Abstract：
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.
摘要：
背景人工智能（AI）和乳房US对于乳房致密的女性进行乳房X线筛查的比较性能尚不清楚。目的比较单纯乳腺X线照相术的表现,人工智能乳房X线照相术，乳房X线照相术加上补充的US来筛查乳房致密的女性，并调查检测到的癌症的特征。材料和方法回顾性数据库搜索确定了2017年1月至2018年12月在初级卫生保健中心接受乳房X线照相术加补充全乳房手持US的连续无症状女性（≥40岁）。由五名乳腺放射科医生进行了单独的乳房X线照相术和借助AI系统的乳房X线照相术的顺序读取，他们的召回决定被记录下来。从数据库中收集了乳房X线照相术和US检查的结果。专门的乳腺放射科医生单独或与AI一起检查了乳房X线照相术的标记，以确认病变的识别。参考标准是组织学检查和1年随访数据。每1000次筛查检查的癌症检出率(CDR)，灵敏度,特异性，以及仅乳房X线照相术的异常解释率（AIR），人工智能乳房X线照相术，和乳房X线照相术加US进行了比较。结果5707名无症状妇女(平均年龄，52.4年±7.9[SD])，33（0.6%）患有癌症（中位病变大小，0.7厘米）。AI的乳房X线检查具有更高的特异性（95.3％[95％CI：94.7，95.8]，P=.003)和较低的空气(5.0%[95%CI：4.5，5.6]，P=.004）比单独的乳房X光检查（94.3％[95％CI：93.6，94.8]和6.0％[95％CI：5.4，6.7]，分别)。乳房X线照相术加上美国的CDR较高（每1000次检查5.6vs3.5，P=.002）和灵敏度（97.0%vs60.6%，P=.002），但特异性较低（77.6%vs95.3%，P<.001）和更高的空气（22.9%对5.0%，P<.001）。仅补充美国就帮助检测了12种癌症，主要是阶段0和I(92%，11of12）。结论虽然AI提高了乳腺X线摄影解释的特异性,乳房X线照相术加补充US有助于检测更多使用乳房X线照相术和AI未检测到的淋巴结阴性早期乳腺癌。©RSNA,2024补充材料可用于本文。另请参阅本期惠特曼和Destounis的社论。
