ArticleNPJ digital medicine2021
Interpretable survival prediction for colorectal cancer using deep learning.
Article in NPJ digital medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 116 papers, 1 of them a synthesis that pooled it.
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The trial behind it
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Who cites it
116 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Direct tumour-adipocyte contact / Stroma AReactive Invasion Front Area (TAC/SARIFA) predicts adverse outcome in colorectal cancer: a focused evidence synthesis and meta-analysis.Histopathology · 2026Pooled it
- Development of a Computational Histology Artificial Intelligence-Powered Prognostic Biomarker in Colorectal Cancer in The Cancer Genome Atlas.World journal of oncology · 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
- Anticipating health and care trajectories from routinely collected social care records.npj health systems · 2026Article
- RNF research trends and an RNF43-based prognostic model for colorectal cancer with immune microenvironment analysis.Discover oncology · 2026Article
- SARIFA and Lipid Metabolic Reprogramming in Prostate Cancer: Fundamental Mechanisms, Tumor Microenvironment, and Novel Biomarker Prospects.Life (Basel, Switzerland) · 2026Review
- REMIL-IBD: Region-filtered multiple instance learning for interpretable slide-level grading of inflammatory bowel disease.Journal of pathology informatics · 2026Article
- Advancing AI for multi-omics and clinical data integration in basic and translational cancer research.Nature reviews. Cancer · 2026Review
- Artificial intelligence models for survival prediction in colorectal cancer: a systematic review of time-to-event approaches.Annals of medicine and surgery (2012) · 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
- [SARIFA: Literature review and expert opinion on the new morphological biomarker].Pathologie (Heidelberg, Germany) · 2026Review
- Deep learning-based H&E-derived risk scores in colorectal cancer: associations with tumour morphology, biology, and predicted drug response.The Journal of pathology · 2026Article
- Interpretable multimodal PET/CT-EHR fusion via mixture-of-experts for prognostic stratification in mantle cell lymphoma: a multicenter study.BMC medicine · 2026Article
- Empirical evaluation of variability and multi-institutional generalizability of deep learning survival models: application to renal cancer CT scans.Computers in biology and medicine · 2026Article
- Deep learning-based pathomics signature predicts prognosis and treatment response in gastric cancer: a multicenter retrospective study.NPJ precision oncology · 2026Article
- Review
- Integrating artificial intelligence (AI) into colorectal cancer reporting.The Journal of pathology · 2026Review
- A Bilayer Feature Fusion Framework for Pan-Cancer Survival Prediction Based on Multihead Attention and Adaptive Differential Privacy: Model Development and Validation Study.JMIR medical informatics · 2026Article
- A comprehensive European Colorectal Cancer Cohort dataset.Scientific data · 2026Article
- A generalizable pathology foundation model using a unified knowledge distillation pretraining framework.Nature biomedical engineering · 2026Article
56 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
23 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deriving interpretable prognostic features from deep-learning-based prognostic histopathology models remains a challenge. In this study, we developed a deep learning system (DLS) for predicting disease-specific survival for stage II and III colorectal cancer using 3652 cases (27,300 slides). When evaluated on two validation datasets containing 1239 cases (9340 slides) and 738 cases (7140 slides), respectively, the DLS achieved a 5-year disease-specific survival AUC of 0.70 (95% CI: 0.66-0.73) and 0.69 (95% CI: 0.64-0.72), and added significant predictive value to a set of nine clinicopathologic features. To interpret the DLS, we explored the ability of different human-interpretable features to explain the variance in DLS scores. We observed that clinicopathologic features such as T-category, N-category, and grade explained a small fraction of the variance in DLS scores (R
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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.