背景：目前，基于乳腺摄影术的风险模型可以估计乳腺癌的短期或长期风险，但是否通过合并这些模型来改善风险评估尚未得到评估。目的：本研究旨在确定通过结合用于病变检测的诊断性人工智能（AI）系统和乳腺摄影图像纹理模型是否能够改善乳腺癌风险评估。材料和方法：回顾性研究包括从2012年11月至2015年12月连续接受乳腺癌乳腺摄影检查的丹麦妇女，她们至少有5年的随访数据。使用商业上可用的用于病变检测的诊断性AI系统评估了这些检查的短期风险，该系统生成一个评分以表示癌症的可能性。一种在单独的数据集上训练的乳腺摄影图像纹理模型评估了与长期癌症风险相关的纹理。根据接受乳腺摄影筛查的妇女，包括2年内筛查后诊断出的间隔性癌症和筛查后2年或更长时间内诊断出的长期癌症，使用受试者工作特征曲线下面积（AUC）分析评估了AI模型和纹理模型的独立和组合性能。使用DeLong检验进行AUC比较。结果：丹麦筛查队列包括119,650名妇女（中位年龄，59岁[IQR，53-64岁]），其中320名患有间隔性癌症，1401名患有长期癌症。与诊断性AI（AUC，0.70）或纹理风险（AUC，0.66）模型相比，组合模型在间隔性和长期癌症合并的AUC上表现更佳（AUC，0.73 vs 0.70；P ＜ .001；AUC，0.73 vs 0.66；P ＜ .001）。通过组合模型，最高风险的10％妇女占筛查后间隔性癌症的44.1％（320中的141名）和长期癌症的33.7％（1401中的472名）。结论：合并诊断性AI系统和乳腺摄影图像纹理模型可改善对间隔性癌症和长期癌症的风险评估，并能够识别高风险妇女。

Background Recent mammography-based risk models can estimate short-term or long-term breast cancer risk, but whether risk assessment may improve by combining these models has not been evaluated. Purpose To determine whether breast cancer risk assessment improves when combining a diagnostic artificial intelligence (AI) system for lesion detection and a mammographic texture model. Materials and Methods This retrospective study included Danish women consecutively screened for breast cancer at mammography from November 2012 to December 2015 who had at least 5 years of follow-up data. Examinations were evaluated for short-term risk using a commercially available diagnostic AI system for lesion detection, which produced a score to indicate the probability of cancer. A mammographic texture model, trained on a separate data set, assessed textures associated with long-term cancer risk. Area under the receiver operating characteristic curve (AUC) analysis was used to evaluate both the individual and combined performance of the AI and texture models for the prediction of future cancers in women with a negative screening mammogram, including those with interval cancers diagnosed within 2 years of screening and long-term cancers diagnosed 2 years or more after screening. AUCs were compared using the DeLong test. Results The Danish screening cohort included 119 650 women (median age, 59 years [IQR, 53-64 years]), of whom 320 developed interval cancers and 1401 developed long-term cancers. The combination model achieved a higher AUC for interval and long-term cancers grouped together than either the diagnostic AI (AUC, 0.73 vs 0.70; P < .001) or the texture risk (AUC, 0.73 vs 0.66; P < .001) models. The 10% of women with the highest combined risk identified by the combination model accounted for 44.1% (141 of 320) of interval cancers and 33.7% (472 of 1401) of long-term cancers. Conclusion Combining a diagnostic AI system and mammographic texture model resulted in improved risk assessment for interval cancers and long-term cancers and enabled identification of women at high risk. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Poynton and Slanetz in this issue.