ArticleNature communications2025
Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Trends and hotspots in research related to tumor immune escape: bibliometric analysis and future perspectives.Frontiers in immunology · 2025Pooled it
- 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
- Self-supervised learning can distinguish myelodysplastic neoplasms from clinical mimics using bone marrow biopsies.Blood neoplasia · 2026Article
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Integrating artificial intelligence and multi-omics data for precision oncology in endometrial cancer: a narrative review.Functional & integrative genomics · 2026Review
- Generalizable self-supervised learning for imaging flow cytometry on multi-dataset leukocyte differential.Microsystems & nanoengineering · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Aligning computational pathology with clinical practice for colorectal cancer.NPJ precision oncology · 2025Review
- The tumour-stroma ratio as predictive aid towards a biopsy-based treatment strategy in rectal carcinoma.Histopathology · 2025Article
- Whole slide image based prognosis prediction in rectal cancer using unsupervised artificial intelligence.BMC cancer · 2024Article
- Unselective Measurement of Tumor-to-Stroma Proportion in Colon Cancer at the Invasion Front-An Elusive Prognostic Factor: Original Patient Data and Review of the Literature.Diagnostics (Basel, Switzerland) · 2024Article
- Results from the UNITED study: a multicenter study validating the prognostic effect of the tumor-stroma ratio in colon cancer.ESMO open · 2024Article
- P-MES: Explainable pathology-based distant metastasis risk stratification in locally advanced nasopharyngeal carcinoma.Digital healthArticle
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Abstract
Self-supervised learning (SSL) automates the extraction and interpretation of histopathology features on unannotated hematoxylin-eosin-stained whole slide images (WSIs). We train an SSL Barlow Twins encoder on 435 colon adenocarcinoma WSIs from The Cancer Genome Atlas to extract features from small image patches (tiles). Leiden community detection groups tiles into histomorphological phenotype clusters (HPCs). HPC reproducibility and predictive ability for overall survival are confirmed in an independent clinical trial (N = 1213 WSIs). This unbiased atlas results in 47 HPCs displaying unique and shared clinically significant histomorphological traits, highlighting tissue type, quantity, and architecture, especially in the context of tumor stroma. Through in-depth analyses of these HPCs, including immune landscape and gene set enrichment analyses, and associations to clinical outcomes, we shine light on the factors influencing survival and responses to treatments of standard adjuvant chemotherapy and experimental therapies. Further exploration of HPCs may unveil additional insights and aid decision-making and personalized treatments for colon cancer patients.
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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.