Abstract
Programmed death ligand-1 (PD-L1) expression is a key biomarker for identifying non-small cell lung cancer (NSCLC) patients eligible for immunotherapy, but its immunohistochemistry assessment is subject to high interobserver variability. This study aimed to develop and validate an automated system based on artificial intelligence (AI) to detect and classify PD-L1-positive cells in digital lung biopsies from Latin American patients with NSCLC. A total of 141 biopsies were digitized, and deep learning models were trained for tissue segmentation and analysis. YOLOv8 was used for cell detection and ResNet50 for classification into four categories (tumor or non-tumor cells, PD-L1 positive or negative). The tumor proportion score (TPS) was calculated using these classifications. The YOLOv8 model achieved 76% precision and a mAP@0.5 of 63.7% in cell detection, whereas ResNet50 reached 85.2% sensitivity in identifying PD-L1-positive tumor cells. The TPS estimated by the system demonstrated 97% concordance with manual evaluation by an expert pathologist. In addition, spatial PD-L1 maps revealed intratumoral heterogeneity, supporting its relevance for immunotherapy decisions. These findings show that automated PD-L1 quantification using AI is feasible and accurate, improves diagnostic reproducibility, and supports personalized treatment decisions, especially in resource-limited settings.