ArticleNature cancer2020
Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis.
Article in Nature cancer, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 327 papers, 1 of them a synthesis 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
327 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning approaches for resolving genomic discrepancies in cancer: a systematic review and clinical perspective.Briefings in bioinformatics · 2025Pooled it
- A Boveri perspective on cancer biomarker testing using artificial intelligence.NPJ precision oncology · 2026Review
- XChondNet: Explainable Chondrogenic Tumor Diagnosis by Spatial-Context-Aware Synergistic Deep Feature Fusion.Sensors (Basel, Switzerland) · 2026Article
- Review
- Artificial intelligence in oncology: linking biological discovery to clinical utility.Molecular cancer · 2026Review
- World Health Organization classification of tumours of the breast 6th edition 2026.Histopathology · 2026Review
- Tumor evolution: signaling pathways, molecular mechanisms and therapeutic targets.Signal transduction and targeted therapy · 2026Review
- Spatial architecture of tertiary lymphoid structures represents an independent prognostic dimension in hepatocellular carcinoma.Journal for immunotherapy of cancer · 2026Article
- From unlabeled to labeled: self-supervised deep learning in computational pathology.BioData mining · 2026Article
- Deep Learning on H&E Pathology Images PredictsDiseases (Basel, Switzerland) · 2026Article
- GenoGlyph: Pan-cancer genomic mutation inference and risk stratification from diagnostic histopathology slides.Research square · 2026Article
- Breast Cancer Hormone Receptor Status Determination from H&E-Stained Biopsy Images Using Pixel-Level Classifiers.Cancers · 2026Article
- Hetairos is a histology-based artificial intelligence model for predicting central nervous system tumor methylation subtypes.Nature cancer · 2026Article
- Two-Step Ensemble Convolutional Neural Networks for Colonoscopic Biopsy Classification Resembling Pathologists' Process.Journal of Korean medical science · 2026Article
- CNet-Cox for interpretable network biomarker discovery and survival risk scoring in precise breast cancer prognosis.NPJ digital medicine · 2026Article
- Digital Pathology and the AI-Based Quantification of the Tumor Microenvironment in Gastrointestinal Cancer: From Tumor Budding and Tumor-Infiltrating Lymphocytes to Tertiary Lymphoid Structures.International journal of molecular sciences · 2026Review
- Comprehensive evaluation of cross cancer generalization in histopathology segmentation models across 21 tumor types.Communications medicine · 2026Article
- Cancer in transition: discovery of tumor-intrinsic transcriptional programs shaping the immune and microenvironmental landscape.Biomarker research · 2026Review
- Integrating Artificial Intelligence into Breast Cancer Histopathology: Toward Improved Diagnosis and Prognosis.Cancers · 2026Review
- Development, validation, and visualization of a novel nomogram for predicting clinical outcomes of postoperative cervical cancer patients.Scientific reports · 2026Article
267 more citing papers are in PubMed but not listed here.
Corrections and comments
- Commented on by
Authors and funding
10 authors.
Funding
Abstract
We use deep transfer learning to quantify histopathological patterns across 17,355 hematoxylin and eosin-stained histopathology slide images from 28 cancer types and correlate these with matched genomic, transcriptomic and survival data. This approach accurately classifies cancer types and provides spatially resolved tumor and normal tissue distinction. Automatically learned computational histopathological features correlate with a large range of recurrent genetic aberrations across cancer types. This includes whole-genome duplications, which display universal features across cancer types, individual chromosomal aneuploidies, focal amplifications and deletions, as well as driver gene mutations. There are widespread associations between bulk gene expression levels and histopathology, which reflect tumor composition and enable the localization of transcriptomically defined tumor-infiltrating lymphocytes. Computational histopathology augments prognosis based on histopathological subtyping and grading, and highlights prognostically relevant areas such as necrosis or lymphocytic aggregates. These findings show the remarkable potential of computer vision in characterizing the molecular basis of tumor histopathology.
Indexed as
Identifiers
35122049What 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.