Lung Cancer Risk Prediction Nomogram in Nonsmoking Chinese Women: Retrospective Cross-sectional Cohort Study

Area of Science:

• Oncology
• Epidemiology
• Public Health

Background:

• Lung cancer affects many women globally, with a significant portion not attributed to smoking.
• Identifying risk factors in non-smoking populations is crucial for targeted prevention strategies.

Purpose of the Study:

• To develop and validate a simple, noninvasive model for assessing lung cancer risk in nonsmoking Chinese women.
• To stratify risk levels for early identification and intervention.

Main Methods:

• Retrospective, cross-sectional cohort study using data from the Cancer Screening Program in Urban China.
• Multivariable Cox regression analysis to identify risk factors.
• Development and validation of a predictive nomogram using training and validation sets.

Main Results:

• The study included 151,834 participants, randomly divided into training and validation sets.
• Key predictors identified: age, chronic respiratory disease, family history of lung cancer, menopause, and benign breast disease history.
• The developed nomograms demonstrated moderate predictive discrimination in both sets.

Conclusions:

• A validated, noninvasive lung cancer risk prediction model for nonsmoking women has been created.
• This model can effectively identify and triage high-risk individuals for further management.