ArticleFrontiers in immunology2025
Development and validation of a deep learning-based pathomics signature for prognosis and chemotherapy benefits in colorectal cancer: a retrospective multicenter cohort study.
Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Review
- A risk stratification framework based on ammonia-induced cell death signatures for prognostic assessment and therapeutic insights in colorectal cancer.Discover oncology · 2026Article
- Innovations in biomarker stratification for precision oncology.Clinical and experimental medicine · 2026Review
- Advancements in artificial intelligence for cancer diagnosis and prognosis prediction: current applications and emerging opportunities.Frontiers in cell and developmental biology · 2026Review
- Intratumoral and peritumoral radiomics of MRI predict pathological differentiation in patients with rectal cancer.Oncology letters · 2026Article
- AI-assisted multimodal data integration for precision oncology.Frontiers in artificial intelligence · 2026Review
- Artificial intelligence for perioperative precision in surgical oncology.Frontiers in surgery · 2026Review
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The conventional tumor-node-metastasis (TNM) classification system remains limited in accurately forecasting prognosis and guiding adjuvant chemotherapy decisions for patients with colorectal cancer (CRC). To address this gap, we introduced and validated a novel pathomics signature (PS Methods: This retrospective study analyzed 883 slides from two independent cohorts. An interpretable multi-instance learning model was developed to construct PS Results: The results demonstrated that PS Discussion: Our findings suggested that the application of deep learning to histopathological images could be an efficient method to improve the prognostic accuracy and evaluate the treatment responses in CRC. The PS
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