Lung cancer (LC) remains a leading cause of cancer-related deaths globally, with early detection crucial for better outcomes. Advances in low-dose computed tomography (LDCT) have significantly improved early screening, detecting over 80% of LC cases at early stages and achieving a 10-year survival rate of up to 88%. However, accurately detecting and classifying pulmonary nodules remains challenging. Artificial intelligence (AI), particularly convolutional neural networks, has shown great promise in automating lung segmentation, nodule detection, classification, and prognosis prediction, reducing errors and improving efficiency.
AI-driven deep learning models outperform traditional methods by autonomously analyzing medical images to classify nodules, predict histological types, and assess tumor growth. Yet, barriers such as limited annotated datasets, poor result interpretability, and generalization issues hinder wider adoption. As AI progresses, its integration into LC screening is expected to revolutionize early detection and clinical decision-making, ultimately improving patient care.
Reference: Quanyang W, Yao H, Sicong W, et al. Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis. Cancer Med. 2024;13(7):e7140. doi: 10.1002/cam4.7140.
