背景：目前决定接受辅助筛查乳腺磁共振成像（MRI）的方法是传统风险模型，这些模型的预测准确性有限。目的：比较基于乳腺X线摄影的深度学习（DL）风险评估模型与传统乳腺癌风险模型在接受辅助筛查MRI的患者中的诊断准确性。材料与方法：本回顾性研究纳入了在四个机构在2017年9月至2020年9月期间进行乳腺癌筛查MRI的连续患者。风险评估依据是Tyrer-Cuzick（TC）和美国国家癌症研究所乳腺癌风险评估工具（BCRAT）的5年和终身模型，以及一个DL 5年模型，该模型基于最近的筛查乳腺X线摄影生成一个风险评分。传统5年模型风险评分为1.67%或更高定义为增加风险，传统终身模型风险评分为20%或更高定义为高风险，DL模型绝对评分为2.3或更高和6.6或更高分别定义为增加和高风险。使用逻辑回归模型比较模型准确性指标，包括癌症检出率（CDR）和阳性预测值（PPV）（筛查异常结果的PPV1，推荐活检的PPV2，执行活检的PPV3）。结果：本研究纳入了2168名妇女，进行了4247次高风险筛查MRI检查（中位年龄54岁，四分位数48-60岁）。根据DL模型，高风险患者的CDR（每1000次检查）更高（20.6 [95% CI: 11.8, 35.6]）比根据TC模型（6.0 [95% CI: 2.9, 12.3]，P < .01）和BCRAT模型（6.8 [95% CI: 2.9, 15.8]，P = .04）的终身模型。DL模型鉴定为高风险的患者的PPV1、PPV2和PPV3（PPV1为14.6%，PPV2为32.4%，PPV3为36.4%）比TC模型（PPV1为5.0%，PPV2为12.7%，PPV3为13.5%，P值范围为.02-.03）和BCRAT模型（PPV1为5.5%，PPV2为11.1%，PPV3为12.5%，P值范围为.02-.05）的终身模型更高。结论：基于乳腺X线摄影的DL风险评估模型鉴定为高风险的患者在乳腺筛查MRI中的CDR比传统风险模型鉴定为高风险的患者更高。© RSNA，2023附加材料可用于本文。另请参见该期刊中Bae的社论。

Background Access to supplemental screening breast MRI is determined using traditional risk models, which are limited by modest predictive accuracy. Purpose To compare the diagnostic accuracy of a mammogram-based deep learning (DL) risk assessment model to that of traditional breast cancer risk models in patients who underwent supplemental screening with MRI. Materials and Methods This retrospective study included consecutive patients undergoing breast cancer screening MRI from September 2017 to September 2020 at four facilities. Risk was assessed using the Tyrer-Cuzick (TC) and National Cancer Institute Breast Cancer Risk Assessment Tool (BCRAT) 5-year and lifetime models as well as a DL 5-year model that generated a risk score based on the most recent screening mammogram. A risk score of 1.67% or higher defined increased risk for traditional 5-year models, a risk score of 20% or higher defined high risk for traditional lifetime models, and absolute scores of 2.3 or higher and 6.6 or higher defined increased and high risk, respectively, for the DL model. Model accuracy metrics including cancer detection rate (CDR) and positive predictive values (PPVs) (PPV of abnormal findings at screening [PPV1], PPV of biopsies recommended [PPV2], and PPV of biopsies performed [PPV3]) were compared using logistic regression models. Results This study included 2168 women who underwent 4247 high-risk screening MRI examinations (median age, 54 years [IQR, 48-60 years]). CDR (per 1000 examinations) was higher in patients at high risk according to the DL model (20.6 [95% CI: 11.8, 35.6]) than according to the TC (6.0 [95% CI: 2.9, 12.3]; P < .01) and BCRAT (6.8 [95% CI: 2.9, 15.8]; P = .04) lifetime models. PPV1, PPV2, and PPV3 were higher in patients identified as high risk by the DL model (PPV1, 14.6%; PPV2, 32.4%; PPV3, 36.4%) than those identified as high risk with the TC (PPV1, 5.0%; PPV2, 12.7%; PPV3, 13.5%; P value range, .02-.03) and BCRAT (PPV1, 5.5%; PPV2, 11.1%; PPV3, 12.5%; P value range, .02-.05) lifetime models. Conclusion Patients identified as high risk by a mammogram-based DL risk assessment model showed higher CDR at breast screening MRI than patients identified as high risk with traditional risk models. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.