Deep learning to predict treatment response of immune checkpoint inhibitors from pretreatment chest X-rays in non–small-cell lung cancer.

e13628 Background: Despite the wide availability and cost-effectiveness of chest X-rays (CXRs), their potential as a biomarker for immune checkpoint inhibitor (ICI) response in non-small cell lung cancer (NSCLC) remains largely unexplored. We developed and validated a novel deep learning model that predicts ICI response from a single pre-treatment CXR, independent of histopathologic biomarkers including PD-L1 expression and immune phenotype. Methods: A retrospective dataset of 2,905 NSCLC patients at Samsung Medical Center was collected, including pretreatment CXRs and H&E whole slide images (WSI). A deep learning model was trained using 1,939 pre-ICI CXRs to predict ICI outcomes, with time to next treatment (TTNT) within 6 months as a proxy. To evaluate regimen-specific performance, the model was tested on pretreatment CXRs from three patient groups defined by first-line regimen: A (N = 119, ICI monotherapy), B (N = 327, platinum-based chemotherapy), and C (N = 520, ICI-chemotherapy combination). To interpret the model's behavior, we generated segmentation masks for tumor regions and lung fields using a separate model and injected white noise at various signal-to-noise ratios (SNR). We assessed tumor infiltrating lymphocyte (TIL) density and immune phenotype on 113 paired H&E WSIs from test set A using Lunit SCOPE IO, a deep learning H&E analyzer, to correlate CXR predictions with the tumor microenvironment. Results: The model achieved an area under the ROC curve (AUC) of 0.745 in test set A, with predicted good responders showing significantly prolonged TTNT (median: 12.0 vs 2.0 months, hazard ratio [HR] 0.41, p < 0.001) and OS (median: 24.7 vs 6.0 months, HR 0.42, p < 0.001) compared to predicted non-responders. The model's AUC was 0.531 in test set B (predicting TTNT after chemotherapy) and 0.654 in test set C (ICI-chemotherapy combination), demonstrating specificity of the model to ICI. Noise injection in the nodule region and lung field led to a decrease in AUC, with the most significant decrease in extra-tumor regions of the ipsilateral lung. Prediction scores were not correlated with intratumoral TIL density (r = 0.07, p = 0.44) or PD-L1 expression (r = -0.08, p = 0.56). The prediction of the CXR model for ICI monotherapy (test set A) was independent of high PD-L1 expression (PD-L1≥50%, HR 0.43, p = 0.02; PD-L1 < 50%, HR 0.38, p = 0.13) or immune phenotypes determined by TIL enrichment (Inflamed: HR 0.48, p < 0.05; Non-inflamed: HR 0.38, p < 0.001). Conclusions: A deep learning CXR model predicted ICI response in NSCLC regardless of PD-L1 expression and immune phenotype. This work suggests that CXRs contain intrinsic information relevant to ICI response, achieving competitive performance with state-of-the-art CT or histology based approaches despite relying on broader lung features.