Eriksson Mikael, Destounis Stamatia, Czene Kamila, Zeiberg Andrew, Day Robert, Conant Emily F, Schilling Kathy, Hall Per
Department of Medical Epidemiology and Biostatistics, Karolinska institutet, SE-171 77 Stockholm, Sweden.
Elizabeth Wende Breast Care, Rochester, NY 14620, USA.
Sci Transl Med. 2022 May 11;14(644):eabn3971. doi: 10.1126/scitranslmed.abn3971.
Screening with digital breast tomosynthesis (DBT) improves breast cancer detection and reduces false positives. However, currently, no breast cancer risk model takes advantage of the additional information generated by DBT imaging for breast cancer risk prediction. We developed and internally validated a DBT-based short-term risk model for predicting future late-stage and interval breast cancers after negative screening exams. We included the available 805 incident breast cancers and a random sample of 5173 healthy women matched on year of study entry in a nested case-control study from 154,200 multiethnic women, aged 35 to 74, attending DBT screening in the United States between 2014 and 2019. A relative risk model was trained using elastic net logistic regression and nested cross-validation to estimate risks for using imaging features and age. An absolute risk model was developed using derived risks and U.S. incidence and competing mortality rates. Absolute risks, discrimination performance, and risk stratification were estimated in the left-out validation set. The discrimination performance of 1-year risk was 0.82 (95% CI, 0.79 to 0.85) with good calibration ( = 0.7). Using the U.S. Preventive Service Task Force guidelines, 14% of the women were at high risk, 19.6 times higher compared to general risk. In this high-risk group, 76% of stage II and III cancers and 59% of stage 0 cancers were observed ( < 0.01). Using mammographic features generated from DBT screens, our image-based risk prediction model could guide radiologists in selecting women for clinical care, potentially leading to earlier detection and improved prognoses.
数字乳腺断层合成（DBT）筛查可提高乳腺癌检测率并减少假阳性。然而，目前尚无乳腺癌风险模型利用DBT成像产生的额外信息进行乳腺癌风险预测。我们开发并在内部验证了一种基于DBT的短期风险模型，用于预测筛查结果为阴性后的未来晚期和间期乳腺癌。在一项巢式病例对照研究中，我们纳入了来自154,200名年龄在35至74岁之间、2014年至2019年在美国接受DBT筛查的多民族女性中的805例新发乳腺癌病例以及在研究入组年份匹配的5173名健康女性的随机样本。使用弹性网逻辑回归和嵌套交叉验证训练相对风险模型，以估计利用影像特征和年龄的风险。使用推导风险以及美国发病率和竞争死亡率建立绝对风险模型。在留出的验证集中估计绝对风险、鉴别性能和风险分层。1年风险的鉴别性能为0.82（95%CI，0.79至0.85），校准良好（ = 0.7）。根据美国预防服务工作组指南，14%的女性处于高风险状态，是一般风险女性的19.6倍。在这个高风险组中，观察到76%的II期和III期癌症以及59%的0期癌症（ < 0.01）。利用DBT筛查产生的乳腺X线特征，我们基于图像的风险预测模型可以指导放射科医生选择需要临床护理的女性患者，有可能实现更早的检测并改善预后。
