Picture Health has developed an AI-based immunotherapy response score (PIRe) to improve patient selection for immune checkpoint inhibitor (ICI) therapy in late-stage non-small cell lung cancer (NSCLC). By analyzing tumor-infiltrating lymphocyte (TIL) density (denTIL) and their spatial interactions with tumor cells (spaTIL) from pre-treatment tissue samples, PIRe + PD-L1 was shown to predict ICI response with an AUC of 0.96, outperforming PD-L1 TPS alone. This suggests that PIRe could help better stratify patients, particularly those with high PD-L1 expression, who may benefit from escalated treatment strategies such as chemo-immunotherapy (chemo-ICI). The model's ability to enhance predictive accuracy was demonstrated across multiple institutions, though further validation in larger cohorts is needed to confirm its clinical utility in optimizing NSCLC treatment.
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