No abstract available.
Magnetic Resonance Imaging* ; Optic Neuropathy, Ischemic*

No abstract available.
Atrophy* ; Brain* ; Leukoencephalopathies*

Background: Lung cancer has a high incidence and mortality worldwide, and smoking, age, sex, and body mass index are known risk factors. Using a health examination cohort, we constructed a comprehensive lung cancer risk-prediction model. Methods: This study comprised 308,804 adults aged 20 years and older who underwent health examinations at one general hospital in Korea, from 2011 to 2018. We developed a lung cancer risk prediction model using a multivariate Cox proportional hazards regression analysis for lung cancer risk factors and estimated the hazard ratios and coefficients. The model evaluation included discrimination and calibration assessments. Results: Among the 308,804 adults in the study cohort, there were 338 (0.11%) patients lung cancer, with 215 males (0.07% of 169,420 males) and 123 females (0.04% of 139,384 females). The prevalence of lung cancer was higher in males and females aged over 60 years. Age, sex, body mass index, and smoking behavior were identified as risk factors for lung cancer prevalence in this model through multivariate Cox proportional hazards analysis. The C-statistic of the development cohort was 0.785 (0.749, 0.821) and that of the validation cohort was 0.823 (0.769, 0.878). Conclusion Our lung cancer risk prediction model showed statistical significance, similar to previous prediction models, among variables that included young age, female sex, and body mass index. Future improvements should focus on population-wide applicability and associated health examination policies.

Background: Lung cancer has a high incidence and mortality worldwide, and smoking, age, sex, and body mass index are known risk factors. Using a health examination cohort, we constructed a comprehensive lung cancer risk-prediction model. Methods: This study comprised 308,804 adults aged 20 years and older who underwent health examinations at one general hospital in Korea, from 2011 to 2018. We developed a lung cancer risk prediction model using a multivariate Cox proportional hazards regression analysis for lung cancer risk factors and estimated the hazard ratios and coefficients. The model evaluation included discrimination and calibration assessments. Results: Among the 308,804 adults in the study cohort, there were 338 (0.11%) patients lung cancer, with 215 males (0.07% of 169,420 males) and 123 females (0.04% of 139,384 females). The prevalence of lung cancer was higher in males and females aged over 60 years. Age, sex, body mass index, and smoking behavior were identified as risk factors for lung cancer prevalence in this model through multivariate Cox proportional hazards analysis. The C-statistic of the development cohort was 0.785 (0.749, 0.821) and that of the validation cohort was 0.823 (0.769, 0.878). Conclusion Our lung cancer risk prediction model showed statistical significance, similar to previous prediction models, among variables that included young age, female sex, and body mass index. Future improvements should focus on population-wide applicability and associated health examination policies.

Background: Lung cancer has a high incidence and mortality worldwide, and smoking, age, sex, and body mass index are known risk factors. Using a health examination cohort, we constructed a comprehensive lung cancer risk-prediction model. Methods: This study comprised 308,804 adults aged 20 years and older who underwent health examinations at one general hospital in Korea, from 2011 to 2018. We developed a lung cancer risk prediction model using a multivariate Cox proportional hazards regression analysis for lung cancer risk factors and estimated the hazard ratios and coefficients. The model evaluation included discrimination and calibration assessments. Results: Among the 308,804 adults in the study cohort, there were 338 (0.11%) patients lung cancer, with 215 males (0.07% of 169,420 males) and 123 females (0.04% of 139,384 females). The prevalence of lung cancer was higher in males and females aged over 60 years. Age, sex, body mass index, and smoking behavior were identified as risk factors for lung cancer prevalence in this model through multivariate Cox proportional hazards analysis. The C-statistic of the development cohort was 0.785 (0.749, 0.821) and that of the validation cohort was 0.823 (0.769, 0.878). Conclusion Our lung cancer risk prediction model showed statistical significance, similar to previous prediction models, among variables that included young age, female sex, and body mass index. Future improvements should focus on population-wide applicability and associated health examination policies.

Background: Lung cancer has a high incidence and mortality worldwide, and smoking, age, sex, and body mass index are known risk factors. Using a health examination cohort, we constructed a comprehensive lung cancer risk-prediction model. Methods: This study comprised 308,804 adults aged 20 years and older who underwent health examinations at one general hospital in Korea, from 2011 to 2018. We developed a lung cancer risk prediction model using a multivariate Cox proportional hazards regression analysis for lung cancer risk factors and estimated the hazard ratios and coefficients. The model evaluation included discrimination and calibration assessments. Results: Among the 308,804 adults in the study cohort, there were 338 (0.11%) patients lung cancer, with 215 males (0.07% of 169,420 males) and 123 females (0.04% of 139,384 females). The prevalence of lung cancer was higher in males and females aged over 60 years. Age, sex, body mass index, and smoking behavior were identified as risk factors for lung cancer prevalence in this model through multivariate Cox proportional hazards analysis. The C-statistic of the development cohort was 0.785 (0.749, 0.821) and that of the validation cohort was 0.823 (0.769, 0.878). Conclusion Our lung cancer risk prediction model showed statistical significance, similar to previous prediction models, among variables that included young age, female sex, and body mass index. Future improvements should focus on population-wide applicability and associated health examination policies.