Development and Validation of a Machine Learning Model to Explore Tyrosine Kinase Inhibitor Response in Patients With Stage IV EGFR Variant–Positive Non–Small Cell Lung Cancer

Key Points Question Can an end-to-end deep learning network model be used to identify patients with stage IV epidermal growth factor receptor ( EGFR ) variant–positive non–small cell lung cancer who will not benefit from EGFR–tyrosine kinase inhibitor (TKI) therapy? Findings In this diagnostic/prognostic study of 342 patients receiving EGFR-TKI therapy, a bidirectional generative adversarial network model demonstrated a 36% reduction in the progression-free survival of patients at high risk for rapid progression but no significant difference in the progression-free survival between these patients and those receiving first-line chemotherapy. The proposed deep learning semantic signature eliminated all manual interventions required while using previous radiomics methods and had a better prognostic performance. Meaning An end-to-end clinically applicable approach is promising for quantitatively identifying the benefit of EGFR-TKI therapy.