PD-L1 Expression Prediction Using Scalable Multi Instance Transformer

Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of non-small cell lung cancer (NSCLC), benefiting 20–30% of patients. The current clinical standard for initiating ICI therapy is the assessment of Programmed Death-Ligand 1 (PD-L1) status via immunohistochemistry (IHC) on biopsy specimens. However, this invasive procedure presents risks and limitations, highlighting the need for a non-invasive alternative. This study retrospectively analyzed a cohort of 746 patients with stage IV metastatic NSCLC undergoing immunotherapy, divided into training (n = 298), internal validation (n = 75), and testing (n = 360) groups. Thirteen cases with poor image quality were excluded from the analysis. We proposed a Scalable Multi Instance Transformer (SMIT), a deep learning model, to predict PD-L1 expression from chest computed tomography (CT) scans, thereby reducing the need for invasive biopsy procedures. Compared to prior studies, our approach integrates multi-scale features from CT images, enhancing prediction accuracy and robustness. SMIT achieved superior performance in predicting PD-L1 status with precision (0.82), sensitivity (0.83), F1 score (0.83), area under the curve (AUC; 81%), and Precision-Recall AUC (0.80). SMIT’s predictions for PD-L1 status (≥50% or < 50%) were comparable to those derived from IHC-based PD-L1 status, validating its potential as a non-invasive diagnostic tool. Additionally, SMIT’s predictions for progression-free survival (PFS) were on par with IHC-based predictions. The SMIT model represents a significant advancement in the non-invasive prediction of PD-L1 expression in NSCLC, offering a viable alternative to traditional biopsy methods. This innovation could streamline immunotherapy selection, making treatments more accessible and personalized.