乳腺癌是导致女性死亡的重要因素，给全球公共卫生带来负担，尤其是在中国，对乳腺癌具有良好区分准确性的风险预测模型仍然缺乏。作为癌症筛查的一部分，开展了一项多中心筛查队列研究中国城市计划。 2014 年至 2019 年间招募了 40-74 岁的居民，并前瞻性随访至 2021 年 6 月 30 日。整个数据集按入学年份划分，以开发预测模型并在内部进行验证。多元 Cox 回归用于确定预测因子并开发风险预测模型。使用曲线下面积、列线图和校准曲线评估模型在 1 年、3 年和 5 年的性能，并随后进行内部验证。预测模型结合了选定的因素，这些因素被分配了适当的权重，以建立风险评分算法。根据风险评分的指导，参与者被分为乳腺癌低、中、高风险组。使用 X-平铺图选择截止值。通过将乳腺癌风险分为低风险组和高风险组来进行敏感性分析。使用决策曲线分析来评估该模型的临床实用性。在 70,520 名入组女性中，447 名被诊断患有乳腺癌（中位随访时间，6.43 [四分位距，3.99-7.12] 年）。最终的预测模型包括年龄和教育水平（高，风险比 [HR]，2.01 [95% CI，1.31-3.09]）、绝经年龄（≥50 岁，1.34 [1.03-1.75]）、既往良性乳腺疾病（ 1.42 [1.09-1.83]）和生殖手术（1.28 [0.97-1.69]）。开发集中的 1 年曲线下面积为 0.607，验证集中的 1 年曲线下面积为 0.643。验证集观察到适度的预测辨别力和令人满意的校准。风险预测显示低、中和高风险组之间存在统计显着差异 (p < .001)。与低风险组相比，高风险组和中风险组女性患乳腺癌的风险分别增加2.17倍和1.62倍。敏感性分析中也得到了类似的结果。开发了一个基于网络的计算器来估计女性的风险分层。本研究通过纳入易于访问的变量和女性因素，开发并内部验证了一个适应风险且用户友好的风险预测模型。个性化模型表现出可靠的校准和中等的判别能力。风险分层筛查策略有助于准确地区分高危人群和无症状人群，并优先进行乳腺癌筛查。乳腺癌在中国仍然是一个负担。为了加强乳腺癌筛查，我们需要在筛查中纳入人群分层。中国仍然缺乏准确的乳腺癌风险预测模型。我们通过结合常规可用变量和女性因素，建立并验证了一个适应风险且用户友好的风险预测模型。使用这种风险分层模型有助于准确识别高风险个体，这在考虑将个体风险评估纳入乳腺癌大规模筛查计划时非常重要。目前临床乳腺癌筛查缺乏建设性的临床路径和指导建议。我们的研究结果可以更好地指导临床医生和医疗保健提供者。© 2023 作者。 《癌症》由 Wiley periodicals LLC 代表美国癌症协会出版。

Breast 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.A 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.Of 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.This 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.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.© 2023 The Authors. Cancer published by Wiley Periodicals LLC on behalf of American Cancer Society.