Prostate-TriMod: A Multi-Scale Tri-Modal Histology Dataset Integrating Morphology, Immune Patterns, and Clinical Outcomes

Accurate prognostic assessment of prostate cancer (PCa) requires an integrated understanding of tumor morphology and the immune microenvironment. We present Prostate-TriMod, a novel tri-modal histology dataset designed to integrate high-resolution morphology with spatial immune patterns and clinical outcomes. This dataset, generated from the Cell DIVE multiplexed imaging platform, consists of three synchronized modalities: (1) multiscale virtual H&E (vH&E) tiles (224px, 256px, 512px, and 2040px), (2) spatial tissue maps identifying cancerous/non-cancerous epithelial, stroma and immune cell populations (via TOPAZ and CAT models), and (3) text captions generated from single-cell data and patterns. The dataset includes comprehensive clinical annotations, including Grade Groups and biochemical recurrence (BCR) status. By providing high-fidelity alignment between morphology, spatial tissue maps, and textual descriptions, this resource enables the development of advanced multimodal AI models including vision-language models (VLMs) and deep learning frameworks for precision oncology.