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Review
. 2024 Apr;13(7):e7140.
doi: 10.1002/cam4.7140.

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

Wu Quanyang  1 , Huang Yao  1 , Wang Sicong  2 , Qi Linlin  1 , Zhang Zewei  3 , Hou Donghui  1 , Li Hongjia  3 , Zhao Shijun  1
Affiliations
Review

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

Wu Quanyang et al. Cancer Med. 2024 Apr.

Abstract

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.

Keywords: artificial intelligence; computed tomography; convolutional neural network; deep learning; lung cancer.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Function diagram of the use of AI for detection, classification, prediction, and prognosis of lung cancer screening. AI, artificial intelligence.
FIGURE 2
FIGURE 2
Convolutional neural network training model for lung cancer.
See this image and copyright information in PMC

References

    1. Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209‐249. doi:10.3322/caac.21660 - DOI - PubMed
    1. Siegel RL, Miller KD, Fuchs HE, Jemal A. Cancer statistics, 2022. CA Cancer J Clin. 2022;72:7‐33. doi:10.3322/caac.21708 - DOI - PubMed
    1. Ettinger DS. Ten years of progress in non‐small cell lung cancer. J Natl Compr Cancer Netw. 2012;10:292‐295. doi:10.1200/JCO.2011.39.8594 - DOI - PubMed
    1. Gettinger S, Horn L, Jackman D, et al. Five‐year follow‐up of nivolumab in previously treated advanced non–small‐cell lung cancer: results from the CA209‐003 study. J Clin Oncol. 2018;36:1675‐1684. doi:10.1200/JCO.2017.77.0412 - DOI - PubMed
    1. International Early Lung Cancer Action Program Investigators . Survival of patients with stage I lung cancer detected on CT screening. N Engl J Med. 2006;355:1763‐1771. doi:10.1056/NEJMoa060476 - DOI - PubMed