Multi-scale histology reveals localized ageing signatures in normal human colon
Year: 2026
Type: preprint
Abstract: Ageing is accompanied by heterogeneous changes in tissue structure, cellular composition, and microenvironments, yet how these changes are reflected in routine human histology remains poorly understood. Here, we introduce a histology-first, multi-scale digital pathology framework to study human colon ageing using hematoxylin and eosin (H&E) whole-slide images from clinically normal colon biopsies. The framework combines pathology foundation-model patch embeddings, attention-based multiple-instance learning, cell-level morphometric profiling, and prototype-based tissue compartmentalization. We show that chronological age can be predicted from normal colon tissue using mesoscopic representations derived from pathology foundation-model patch embeddings and attention-based multiple-instance learning. Incorporating cell-type-level morpho-metric features further improves prediction and refines the spatial allocation of model attention. By integrating attention maps with prototype-defined tissue compartments, we identify localized age-associated differences within stromal, epithelial, and lymphoid mucosal niches. Finally, we show that prediction-discordant cases exhibit cellular profiles shifted toward the model-assigned age group, suggesting that some apparent model errors may reflect tissue-level age discordance beyond chronological age. Together, these findings show that routine H&E histology captures spatially localized signatures of normal human colon ageing and provides a scalable framework for linking tissue morphology with biological ageing state.
Institution: Sheba Medical Center
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7223455793