Opportunities for Predicting Lung Cancer Screening Nonadherence: A Systematic Review and Meta-Analysis
Year: 2025
Type: review
Study designs: Systematic Review, Meta-Analysis
Abstract: Purpose The low adherence rate to annual lung cancer screening (LCS) reduces potential mortality benefits. Interventions to improve adherence often do not consider individualized risk for nonadherence, which is vital in informing the design of tailored interventions. The authors systematically reviewed the literature and conducted a meta-analysis on predicting LCS nonadherence risk using machine learning. Methods The authors searched citation databases such as PubMed, Embase, and Web of Science for original studies that mentioned LCS nonadherence risk assessment between April 28, 2014, and May 8, 2025. Study characteristics, nonadherence information, and prediction model performance were extracted. The review protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO CRD420251049715). Results Nine studies published between 2020 and 2025 were included in this systematic review, with sample sizes varying between 168 and 28,294, from various institutional settings. Four of the nine included studies reported prediction model performance, with a pooled cross-validated or internal test area under the receiver operating characteristics curve of 0.80 (95% confidence interval, 0.64-0.90) across three distinct study populations and a relatively small sample size between 168 and 2,430, with large heterogeneity detected across studies ( P < .05, Cochran's Q test; I 2 = 98.7%). The authors also explored the feasibility of leveraging four national databases to support future model development and validation efforts aimed at improving LCS adherence. Conclusions Machine learning models that can predict the individual risk for LCS nonadherence are underdeveloped. Radiologists and other stakeholders should invest in the curation of large, multicenter, national databases to facilitate the development and validation of models that identify patients who are in the greatest need of tailored interventions to improve adherence and enhance patient outcomes.
Institution: University of California, Los Angeles
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,810
Funders: V Foundation for Cancer Research, Radiological Society of North America, National Institutes of Health, Agency for Healthcare Research and Quality, University of California, Los Angeles, Kaiser Foundation Research Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, Jonsson Comprehensive Cancer Center, Janssen Pharmaceuticals, Clinical and Translational Science Institute, University of California, Los Angeles
OpenAlex ID: W4416839708