Clinical lung cancer · 2026
Development and Validation of Lung Cancer Risk Prediction Models in the Singapore Chinese Population.
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Juang Yah Ru, Chen Wenjia, Ng Alvin Jing Hui, Sim Xueling, Lim Darren Wan-Teck, Yuan Jian-Min, Seow Adeline, Seow Wei Jie
Аннотация
BACKGROUND: Despite effective screening technologies for smokers, lung cancer is often diagnosed late, especially among never-smokers who fall outside current screening guidelines. We developed 10-year risk prediction models for sex and smoking subgroups to support more precise identification of high-risk individuals in clinical settings.
PATIENTS AND METHODS: Using the Singapore Chinese Health Study (n = 63,187; recruitment period: 1993-1998), we developed a robust Cox regression model to predict 10-year lung cancer risk. Essential predictors were identified from sociodemographic, smoking-related, environmental exposure, reproductive, and dietary factors using Elastic Net regularization with 10-fold cross-validation. External validation was conducted using the Singapore Multi-Ethnic Cohort (n = 12,944; recruitment period: 2004-2010). Sex- and smoking-specific risk models were developed using the same approach.
RESULTS: The overall risk model demonstrated strong discrimination and calibration on external validation (C-index = 0.821; 95% CI, 0.738-0.895). Ranked by relative importance, key predictors included age, smoking exposure (pack-years, smoking status), body mass index, education, sex, environmental tobacco smoke exposure, and dietary factors (tea and fruit consumption). The ever-smoker risk model included age, body mass index, pack-years, education, and weekly fruit consumption (C-index = 0.734), while the never-smoker risk model included age, sex, environmental tobacco smoke exposure, personal cancer history, and allergic rhinitis (C-index = 0.702). Sex-specific risk models showed good discrimination (male C-index = 0.785; female C-index = 0.769), with the female risk model influenced by reproductive and medical history factors.
CONCLUSION: These subgroup-specific risk models provide additional insights into lung cancer risk assessment beyond smoking history and, with further real-world evaluation, may support more tailored risk stratification in Singapore's clinical settings.
PATIENTS AND METHODS: Using the Singapore Chinese Health Study (n = 63,187; recruitment period: 1993-1998), we developed a robust Cox regression model to predict 10-year lung cancer risk. Essential predictors were identified from sociodemographic, smoking-related, environmental exposure, reproductive, and dietary factors using Elastic Net regularization with 10-fold cross-validation. External validation was conducted using the Singapore Multi-Ethnic Cohort (n = 12,944; recruitment period: 2004-2010). Sex- and smoking-specific risk models were developed using the same approach.
RESULTS: The overall risk model demonstrated strong discrimination and calibration on external validation (C-index = 0.821; 95% CI, 0.738-0.895). Ranked by relative importance, key predictors included age, smoking exposure (pack-years, smoking status), body mass index, education, sex, environmental tobacco smoke exposure, and dietary factors (tea and fruit consumption). The ever-smoker risk model included age, body mass index, pack-years, education, and weekly fruit consumption (C-index = 0.734), while the never-smoker risk model included age, sex, environmental tobacco smoke exposure, personal cancer history, and allergic rhinitis (C-index = 0.702). Sex-specific risk models showed good discrimination (male C-index = 0.785; female C-index = 0.769), with the female risk model influenced by reproductive and medical history factors.
CONCLUSION: These subgroup-specific risk models provide additional insights into lung cancer risk assessment beyond smoking history and, with further real-world evaluation, may support more tailored risk stratification in Singapore's clinical settings.