Development and validation of an interpretable machine learning model for predicting progression-free survival after immunotherapy in patients with non-small cell lung cancer: a multicenter study

This study establishes a robust, interpretable ctDNA-derived machine learning algorithm for predicting PFS in NSCLC patients receiving immune checkpoint inhibitors. The identification of TP53, BRCA2, and NOTCH1 as biologically plausible predictive biomarkers advances understanding of immunotherapy response mechanisms and enables clinically actionable risk stratification to guide therapeutic decision-making. These findings underscore the need for prospective multicenter validation to facilitate translation into precision oncology practice.