers and across different cancer types. The performance appears to be independent of tumor purity, sample size, and class ratio (prevalence), suggesting a degree of inherent predictability in histomorphology.
conclusionsThe results demonstrate that DL holds promise to predict a wide range of biomarkers across the omics spectrum using only H&E-stained histological slides of solid tumors. This paves the way for accelerating diagnosis and developing more precise treatments for cancer patients.
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.