Lung Cancer Risk Prediction Models for Asian Ever-Smokers.

Area of Science:

• Oncology
• Epidemiology
• Biostatistics

Background:

• Lung cancer risk prediction models are crucial for targeted screening.
• The generalizability of Western-developed models to Asian populations is uncertain.
• Accurate risk assessment is vital for effective lung cancer prevention strategies.

Purpose of the Study:

• To evaluate the performance of 11 existing lung cancer risk prediction models in diverse Asian populations.
• To develop and validate refined lung cancer risk prediction models specifically for Asian individuals.
• To address the underestimation of lung cancer risk in specific Asian subgroups by existing models.

Main Methods:

• Pooled analysis of 186,458 Asian ever-smokers from 19 prospective cohorts.
• Assessment of model calibration (expected-to-observed ratio) and discrimination (area under the receiver operating characteristic curve [AUC]).
• Development and external validation of new "Shanghai models" using population-based prospective cohorts.

Main Results:

• The Lung Cancer Death Risk Assessment Tool showed the highest AUC (0.71 for death, 0.69 for incidence).
• Existing models underestimated risk for Asian smokers with <10 pack-years or quitters ≥20 years ago.
• The "Shanghai models" demonstrated marginal overall discrimination improvement but outperformed Western models in low-intensity smokers and long-term quitters.

Conclusions:

• The "Shanghai models" offer comparable overall performance to existing top models.
• These new models significantly enhance lung cancer risk prediction accuracy for low-intensity smokers and long-term quitters in Asia.
• Refined models are necessary to improve lung cancer screening efficacy in diverse Asian populations.