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Risk prediction for lung cancer screening: a systematic review and meta-regression
Ramin Rezaeianzadeh1, Crystal Leung1, Soo Jeong Kim1
1Respiratory Evaluation Sciences Program, Collaboration for Outcomes Research and Evaluation, Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, BC, Canada.
Summary
New lung cancer (LC) risk models show promise for screening selection and nodule classification. However, most require further external validation before clinical use to ensure reliability and practical value in lung cancer detection.
Area of Science:
- Oncology
- Radiology
- Biostatistics
Background:
- Lung cancer (LC) is a leading cause of cancer death, often diagnosed late.
- Low-dose computed tomography (LDCT) screening reduces mortality in high-risk individuals.
- Recent guideline expansions necessitate updated risk assessment models for LC screening.
Conclusions:
- Numerous post-2020 risk models for lung cancer show potential.
- Most models lack sufficient external validation and real-world performance data for clinical adoption.
- Future research should focus on validation, comparative studies, and implementation rather than new model development.
