ArticleOncoimmunology2026
Deep learning based on CD3 histological slides for prediction of colon cancer outcome: analysis of three international stage III colon cancer cohorts.
Article in Oncoimmunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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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.
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Who cites it
1 citing paper in PubMed.
- Causal AI for cancer immunotherapy: a narrative framework review of target trial emulation, treatment-effect learning and clinical translation.Frontiers in immunology · 2026Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prognostic stratification in stage III colon cancer remains poor, despite treatment advances. Tumor-infiltrating lymphocytes, particularly CD3+ T cells, are potential prognostic markers, but manual assessment is labor-intensive and not robust. This study aimed to develop a deep learning model for automated analysis of CD3-stained histological slides to improve prognostic prediction. A total of 1737 patients from three international cohorts (PETACC08, PRODIGE-13, and HARMONY) were analyzed. The deep learning model (VGG19) identified tumor core (TC) and invasive margin (IM) regions on CD3-stained slides. Features from VGG19 and UNI models were used to cluster patients using hierarchical classification. Prognostic performance was evaluated using disease-free survival (DFS) across training, internal validation, and external validation sets. Deep learning classifiers identified distinct patient clusters with significantly different DFS based on TC and IM. For both IM and TC analysis, patients in the favorable group had a better DFS in all sets (IM:
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