ArticlePLoS medicine2019
Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study.
Article in PLoS medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 370 papers, 3 of them syntheses that pooled it.
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Who cites it
370 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning driven colorectal polyp analysis: a review of detection, classification and segmentation methods.Frontiers in artificial intelligence · 2026Pooled it
- Transforming histologic assessment: artificial intelligence in cancer diagnosis and personalized treatment.British journal of cancer · 2025Pooled it
- Application of Artificial Intelligence in the diagnosis and treatment of colorectal cancer: a bibliometric analysis, 2004-2023.Frontiers in oncology · 2024Pooled it
- Using deep learning to predict survival outcome in non-surgical cervical cancer patients based on pathological images.Journal of cancer research and clinical oncology · 2023Trial
- Deep learning based on CD3 histological slides for prediction of colon cancer outcome: analysis of three international stage III colon cancer cohorts.Oncoimmunology · 2026Article
- 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
- Multimodal Artificial Intelligence in Lung Cancer: From Data Integration to Precision Oncology.Cancers · 2026Review
- Effective tumor kinetics inferred from single routine H&E biopsies enable counterfactual virtual radiotherapy trials.medRxiv : the preprint server for health sciences · 2026Article
- Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology.Cancer research · 2026Article
- FTU-Seek: Foundation Model-Guided Hard-Negative Learning for Sparse Functional Tissue Unit Segmentation.Biomedicines · 2026Article
- V7-DiVA: a feature-based Deep MIL colorectal whole-slide histopathology research platform with blinded external image-input validation.Journal of translational medicine · 2026Article
- Pathology-derived clinical micro-architectural diagnostics of tumour-microbiome interactions in colorectal cancer.Journal of translational medicine · 2026Article
- SurvGRN: a multi-feature fusion framework for bladder cancer survival prediction.International journal of surgery (London, England) · 2026Article
- Artificial intelligence in nephrology: predicting CKD progression and personalizing treatment.International urology and nephrology · 2026Review
- A lightweight ResNet50V2-ECA model for renal cell carcinoma grading: efficiency, calibration, and state-of-the-art performance.Scientific reports · 2026Article
- Enhancing Whole Slide Image Classification in Renal Cell Carcinoma via Swin Transformer-Based Multiple Instance Learning.Bioengineering (Basel, Switzerland) · 2026Article
- A generalist biomedical vision-language model via multi-CLIP knowledge distillation.Nature communications · 2026Article
- Continual Learning for Histopathology Image Classification in Class-Incremental Learning.Diagnostics (Basel, Switzerland) · 2026Article
- Review
- MCLAM: a cost-effective deep learning model for predicting recurrence risk in HR+/HER2- breast cancer-a multi-center study in a Chinese cohort.Journal of the National Cancer Center · 2026Article
310 more citing papers are in PubMed but not listed here.
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18 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFor virtually every patient with colorectal cancer (CRC), hematoxylin-eosin (HE)-stained tissue slides are available. These images contain quantitative information, which is not routinely used to objectively extract prognostic biomarkers. In the present study, we investigated whether deep convolutional neural networks (CNNs) can extract prognosticators directly from these widely available images. METHODS AND
findingsWe hand-delineated single-tissue regions in 86 CRC tissue slides, yielding more than 100,000 HE image patches, and used these to train a CNN by transfer learning, reaching a nine-class accuracy of >94% in an independent data set of 7,180 images from 25 CRC patients. With this tool, we performed automated tissue decomposition of representative multitissue HE images from 862 HE slides in 500 stage I-IV CRC patients in the The Cancer Genome Atlas (TCGA) cohort, a large international multicenter collection of CRC tissue. Based on the output neuron activations in the CNN, we calculated a "deep stroma score," which was an independent prognostic factor for overall survival (OS) in a multivariable Cox proportional hazard model (hazard ratio [HR] with 95% confidence interval [CI]: 1.99 [1.27-3.12], p = 0.0028), while in the same cohort, manual quantification of stromal areas and a gene expression signature of cancer-associated fibroblasts (CAFs) were only prognostic in specific tumor stages. We validated these findings in an independent cohort of 409 stage I-IV CRC patients from the "Darmkrebs: Chancen der Verhütung durch Screening" (DACHS) study who were recruited between 2003 and 2007 in multiple institutions in Germany. Again, the score was an independent prognostic factor for OS (HR 1.63 [1.14-2.33], p = 0.008), CRC-specific OS (HR 2.29 [1.5-3.48], p = 0.0004), and relapse-free survival (RFS; HR 1.92 [1.34-2.76], p = 0.0004). A prospective validation is required before this biomarker can be implemented in clinical workflows.
conclusionsIn our retrospective study, we show that a CNN can assess the human tumor microenvironment and predict prognosis directly from histopathological images.
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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.