Prediction models for early detection and diagnosis of lung cancer in people who have never smoked: a systematic review and critical appraisal

BACKGROUND: Never-smokers can develop lung cancer, but this possibility is often overlooked by patients and physicians. We conducted a systematic review to identify existing prediction models that could be implemented in primary care or at a population level to facilitate early detection and diagnosis of lung cancer in never-smokers. METHODS: This study was registered on PROSPERO (ref: CRD42023374471). We systematically searched literature on the Medline, Embase, PsycINFO, and CINAHL databases published before 22 January 2025, with additional hand searching. Primary care or population-level data from subjects including never-smokers had to be used for model derivation. Studies involving specialized tests (radiological or genetic) were excluded. We used CHARMS to guide data extraction and critical appraisal, the TRIPOD statement for model evaluation, and PROBAST for risk of bias assessment. RESULTS: Among 2,431 studies retrieved, 31 models were included. Eight models were developed exclusively for never-smokers, but none were at low risk of bias. Among 23 models derived from never- and ever-smokers, five were at low risk of bias. Two were diagnostic models with 1-2 years prediction horizons, and three were prognostic models with 2-10 years prediction horizons. Methodological issues from the included studies were identified, analyzed, and discussed. CONCLUSION: This systematic review critically appraises and summarizes key information from currently available prediction models for lung cancer in never-smokers. The findings can inform future research to improve care and services for this underserved population.