Deep learning of CT imaging predicts PD-L1 expression and immunotherapy response in metastatic NSCLC: A multi-center...

Immune checkpoint inhibitors (ICIs) benefit only a subset of patients with metastatic non-small cell lung cancer (NSCLC), but current selection relies on tissue PD-L1 immunohistochemistry (IHC), which is invasive and prone to sampling bias. We developed and validated SCENT (Scalable Ensemble Transformer), a CT-based deep learning model for noninvasive prediction of PD-L1 status and immunotherapy outcomes. In this retrospective study, 972 stage IV NSCLC patients treated with ICIs at MD Anderson were analyzed; SCENT was developed and validated in 640 patients with paired CT and PD-L1 IHC, and clinical applicability was assessed in an additional 332 CT-only patients. Generalizability was evaluated in independent cohorts from Mayo Clinic (n = 72) and the phase III LONESTAR trial (n = 116), where paired baseline and 3-month CT enabled longitudinal assessment. SCENT classified PD-L1 status (50% or higher vs lower) in the MD Anderson cohort with AUC 0.84 (95% CI 0.785 to 0.887), specificity 83.9%, and sensitivity 85.3%, outperforming clinical and radiomics models; external validation achieved AUC 0.80 (Mayo) and 0.78 (LONESTAR). SCENT-derived PD-L1 stratified progression-free survival (HR 1.49, p < 0.001) and overall survival (HR 1.40, p = 0.009), comparable to IHC, and provided complementary prognostic value when combined with IHC, with concordant low-low patients showing the poorest survival (OS HR 1.45, p = 0.008). In LONESTAR, serial SCENT-inferred PD-L1 status showed a borderline association with 3-month progression without paired post-treatment tissue confirmation. SCENT is a generalizable CT-based virtual biopsy for baseline PD-L1 prediction and complementary tissue IHC stratification, with longitudinal use requiring prospective validation.