Lung cancer is one of the most prevalent and lethal malignant tumors globally, characterized by high morbidity and mortality rates. Early diagnosis is crucial for enhancing treatment efficacy, reducing mortality, and improving patients’ quality of life. Traditional manual interpretation of computed tomography (CT) images—the primary screening tool for lung cancer—is often limited by subjectivity, heavy workload, and potential missed diagnoses. Artificial Intelligence (AI) methodologies have emerged as indispensable assets for radiologists, empowering them to conduct in-depth analyses of lung CT scans. This technology facilitates a more streamlined and unbiased evaluation of anatomical structures and pathological features depicted in chest CT images. Driven by advances in deep learning models such as convolutional neural networks (CNNs), the accuracy and robustness of AI methods for lung cancer diagnosis have been significantly improved, providing reliable references for clinical practice. This paper presents a brief review of the applications of AI methods in lung cancer diagnosis based on CT imaging in recent years. It systematically organizes and elaborates on the applications of AI methods represented by deep learning in pulmonary nodule detection, lung tumor segmentation, benign-malignant classification of lung cancer, and lung cancer-related gene mutation detection. Additionally, it analyzes the performance of different models on relevant datasets. Furthermore, the limitations of AI in CT imaging-based lung cancer diagnosis and future research directions are summarized. Through this survey, we aim to provide a concise and comprehensive overview of the current status of lung cancer diagnosis using CT-AI integrated systems, and ultimately promote the development and clinical application of these technologies in the early and accurate detection of lung cancer.
Wang et al. (Tue,) studied this question.