LUNG CANCER RISK PREDICTION MODELS: A REVIEW
Main Article Content
Abstract
Objective: To systematically review lung cancer risk prediction models worldwide in order to describe their characteristics, risk factors, predictive performance, and applicability, thereby providing recommendations for Vietnam. Methods: A systematic review was conducted using both domestic and international databases up to January 2025. Studies were selected according to PICOS criteria and the PRISMA guidelines. Model performance was evaluated using sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC). Results: A total of 72 studies were included in the analysis. Most prediction models were developed using logistic regression or Cox regression. Common risk factors included age, sex, smoking status and intensity, body mass index (BMI), family history of lung cancer, and chronic lung diseases. Models developed for smokers demonstrated moderate to good predictive performance (AUC 0.77–0.88), whereas models for non-smokers remain limited but are increasingly studied, particularly in Asian populations. Conclusion: Numerous lung cancer risk prediction models have been developed; however, most are based on Western populations. Their application in Vietnam requires validation and calibration to ensure they are suitable for local epidemiological characteristics.
Article Details
Keywords
risk prediction model, lung cancer, risk factors
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