Artificial intelligence in lung cancer screening: Detection, classification, prediction, and prognosis

• Published in: Cancer medicine (Malden, MA) Vol. 13; no. 7; pp. e7140 - n/a
• Main Authors: Quanyang, Wu, Yao, Huang, Sicong, Wang, Linlin, Qi, Zewei, Zhang, Donghui, Hou, Hongjia, Li, Shijun, Zhao
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 01.04.2024; Wiley
• Subjects: Algorithms; Artificial Intelligence; Automation; Blood vessels; Cancer screening; Classification; computed tomography; convolutional neural network; Deep learning; Early Detection of Cancer; Humans; Information processing; Lung; Lung cancer; Lung diseases; Lung Neoplasms - diagnosis; Lung nodules; Medical prognosis; Medical screening; Methods; Mortality; Neural networks; Prognosis; Segmentation; Tomography, X-Ray Computed - methods; computed tomography; lung cancer; deep learning; convolutional neural network; artificial intelligence
• ISSN: 2045-7634; 2045-7634
• DOI: 10.1002/cam4.7140

Background The exceptional capabilities of artificial intelligence (AI) in extracting image information and processing complex models have led to its recognition across various medical fields. With the continuous evolution of AI technologies based on deep learning, particularly the advent of convolutional neural networks (CNNs), AI presents an expanded horizon of applications in lung cancer screening, including lung segmentation, nodule detection, false‐positive reduction, nodule classification, and prognosis. Methodology This review initially analyzes the current status of AI technologies. It then explores the applications of AI in lung cancer screening, including lung segmentation, nodule detection, and classification, and assesses the potential of AI in enhancing the sensitivity of nodule detection and reducing false‐positive rates. Finally, it addresses the challenges and future directions of AI in lung cancer screening. Results AI holds substantial prospects in lung cancer screening. It demonstrates significant potential in improving nodule detection sensitivity, reducing false‐positive rates, and classifying nodules, while also showing value in predicting nodule growth and pathological/genetic typing. Conclusions AI offers a promising supportive approach to lung cancer screening, presenting considerable potential in enhancing nodule detection sensitivity, reducing false‐positive rates, and classifying nodules. However, the universality and interpretability of AI results need further enhancement. Future research should focus on the large‐scale validation of new deep learning‐based algorithms and multi‐center studies to improve the efficacy of AI in lung cancer screening.