Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective
Abstract
Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer is relatively lower than many leading cancers. Early diagnosis and prognosis of lung cancer are essential to improve the patient's survival rate. With artificial intelligence (AI) approaches widely applied in lung cancer, early diagnosis and prediction have achieved excellent performance in recent years. This review summarizes various types of AI algorithm applications in lung cancer, including natural language processing (NLP), machine learning and deep learning, and reinforcement learning. In addition, we provides evidence regarding the application of AI in lung cancer diagnostic and clinical prognosis. This review aims to elucidate the value of AI in lung cancer diagnosis and prognosis as the novel screening decision-making for the precise treatment of lung cancer patients.
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Integrating multiple biomarkers such as HE4, sVCAM-1, TTR, ApoA2, and CEA can markedly enhance diagnostic performance; one such model reached a sensitivity of 93.33%, specificity of 92.00%, and an AUC of 0.988.
“Additional studies have reported that integrating multiple biomarkers—such as human epididymis protein 4 (HE4), soluble vascular cell adhesion molecule-1 (sVCAM-1), transthyretin (TTR), apolipoprotein A2 (ApoA2), and the carcinoembryonic antigen CEA—can markedly enhance diagnostic performance. One such model reached a sensitivity of 93.33%, specificity of 92.00%, and an AUC of 0.988, indicating”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [24]
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The artificial intelligence and machine learning in lung cancer immunotherapy
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“The analysis of medical images of different organs of the body to detect irregular behavior plays a vital role in the medical field, such as skin cancer [1], brain cancer [2], lung cancer [3], breast cancer [4], and retina [5].”
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- Authors
- Huang, S., Yang, J., Shen, N., Xu, Q., & Zhao, Q.
- Venue
- Seminars in Cancer Biology
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 89
- Pages
- 30-37
- ISSN
- 1044-579X
- Cited by
- 275
- References
- 5
- Language
- en
- Licence
- Funders
- Natural Science Foundation of Chongqing (cstc2021jcyj-msxmX1108)
- National Key Research and Development Program of China (2019YFA0904400)
- Ministry of Education of the People's Republic of China
- Shanghai Science and Technology Development Foundation
- Science and Technology Planning Project of Shenzhen Municipality (SGDX2020110309280301)
- Universidade de Macau (MYRG2022-00143-FHS, MYRG2019-00069-FHS)
- Shenzhen Science and Technology Innovation Commission
- Chongqing Municipal Education Commission (KJQN0202203602, KJQN202203607, KJZD-K202203601)
- Humanities and Social Science Fund of Ministry of Education of the People's Republic of China (22YJCZH213)
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