背景 对于接受乳房 X 光检查的致密乳房女性来说，人工智能 (AI) 和乳房超声的表现比较仍不清楚。目的 比较单独乳房 X 光检查、乳房 X 光检查结合 AI 以及乳房 X 光检查加补充 US 筛查致密乳房女性的性能，并调查检测到的癌症的特征。材料和方法 回顾性数据库检索确定了 2017 年 1 月至 2018 年 12 月在初级卫生保健中心连续接受乳房 X 光检查和补充全乳房手持式超声检查的无症状、致密乳房的女性（≥ 40 岁）。五名乳腺放射科医生对单独的乳房X光检查和借助人工智能系统的乳房X光检查进行了顺序读取，并记录了他们的召回决定。从数据库中收集乳房X线照相术和超声检查相结合的结果。专门的乳腺放射科医生单独或使用人工智能检查乳房 X 光检查标记，以确认病变识别。参考标准是组织学检查和1年随访数据。比较了单独乳房X线摄影、AI乳房X线摄影和乳房X线摄影加US的每1000次筛查检查的癌症检出率（CDR）、敏感性、特异性和异常判读率（AIR）。结果 在 5707 名无症状女性（平均年龄，52.4 岁 ± 7.9 [SD]）中，33 名 (0.6%) 患有癌症（中位病变大小，0.7 厘米）。与单独的乳房 X 光检查相比，采用 AI 的乳房 X 光检查具有更高的特异性 (95.3% [95% CI: 94.7, 95.8], P = .003) 和较低的 AIR (5.0% [95% CI: 4.5, 5.6], P = .004)。分别为 94.3% [95% CI: 93.6, 94.8] 和 6.0% [95% CI: 5.4, 6.7]。乳房X光检查加US具有较高的CDR（每1000次检查5.6 vs 3.5，P = .002）和敏感性（97.0% vs 60.6%，P = .002），但特异性较低（77.6% vs 95.3%，P < .001）和与 AI 乳房 X 线摄影相比，AIR 更高（22.9% vs 5.0%，P < .001）。仅补充超声检查就帮助检测出 12 种癌症，其中大部分为 0 期和 I 期（92%，12 种癌症中的 11 种）。结论 虽然 AI 提高了乳房 X 线摄影解读的特异性，但乳房 X 线摄影加上补充超声有助于检测更多使用 AI 乳房 X 线摄影无法检测到的淋巴结阴性早期乳腺癌。 © RSNA，2024 本文提供补充材料。另请参阅惠特曼和德斯托尼斯在本期的社论。

Background Comparative performance between artificial intelligence (AI) and breast US for women with dense breasts undergoing screening mammography remains unclear. Purpose To compare the performance of mammography alone, mammography with AI, and mammography plus supplemental US for screening women with dense breasts, and to investigate the characteristics of the detected cancers. Materials and Methods A retrospective database search identified consecutive asymptomatic women (≥40 years of age) with dense breasts who underwent mammography plus supplemental whole-breast handheld US from January 2017 to December 2018 at a primary health care center. Sequential reading for mammography alone and mammography with the aid of an AI system was conducted by five breast radiologists, and their recall decisions were recorded. Results of the combined mammography and US examinations were collected from the database. A dedicated breast radiologist reviewed marks for mammography alone or with AI to confirm lesion identification. The reference standard was histologic examination and 1-year follow-up data. The cancer detection rate (CDR) per 1000 screening examinations, sensitivity, specificity, and abnormal interpretation rate (AIR) of mammography alone, mammography with AI, and mammography plus US were compared. Results Among 5707 asymptomatic women (mean age, 52.4 years ± 7.9 [SD]), 33 (0.6%) had cancer (median lesion size, 0.7 cm). Mammography with AI had a higher specificity (95.3% [95% CI: 94.7, 95.8], P = .003) and lower AIR (5.0% [95% CI: 4.5, 5.6], P = .004) than mammography alone (94.3% [95% CI: 93.6, 94.8] and 6.0% [95% CI: 5.4, 6.7], respectively). Mammography plus US had a higher CDR (5.6 vs 3.5 per 1000 examinations, P = .002) and sensitivity (97.0% vs 60.6%, P = .002) but lower specificity (77.6% vs 95.3%, P < .001) and higher AIR (22.9% vs 5.0%, P < .001) than mammography with AI. Supplemental US alone helped detect 12 cancers, mostly stage 0 and I (92%, 11 of 12). Conclusion Although AI improved the specificity of mammography interpretation, mammography plus supplemental US helped detect more node-negative early breast cancers that were undetected using mammography with AI. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Whitman and Destounis in this issue.