Explainable Machine Learning to Predict Treatment Response in Advanced Non-Small Cell Lung Cancer.

Purpose: Immune checkpoint inhibitors (ICIs) have demonstrated promise in the treatment of various cancers. Single-drug ICI therapy (immuno-oncology [IO] monotherapy) that targets PD-L1 is the standard of care in patients with advanced non-small cell lung cancer (NSCLC) with PD-L1 expression ≥50%. We sought to find out if a machine learning (ML) algorithm can perform better as a predictive biomarker than PD-L1 alone.

Methods: Using a real-world, nationwide electronic health record-derived deidentified database of 38,048 patients with advanced NSCLC, we trained binary prediction algorithms to predict likelihood of 12-month progression-free survival (PFS; 12-month PFS) and 12-month overall survival (OS; 12-month OS) from initiation of first-line therapy. We evaluated the algorithms by calculating the AUC on the test set. We plotted Kaplan-Meier curves and fit Cox survival models comparing survival between patients who were classified as low-risk (LR) for 12-month disease progression or 12-month mortality versus those classified as high-risk.

Results: The ML algorithms achieved an AUC of 0.701 (95% CI, 0.689 to 0.714) and 0.718 (95% CI, 0.707 to 0.730) for 12-month PFS and 12-month OS, respectively. Patients in the LR group had lower 12-month disease progression (hazard ratio [HR], 0.47 [95% CI, 0.45 to 0.50]; < .001) and 12-month all-cause mortality (HR, 0.31 [95% CI, 0.29 to 0.34]; < .0001) compared with the high-risk group. Patients deemed LR for disease progression and mortality on IO monotherapy were less likely to progress (HR, 0.53 [95% CI, 0.46 to 0.61]; < .0001) or die (HR, 0.30 [95% CI, 0.24 to 0.37]; < .001) compared with the high-risk group.

Conclusion: An ML algorithm can more accurately predict response to first-line therapy, including IO monotherapy, in patients with advanced NSCLC, compared with PD-L1 alone. ML may better aid clinical decision making in oncology than a single biomarker.