United KingdomResearchers at Stanford Medicine have developed an artificial intelligence platform that can analyze standard H&E-stained pathology slides to identify complex cellular interactions within tumors. This technology transforms traditional two-dimensional images into detailed spatial maps showing how cancer cells, immune cells, and stromal cells communicate. The system identifies 10 distinct cellular 'neighborhoods' in non-small cell lung cancer, some of which correlate with poor patient outcomes and resistance to immunotherapy. The method leverages a technique called CODEX, which detects multiple proteins and cell types within tumors but is resource-intensive. To address these limitations, the team trained AI on over 18 million cells from 457 patients, enabling predictions based on existing pathology slides rather than requiring costly experiments. The study, led by Dr. Ruijiang Li and published in the journal Cell, highlights potential advancements in understanding tumor ecosystems and improving cancer treatment strategies.
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