From prediction to interpretation in computational pathology.

PathPrism enables interpretable spatial tissue representations that enhance biomarker discovery and clinical predictions from routine histopathology slides.

• Why it matters: Understanding tissue organization is crucial for advancing cancer diagnosis and treatment, yet current methods often lack interpretability, limiting biological insights and clinical utility.
• What they did: Liang et al. developed PathPrism, a framework that creates spatial tissue representations, facilitating the transition from black-box models to biologically meaningful interpretations in computational pathology.
• The result: This approach supports biomarker discovery, clinical prediction, and hypothesis generation, promoting a shift toward more transparent and insightful analysis of histopathology data.