Radiomics and Transformer-Based Deep Learning for Non-Invasive Prediction of PD-L1 Expression in Non-Small Cell Lung Cancer: A Paradigm Shift in Precision Oncology
Traditional biomarker testing for Programmed Death-Ligand 1 (PD-L1) in Non-Small Cell Lung Cancer (NSCLC) remains invasive and costly. This study proposes a non-invasive alternative by integrating radiomic features extracted from CT scans with advanced deep learning architectures. We evaluated Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Recurrent Neural Networks (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). Our results demonstrate that Transformer-based models significantly outperform conventional appro