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We aimed to determine participation in low‐dose computed tomography (LDCT) of individuals with a family history of common cancers in a population‐based screening program to provide timely evidence in high‐risk populations in China. The analysis was conducted using data from the Cancer Screening Program in Urban China (CanSPUC), which recruited 282 377 participants aged 40 to 74 years from eight cities in the Henan province. Using the CanSPUC risk score system, 55 428 participants were evaluated to have high risk for lung cancer and were recommended for LDCT. We calculated the overall and group‐specific participation rates using family history of common cancers and compared differences in participation rates between different groups. Odds ratios (ORs) and 95% confidence intervals were derived by multivariable logistic regression. Of the 55 428 participants, 22 260 underwent LDCT (participation rate, 40.16%). Family history of lung, esophageal, stomach, liver and colorectal cancer was associated with increased participation in LDCT screening. The odds of participants with a family history of one, two, three and four or more cancer cases undergoing LDCT screening were 1.9, 2.7, 2.8 and 3.5 times, respectively, than those without a family history of cancer. Compared to those without a history of cancer, participation in LDCT gradually increased as the number of cancer cases in the family increased (P <.001). Our findings suggest that there is room for improvement in lung cancer screening given the relatively low participation rate. Lung cancer screening in populations with a family history of cancer may improve efficiency and cost‐effectiveness; however, this requires further verification. [ABSTRACT FROM AUTHOR]

Background: About 15% of lung cancers in men and 53% in women are not attributable to smoking worldwide. The aim was to develop and validate a simple and non-invasive model which could assess and stratify lung cancer risk in non-smokers in China. Methods: A large-sample size, population-based study was conducted under the framework of the Cancer Screening Program in Urban China (CanSPUC). Data on the lung cancer screening in Henan province, China, from October 2013 to October 2019 were used and randomly divided into the training and validation sets. Related risk factors were identified through multivariable Cox regression analysis, followed by establishment of risk prediction nomogram. Discrimination [area under the curve (AUC)] and calibration were further performed to assess the validation of risk prediction nomogram in the training set, and then validated by the validation set. Results: A total of 214,764 eligible subjects were included, with a mean age of 55.19 years. Subjects were randomly divided into the training (107,382) and validation (107,382) sets. Elder age, being male, a low education level, family history of lung cancer, history of tuberculosis, and without a history of hyperlipidemia were the independent risk factors for lung cancer. Using these six variables, we plotted 1-year, 3-year, and 5-year lung cancer risk prediction nomogram. The AUC was 0.753, 0.752, and 0.755 for the 1-, 3- and 5-year lung cancer risk in the training set, respectively. In the validation set, the model showed a moderate predictive discrimination, with the AUC was 0.668, 0.678, and 0.685 for the 1-, 3- and 5-year lung cancer risk. Conclusions: We developed and validated a simple and non-invasive lung cancer risk model in non-smokers. This model can be applied to identify and triage patients at high risk for developing lung cancers in non-smokers. [ABSTRACT FROM AUTHOR]

• In a large prospective lung cancer screening cohort study, we developed and internally validated a simple risk prediction model for lung cancer. • Our results showed that the model has moderate discriminatory accuracy and goodness-of-fit for both men and women, smokers and never-smokers. • The model has potential utility for shared decision-making and individualized risk assessment for tailored lung cancer screening. Two large randomized controlled trials (RCTs) have demonstrated that low dose computed tomography (LDCT) screening reduces lung cancer mortality. Risk-prediction models have been proved to select individuals for lung cancer screening effectively. With the focus on established risk factors for lung cancer routinely available in general cancer screening settings, we aimed to develop and internally validated a risk prediction model for lung cancer. Using data from the Cancer Screening Program in Urban China (CanSPUC) in Henan province, China between 2013 and 2019, we conducted a prospective cohort study consisting of 282,254 participants including 126,445 males and 155,809 females. Detailed questionnaire, physical assessment and follow-up were completed for all participants. Using Cox proportional risk regression analysis, we developed the Henan Lung Cancer Risk Models based on simplified questionnaire. Model discrimination was evaluated by concordance statistics (C-statistics), and model calibration was evaluated by the bootstrap sampling, respectively. By 2020, a total of 589 lung cancer cases occurred in the follow-up yielding an incident density of 64.91/100,000 person-years (pyrs). Age, gender, smoking, history of tuberculosis and history of emphysema were included into the model. The C-index of the model for 1-year lung cancer risk was 0.766 and 0.741 in the training set and validation set, respectively. In stratified analysis, the model showed better predictive power in males, younger participants, and former or current smoking participants. The model calibrated well across the deciles of predicted risk in both the overall population and all subgroups. We developed and internally validated a simple risk prediction model for lung cancer, which may be useful to identify high-risk individuals for more intensive screening for cancer prevention. [ABSTRACT FROM AUTHOR]