ArticleNature communications2023
Histopathology images predict multi-omics aberrations and prognoses in colorectal cancer patients.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
What it found
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Who cites it
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- A Generalizable Multimodal Model for Treatment-Stratified Risk and Survival Assessment under Real-World Constraints: A Multi-Center Study of Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- The spatial architecture of tumor immunity: Emerging predictive biomarkers across cancer.Immunologic research · 2026Review
- Artificial Intelligence-Based Prediction of Molecular Alterations in Colorectal Cancer Using Routine H&E Whole-Slide Images.International journal of molecular sciences · 2026Review
- Multi-omics characterization of a GPRC5AJournal of translational medicine · 2026Article
- DFCE-KanT: predicting spatial gene expression from histology images via contrastive learning.Bioinformatics (Oxford, England) · 2026Article
- SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.International journal of surgery (London, England) · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Review
- Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance.bioRxiv : the preprint server for biology · 2026Article
- Deep learning for predicting pituitary neuroendocrine tumour lineage and high-risk subtypes from histology.NPJ precision oncology · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- The interplay between Peptostreptococcus and Fusobacterium as novel signatures in colorectal cancer recurrence.Microbiome · 2026Article
- Foundation Model-Enabled Multimodal Deep Learning for Prognostic Prediction in Colorectal Cancer with Incomplete Modalities: A Multi-Institutional Retrospective Study.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Interpretable Cancer Survival Prediction by Fusing Semantic Labelling of Cell Types and Whole Slide Images.Interdisciplinary sciences, computational life sciences · 2026Article
- Advances in predicting omics profiles from imaging data.Briefings in bioinformatics · 2026Review
- The consensus molecular subtypes of esophageal squamous cell carcinoma.Signal transduction and targeted therapy · 2026Article
- An interpretable deep learning model for predicting endometrial cancer molecular subtypes from H&E-stained slides.NPJ precision oncology · 2026Article
- Integrating Genomics, Radiomics, and Pathomics in Oncology: A Scoping Review and a Framework for AI-Enabled Surgomics.Bioengineering (Basel, Switzerland) · 2026Review
- Predicting MammaPrint Recurrence Risk from Breast Cancer Pathological Images Using a Weakly Supervised Transformer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
13 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Histopathologic assessment is indispensable for diagnosing colorectal cancer (CRC). However, manual evaluation of the diseased tissues under the microscope cannot reliably inform patient prognosis or genomic variations crucial for treatment selections. To address these challenges, we develop the Multi-omics Multi-cohort Assessment (MOMA) platform, an explainable machine learning approach, to systematically identify and interpret the relationship between patients' histologic patterns, multi-omics, and clinical profiles in three large patient cohorts (n = 1888). MOMA successfully predicts the overall survival, disease-free survival (log-rank test P-value<0.05), and copy number alterations of CRC patients. In addition, our approaches identify interpretable pathology patterns predictive of gene expression profiles, microsatellite instability status, and clinically actionable genetic alterations. We show that MOMA models are generalizable to multiple patient populations with different demographic compositions and pathology images collected from distinctive digitization methods. Our machine learning approaches provide clinically actionable predictions that could inform treatments for colorectal cancer patients.
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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.