Ruimtelijke analyse van tumorimmuunmicro-omgeving voorspelt recidiefvrije overleving bij rectumcarcinoom

The tumor immune microenvironment in colorectal cancer has emerged as a major determinant of patient outcome, with tumor-infiltrating lymphocytes, particularly CD8⁺ T cells, mediating antitumor immunity. Standardized density-based metrics such as the Immunoscore have demonstrated prognostic value but cannot capture the spatial interactions among cells that underlie immune-tumor biology. We introduce a deep learning framework that integrates two complementary axes of analysis: Cu-Cyto, a deep learning-based image cytometry platform that detects and classifies approximately twenty cell types from standard immunohistochemistry-stained whole-slide images, using a bit-pattern kernel-filtering algorithm to prevent multi-counting and an off-target labeling strategy for precise nuclear-center localization; and the Co-Localization Index, which converts the classification probabilities and coordinates produced by Cu-Cyto into a single quantitative measure of co-localization between two or three cell types. We illustrate this framework through CD103⁺CD8⁺ tissue-resident memory-like T cells in rectal cancer treated with neoadjuvant chemoradiotherapy: their stromal but not intratumoral density independently predicts relapse-free survival. This divergence, which compartment-aware density alone cannot explain, motivates a metric that captures within-compartment spatial relationships. The Co-Localization Index extends naturally to three-cell interactions, providing a quantitative readout of the tri-cellular biology among CD103⁺CD8⁺ T cells, tumor cells, and stromal components. Prospective validation in independent rectal-cancer cohorts, addressing watch-and-wait organ preservation and adjuvant chemotherapy decisions, will determine whether this spatial-cellular framework translates into routine clinical decision support with potential extension to other solid tumor contexts.