ArticleBritish journal of cancer2024
Predicting 5-year recurrence risk in colorectal cancer: development and validation of a histology-based deep learning approach.
Article in British journal of cancer, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 15 citations in OpenAlex.
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- REMIL-IBD: Region-filtered multiple instance learning for interpretable slide-level grading of inflammatory bowel disease.Journal of pathology informatics · 2026Article
- Artificial intelligence for the prediction of prognosis in colorectal cancer patients using routine blood indices.NPJ digital medicine · 2026Article
- KRASCellular & molecular biology letters · 2026Article
- From inflammation to precision medicine in colon cancer: Methodological considerations and future directions.World journal of gastrointestinal surgery · 2026Article
- Artificial intelligence in the surgical management for colorectal cancer: current applications and future directions.Frontiers in oncology · 2026Review
- Deep learning-enabled multiphoton microscopy predicts colorectal cancer recurrence from routine FFPE specimens.NPJ digital medicine · 2025Article
- Multimodal artificial intelligence technology in the precision diagnosis and treatment of gastroenterology and hepatology: Innovative applications and challenges.World journal of gastroenterology · 2025Review
- Construction of a risk prediction model for early postoperative recurrence in stage II/III colorectal cancer.World journal of gastrointestinal oncology · 2025Article
- Explainable, federated deep learning model predicts disease progression risk of cutaneous squamous cell carcinoma.NPJ precision oncology · 2025Article
- Deep learning-based interpretable prediction of recurrence of diffuse large B-cell lymphoma.BJC reports · 2025Article
- Personalized surveillance in colorectal cancer: Integrating circulating tumor DNA and artificial intelligence into post-treatment follow-up.World journal of gastroenterology · 2025Article
- Self-supervised learning reveals clinically relevant histomorphological patterns for therapeutic strategies in colon cancer.Nature communications · 2025Article
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Authors and funding
12 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundAccurate estimation of the long-term risk of recurrence in patients with non-metastatic colorectal cancer (CRC) is crucial for clinical management. Histology-based deep learning is expected to provide more abundant information for risk stratification.
methodsWe developed and validated a weakly supervised deep-learning model for predicting 5-year relapse-free survival (RFS) to stratify patients with different risks based on histological images from three hospitals of 614 cases with non-metastatic CRC. A deep prognostic factor (DL-RRS) was established to stratify patients into high and low-risk group. The areas under the curve (AUCs) were calculated to evaluate the performances of models.
resultsOur proposed model achieves the AUCs of 0.833 (95% CI: 0.736-0.905) and 0.715 (95% CI: 0.647-0.776) on validation cohort and external test cohort, respectively. The 5-year RFS rate was 45.7% for high DL-RRS patients, and 82.5% for low DL-RRS patients respectively in the external test cohort (HR: 3.89, 95% CI: 2.51-6.03, P < 0.001). Adjuvant chemotherapy was associated with improved RFS in Stage II patients with high DL-RRS (HR: 0.15, 95% CI: 0.06-0.38, P < 0.001).
conclusionsDL-RRS has a good predictive performance of 5-year recurrence risk in CRC, and will better serve the clinical decision-making.
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