ArticleJournal of pathology informatics2026
TIMEL: Deep learning-statistical integration reveals spatial stromal and immune signatures of aggressive colon cancer.
Article in Journal of pathology informatics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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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5 authors.
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Abstract
Background: Characterizing the tumor immune microenvironment (TIME) is essential for understanding anti-tumoral responses in colon cancer. This study introduces TIME Landscaper (TIMEL), a computational framework that uses deep learning to identify tissue structures at the microscopic level and summarizes their distribution across whole-slide images (WSIs) using statistical descriptors reflecting intratumoral heterogeneity for prognostic biomarker discovery. Methods: The performance of six deep learning image classification models (Inception V3, DenseNet-121, ViT-base, UNI, Prov-GigaPath, and Virchow) was evaluated to segment microarchitectural tumor, stromal, and immune areas using nearly 50,000 mini-patches. The selected model was applied to WSIs from discovery (TCGA-COAD; Results: Virchow outperformed other models in segmenting tissue compartments (AUCs: 0.99, 0.98, 0.98 for tumor, stromal, immune components). Slide-level stromal and immune variance correlated with pathologist-assessed metrics ( Conclusion: TIMEL integrates deep learning and statistics to capture histological heterogeneity within the TIME, enhancing prognostic assessment and supporting precision oncology from routine histology.
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