Stand-Alone Use of Artificial Intelligence for Digit Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation
Stand-Alone Use of Artificial Intelligence for Digit Mammography and Digital Breast Tomosynthesis Screening: A Retrospective Evaluation
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DOI：
10.1148/radiol.211590
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发表时间：
2022-03-01
期刊：
影响因子：
19.7
通讯作者：
Alvarez-Benito, Marina
中科院分区：
文献类型：
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作者：
Romero-Martin, Sara;Elias-Cabot, Esperanza;Alvarez-Benito, Marina
背景资料：使用人工智能（AI）作为数字乳腺X射线摄影（DM）或数字乳腺断层合成摄影（DBT）乳腺筛查的独立阅片器可以减轻放射科医生的工作量，同时保持质量。目的：回顾性评估AI系统作为DM和DBT筛查检查的独立阅片器的独立性能。材料和方法：从断层合成科尔多瓦筛选试验中回顾性收集了2015年1月至2016年12月期间采集的连续筛选配对和独立读取的DM和DBT图像。AI系统独立计算DM和DBT检查的癌症风险评分（范围，1-100）。使用受试者工作特征曲线（AUC）下的面积以及选择与人类读数相比具有非劣效性灵敏度（非劣效性界值，5%）的不同操作点的灵敏度和召回率来测量AI独立性能。AI和人类读数的召回率进行了比较，使用McNemar test.Results：共15 999 DM和DBT检查（113乳腺癌，包括98个屏幕检测和15个间隔癌症）从15 998名妇女（平均年龄，58岁6 6 [标准差]）进行了评价。DM和DBT的AI AUC分别为0.93（95% CI：0.89，0.96）和0.94（95% CI：0.91，0.97）。对于DM，AI作为单一（58.4%; 66/113; 95%CI：49.2，67.1）或双重（67.3%; 76/113; 95%CI：58.2，75.2）阅读者的敏感性达到非劣效性，召回率降低（P
Background: Use of artificial intelligence (AI) as a stand-alone reader for digital mammography (DM) or digital breast tomosynthesis (DBT) breast screening could ease radiologists' workload while maintaining quality.Purpose: To retrospectively evaluate the stand-alone performance of an AI system as an independent reader of DM and DBT screening examinations.Materials and Methods: Consecutive screening-paired and independently read DM and DBT images acquired between January 2015 and December 2016 were retrospectively collected from the Tomosynthesis Cordoba Screening Trial. An AI system computed a cancer risk score (range, 1-100) for DM and DBT examinations independently. AI stand-alone performance was measured using the area under the receiver operating characteristic curve (AUC) and sensitivity and recall rate at different operating points selected to have noninferior sensitivity compared with the human readings (noninferiority margin, 5%). The recall rate of AI and the human readings were compared using a McNemar test.Results: A total of 15 999 DM and DBT examinations (113 breast cancers, including 98 screen-detected and 15 interval cancers) from 15 998 women (mean age, 58 years 6 6 [standard deviation]) were evaluated. AI achieved an AUC of 0.93 (95% CI: 0.89, 0.96) for DM and 0.94 (95% CI: 0.91, 0.97) for DBT. For DM, AI achieved noninferior sensitivity as a single (58.4%; 66 of 113; 95% CI: 49.2, 67.1) or double (67.3%; 76 of 113; 95% CI: 58.2, 75.2) reader, with a reduction in recall rate (P