POSTER: Using Artificial Intelligence to Predict Response to Immunotherapy

Key Concepts:

Pathological interpretations applied to PD-L1 IHC as a response biomarker are becoming more complex, going beyond simple tumor proportion score (TPS) and requiring more complex diagnostic algorithms that evaluate the role of PDL1 expression in tumor cells, immune cells in the tumor microenvironment (TME), and tumor-infiltrating lymphocytes (TILs), all whose spatial relationships are critical for understanding the immune contexture.

This complex matrix of several different biological cell types and spatial relationships can quickly become impossible for a pathologist to record and report successfully. By applying Flagship’s cTA AI to existing PD-L1 IHC companion diagnostics, clinical laboratories can now go beyond using tissue image analysis for improving objectivity and reproducibility and can also create entirely new scoring approaches from existing PD-L1 IHC companion diagnostics to improve clinical performance.

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