Diagnostic test accuracy of artificial intelligence-based imaging for lung cancer screening: A systematic review and meta-analysis
Abstract
Background Lung cancer is the principal cause of cancer-related deaths worldwide. Early detection of lung cancer with screening is indispensable to reduce the high morbidity and mortality rates. Artificial intelligence (AI) is widely utilised in healthcare, including in the assessment of medical images. A growing number of reviews studied the application of AI in lung cancer screening, but no overarching meta-analysis has examined the diagnostic test accuracy (DTA) of AI-based imaging for lung cancer screening. Objective To systematically review the DTA of AI-based imaging for lung cancer screening. Methods PubMed, EMBASE, Cochrane Library, CINAHL, IEEE Xplore, Web of Science, ACM Digital Library, Scopus, PsycINFO, and ProQuest Dissertations and Theses were searched from inception to date. Studies that were published in English and that evaluated the performance of AI-based imaging for lung cancer screening were included. Two independent reviewers screened titles and abstracts and used the Quality Assessment of Diagnostic Accuracy Studies-2 tool to appraise the quality of selected studies. Grading of Recommendations Assessment, Development, and Evaluation to diagnostic tests was used to assess the certainty of evidence. Results Twenty-six studies with 150,721 imaging data were included. Hierarchical summary receiver-operating characteristic model used for meta-analysis demonstrated that the pooled sensitivity for AI-based imaging for lung cancer screening was 94.6 % (95 % CI:…
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AI algorithms based on deep learning and convolutional neural networks have demonstrated high sensitivity up to 94.6% and specificity up to 93.6% for lung nodule detection, often matching or surpassing radiologist performance, especially for small nodules.
“AI algorithms, especially those based on deep learning and convolutional neural networks, have demonstrated high sensitivity (up to 94.6%) and specificity (up to 93.6%) for lung nodule detection, often matching or surpassing radiologist performance, especially for small nodules [13,14].”
From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [13]
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Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10 · see citing works
- Authors
- THONG, L. T., CHOU, H. S., CHEW, H. S. J., & LAU, Y.
- Venue
- Lung Cancer
- Publisher
- Elsevier BV
- Published
- 2023-02
- Volume
- 176
- Pages
- 4-13
- ISSN
- 0169-5002
- Cited by
- 64
- References
- 4
- Language
- en
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