Abstract
With only 30% of non-small cell lung cancer (NSCLC) patients showing a durable disease control to immune-oncology drugs, biomarkers are expected to enable more personalized treatment. We have applied statistical and machine learning methods to sequential serum protein measurements to predict early disease progression in immunotherapy treated NSCLC patients. This study evaluates the potential impact of introducing such a prediction model in clinical practice on cancer management and subsequent costs.
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