Development and evaluation of a risk assessment tool for the personalized screening of breast cancer in Chinese populations: A prospective cohort study

Abstract

AbstractIntroductionBreast cancer is a significant contributor to female mortality, exerting a public health burden worldwide, especially in China, where risk‐prediction models with good discriminating accuracy for breast cancer are still scarce.MethodsA multicenter screening cohort study was conducted as part of the Cancer Screening Program in Urban China. Dwellers aged 40–74 years were recruited between 2014 and 2019 and prospectively followed up until June 30, 2021. The entire data set was divided by year of enrollment to develop a prediction model and validate it internally. Multivariate Cox regression was used to ascertain predictors and develop a risk‐prediction model. Model performance at 1, 3, and 5 years was evaluated using the area under the curve, nomogram, and calibration curves and subsequently validated internally. The prediction model incorporates selected factors that are assigned appropriate weights to establish a risk‐scoring algorithm. Guided by the risk score, participants were categorized into low‐, medium‐, and high‐risk groups for breast cancer. The cutoff values were chosen using X‐tile plots. Sensitivity analysis was conducted by categorizing breast cancer risk into the low‐ and high‐risk groups. A decision curve analysis was used to assess the clinical utility of the model.ResultsOf the 70,520 women enrolled, 447 were diagnosed with breast cancer (median follow‐up, 6.43 [interquartile range, 3.99–7.12] years). The final prediction model included age and education level (high, hazard ratio [HR], 2.01 [95% CI, 1.31–3.09]), menopausal age (≥50 years, 1.34 [1.03–1.75]), previous benign breast disease (1.42 [1.09–1.83]), and reproductive surgery (1.28 [0.97–1.69]). The 1‐year area under the curve was 0.607 in the development set and 0.643 in the validation set. Moderate predictive discrimination and satisfactory calibration were observed for the validation set. The risk predictions demonstrated statistically significant differences between the low‐, medium‐, and high‐risk groups (p < .001). Compared with the low‐risk group, women in the high‐ and medium‐risk groups posed a 2.17‐fold and 1.62‐fold elevated risk of breast cancer, respectively. Similar results were obtained in the sensitivity analyses. A web‐based calculator was developed to estimate risk stratification for women.ConclusionsThis study developed and internally validated a risk‐adapted and user‐friendly risk‐prediction model by incorporating easily accessible variables and female factors. The personalized model demonstrated reliable calibration and moderate discriminative ability. Risk‐stratified screening strategies contribute to precisely distinguishing high‐risk individuals from asymptomatic individuals and prioritizing breast cancer screening.Plain Language Summary Breast cancer remains a burden in China. To enhance breast cancer screening, we need to incorporate population stratification in screening. Accurate risk‐prediction models for breast cancer remain scarce in China. We established and validated a risk‐adapted and user‐friendly risk‐prediction model by incorporating routinely available variables along with female factors. Using this risk‐stratified model helps accurately identify high‐risk individuals, which is of significant importance when considering integrating individual risk assessments into mass screening programs for breast cancer. Current clinical breast cancer screening lacks a constructive clinical pathway and guiding recommendations. Our findings can better guide clinicians and health care providers.