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0The paper introduce PathPrism, an innovative AI framework designed to improve cancer research by converting complex medical images into interpretable spatial biomarkers. Unlike traditional "black-box" models that offer little insight into their decision-making, this system deconstructs tissue architecture into quantified semantic maps and pathologically grounded features. By analyzing over 7,000 patients, the researchers demonstrated that these transparent representations can accurately predict patient survival, genetic mutations, and treatment efficacy. A key feature, VirtualWSI, allows scientists to perform digital experiments by altering tissue components to see how such changes impact clinical risk. Furthermore, the framework utilizes large language models to help experts organize these findings into actionable biological hypotheses. Ultimately, PathPrism bridges the gap between high-performance machine learning and human-understandable pathology, offering a scalable tool for personalized medicine.
References:
Liang J, Jiang X, Reitsam N G, et al. Spatial biomarker discovery via interpretable semantic learning in histopathology[J]. Cancer Cell, 2026.
