From prediction to interpretation in computational pathology

PathPrism, a new computational pathology framework reported in Cancer Cell, demonstrates that interpretable spatial representations of tissue organization can support biomarker discovery, clinical prediction, and hypothesis generation from routine H&E histopathology slides. The approach marks a shift from black-box deep learning models toward biologically meaningful, explainable outputs in digital pathology. By linking tissue architecture patterns to clinical outcomes, PathPrism could accelerate translational research across multiple cancer types.