Beyond Appearance: Protein-Grounded Relational Reasoning for Structural Pathological Event Recognition

Modern computational pathology has achieved remarkable progress in cancer diagnosis, yet most models still make decisions primarily from histological appearance. This paradigm becomes insufficient for structural pathological events, where visually similar tissue environments can imply opposite diagnoses simply because their constituents are organized differently. We term this ambiguity histological isomerism: a model may recognize the right constituents yet still reach the wrong diagnosis if it fails to understand how they constitute a pathological event. We present FRAME, which moves computational pathology beyond appearance toward biologically grounded structural reasoning. FRAME learns molecularly grounded H&E representations from limited paired H&E-mIF data, organizes local evidence into anatomical targets and their surrounding constituents, and reasons about how these constituents are configured relative to target boundaries. mIF is required only during training, while inference remains H&E-only. With only hundreds to thousands of task-specific training samples, FRAME outperforms large-scale pathology foundation-model baselines by 2.5-5.1 points on general cancer diagnosis and grading. With only dozens of pathologist-corrected ROIs, it further outperforms the strongest pathology foundation-model baselines by an average of 12.5 points on composition-matched histological isomerism hard subsets.