Jiang Shu, Bennett Debbie L, Colditz Graham A
Division of Public Health Sciences, Department of Surgery, Washington University School of Medicine in St Louis, St Louis, Missouri.
Department of Radiology, Washington University School of Medicine in St Louis, St Louis, Missouri.
JAMA Netw Open. 2025 Jun 2;8(6):e2512681. doi: 10.1001/jamanetworkopen.2025.12681.
For breast cancer risk prediction to be clinically useful, it must be accurate and applicable to diverse groups of women across multiple settings.
To examine whether a dynamic risk prediction model incorporating prior mammograms, previously validated in Black and White women, could predict future risk of breast cancer across a racially and ethnically diverse population in a population-based screening program.
DESIGN, SETTING, AND PARTICIPANTS: This prognostic study included women aged 40 to 74 years with 1 or more screening mammograms drawn from the British Columbia Breast Screening Program from January 1, 2013, to December 31, 2019, with follow-up via linkage to the British Columbia Cancer Registry through June 2023. This provincial, organized screening program offers screening mammography with full field digital mammography (FFDM) every 2 years. Data were analyzed from May to August 2024.
FFDM-based, artificial intelligence-generated mammogram risk score (MRS), including up to 4 years of prior mammograms.
The primary outcomes were 5-year risk of breast cancer (measured with the area under the receiver operating characteristic curve [AUROC]) and absolute risk of breast cancer calibrated to the US Surveillance, Epidemiology, and End Results incidence rates.
Among 206 929 women (mean [SD] age, 56.1 [9.7] years; of 118 093 with data on race, there were 34 266 East Asian; 1946 Indigenous; 6116 South Asian; and 66 742 White women), there were 4168 pathology-confirmed incident breast cancers diagnosed through June 2023. Mean (SD) follow-up time was 5.3 (3.0) years. Using up to 4 years of prior mammogram images in addition to the most current mammogram, a 5-year AUROC of 0.78 (95% CI, 0.77-0.80) was obtained based on analysis of images alone. Performance was consistent across subgroups defined by race and ethnicity in East Asian (AUROC, 0.77; 95% CI, 0.75-0.79), Indigenous (AUROC, 0.77; 95% CI 0.71-0.83), and South Asian (AUROC, 0.75; 95% CI 0.71-0.79) women. Stratification by age gave a 5-year AUROC of 0.76 (95% CI, 0.74-0.78) for women aged 50 years or younger and 0.80 (95% CI, 0.78-0.82) for women older than 50 years. There were 18 839 participants (9.0%) with a 5-year risk greater than 3%, and the positive predictive value was 4.9% with an incidence of 11.8 per 1000 person-years.
A dynamic MRS generated from both current and prior mammograms showed robust performance across diverse racial and ethnic populations in a province-wide screening program starting from age 40 years, reflecting improved accuracy for racially and ethnically diverse populations.
为使乳腺癌风险预测在临床上有用，它必须准确且适用于多种环境中的不同女性群体。
检验一种结合既往乳房X光检查结果的动态风险预测模型，该模型先前已在黑人和白人女性中得到验证，能否在一项基于人群的筛查项目中，预测不同种族和族裔人群未来患乳腺癌的风险。
设计、设置和参与者：这项预后研究纳入了年龄在40至74岁之间、有1次或更多次筛查乳房X光检查的女性，这些检查数据来自2013年1月1日至2019年12月31日的不列颠哥伦比亚省乳房筛查项目，并通过与不列颠哥伦比亚癌症登记处的关联进行随访，直至2023年6月。这个省级有组织的筛查项目每2年提供一次采用全场数字化乳房X光摄影（FFDM）的筛查。数据于2024年5月至8月进行分析。
基于FFDM的、人工智能生成的乳房X光检查风险评分（MRS），包括长达4年的既往乳房X光检查结果。
主要结局是乳腺癌的5年风险（用受试者工作特征曲线下面积[AUROC]衡量）以及根据美国监测、流行病学和最终结果发病率校准的乳腺癌绝对风险。
在206929名女性中（平均[标准差]年龄为56.1[9.7]岁；在118093名有种族数据的女性中，有34266名东亚女性、1946名原住民、6116名南亚女性和66742名白人女性），截至2023年6月，有4168例经病理确诊的新发乳腺癌。平均（标准差）随访时间为5.3（3.0）年。除了最新的乳房X光检查图像外，使用长达4年的既往乳房X光检查图像，仅基于图像分析得出的5年AUROC为0.78（95%置信区间，0.77 - 0.80）。在按种族和族裔定义的亚组中，东亚女性（AUROC为0.77；95%置信区间，0.75 - 0.79）、原住民女性（AUROC为0.77；95%置信区间0.71 - 0.83）和南亚女性（AUROC为0.75；95%置信区间0.71 - 0.79）的表现一致。按年龄分层，50岁及以下女性的5年AUROC为0.76（95%置信区间，0.74 - 0.78），50岁以上女性为0.80（95%置信区间，0.78 - 0.82）。有18839名参与者（9.0%）的5年风险大于3%，阳性预测值为4.9%，发病率为每1000人年11.8例。
由当前和既往乳房X光检查生成的动态MRS在一个全省范围的筛查项目中，从40岁开始，在不同种族和族裔人群中表现出强大的性能，这反映出对不同种族和族裔人群的准确性有所提高。
