Decoding Lung Cancer with Artificial Intelligence: A Path to Personalized Diagnostics and Therapy

Non-small-cell lung cancer (NSCLC) represents the majority of lung cancer cases and remains the leading cause of cancer-related mortality worldwide. Early detection, accurate staging, and individualized therapy are crucial for improving patient survival. Recent advances in artificial intelligence (AI) have transformed NSCLC care through the automated analysis of imaging, pathology, and molecular datasets. Deep learning (DL) and machine learning (ML) models, particularly convolutional neural networks (CNNs), have demonstrated high accuracy in tumor detection, histologic classification, biomarker prediction (PD-L1, EGFR, KRAS) and prognosis assessment. Integration of the AI with whole slide imaging (WSI), computed tomography (CT), PET/CT and liquid biopsies enables non-invasive tumor characterization, early recurrence detection and prediction of treatment response. Multimodal AI approaches that combine imaging, genomic, and clinical data offer superior patient stratification and personalized treatment planning compared with traditional methods. Beyond diagnosis, AI facilitates therapeutic development through radiomics based nodule classification, multi-omics data interpretation, and drug discovery. Radiomics, in particular, extracts quantitative imaging biomarkers that capture tumor heterogeneity beyond human perception. The AI further supports clinical decision-making in surgery, radiotherapy, and systemic therapy, enhancing workflow efficiency. Despite challenges in data quality, interpretability, and clinical adoption, AI holds significant promise for advancing precision oncology and improving outcomes in NSCLC management.