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Published on: May 20, 2016
Use and impact of risk-based eligibility models in low-dose computed tomography lung cancer screening: a systematic
Veronika Elisabeth Mikl1,2, Mohammad Azizzadeh3,4, Marie-Kathrin Breyer3,4,5
1Doctoral Program Public Health, Institute of Outcomes Research, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.
Objectives:
Low-dose computed tomography lung cancer screening (LDCT-LCS) significantly reduces mortality, yet identifying high-risk individuals while reducing over-screening remains challenging. Risk-based eligibility models are promising to optimize participant selection. Within the European context, we assessed the types, outcomes, and impact of these risk-based eligibility models for LDCT-LCS.
Methods:
We systematically reviewed prediction model studies (PROSPERO CRD42025648906) across EMBASE, MEDLINE, and Cochrane Central Register of Controlled Trials. We included original research on adults aged 18+ at risk for LC, excluding East-Asian populations. Study characteristics, model type, performance and outcomes were extracted for narrative synthesis.
Results:
The review included 46 articles (2003-2025), identifying 39 risk-prediction models. Models were primarily statistical (72%); PLCOm2012 was most frequent. Age (100%), smoking duration (91%), and intensity (72%) were the most common variables. Risk models improved eligibility and demonstrated cost-effectiveness over traditional criteria, though heterogeneity and population-specific calibration remain challenges.
Conclusion:
Risk-based eligibility models improve LDCT-LCS efficiency by enhancing detection rates and personalization. While PLCOm2012 is prominent, addressing model heterogeneity, ensuring population-specific validation, and calibration are crucial to optimize LDCT-LCS outcomes in Europe.
Systematic Review Registration:
Identifier CRD42025648906.
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