Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
Detection of Breast Cancer with Mammography: Effect of an Artificial Intelligence Support System
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DOI：
10.1148/radiol.2018181371
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发表时间：
2019-02-01
期刊：
影响因子：
19.7
通讯作者：
Mann, Ritse M.
中科院分区：
文献类型：
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作者：
Rodriguez-Ruiz, Alejandro;Krupinski, Elizabeth;Mann, Ritse M.
目的：比较乳腺癌的检测性能的放射科医师阅读乳腺X线检查独立与支持的人工智能（AI）system.Materials和Methods：一个丰富的回顾性，完全交叉，多读者，多病例，符合健康保险责任法案的研究进行。纳入了2013年至2017年期间进行的240名女性（中位年龄为62岁;范围为39-89岁）的数字乳腺X线筛查检查。240次检查（100次显示癌症，40次导致假阳性召回，100次正常）由14名乳腺X线摄影质量标准法案合格的放射科医生进行解释，一次有AI支持，一次没有AI支持。阅片者提供了乳腺成像报告和数据系统评分以及恶性肿瘤的概率。AI支持为放射科医生提供了交互式决策支持（点击乳房区域会产生局部癌症可能性评分），计算机检测异常的传统病变标记，以及基于检查的癌症可能性评分。使用混合模型方差分析和广义线性模型多次重复测量的条件下的受试者工作特征曲线下面积（AUC），特异性和灵敏度，和阅读时间进行了比较。结果：平均而言，AUC与AI支持高于无辅助阅读（0.89与0.87，分别为P = 0.002）。随着AI支持，敏感性增加（86% [86/100] vs 83% [83/100]; P = 0.046），而特异性有改善趋势（79% [111/140] vs 77% [108/140]; P = 0.06）。每个病例的阅读时间相似（无辅助，146秒; AI支持，149秒; P = 0.15）。单独使用AI系统的AUC与放射科医生的平均AUC相似（0.89 vs 0.87）。结论：当使用人工智能系统进行支持时，放射科医生在乳房X光检查时改善了癌症检测，而不需要额外的阅读时间。
Purpose: To compare breast cancer detection performance of radiologists reading mammographic examinations unaided versus supported by an artificial intelligence (AI) system.Materials and Methods: An enriched retrospective, fully crossed, multireader, multicase, HIPAA-compliant study was performed. Screening digital mammographic examinations from 240 women (median age, 62 years; range, 39-89 years) performed between 2013 and 2017 were included. The 240 examinations (100 showing cancers, 40 leading to false-positive recalls, 100 normal) were interpreted by 14 Mammography Quality Standards Act-qualified radiologists, once with and once without AI support. The readers provided a Breast Imaging Reporting and Data System score and probability of malignancy. AI support provided radiologists with interactive decision support (clicking on a breast region yields a local cancer likelihood score), traditional lesion markers for computer-detected abnormalities, and an examination-based cancer likelihood score. The area under the receiver operating characteristic curve (AUC), specificity and sensitivity, and reading time were compared between conditions by using mixed-models analysis dof variance and generalized linear models for multiple repeated measurements.Results: On average, the AUC was higher with AI support than with unaided reading (0.89 vs 0.87, respectively; P =.002). Sensitivity increased with AI support (86% [86 of 100] vs 83% [83 of 100]; P =.046), whereas specificity trended toward improvement (79% [111 of 140]) vs 77% [108 of 140]; P =.06). Reading time per case was similar (unaided, 146 seconds; supported by AI, 149 seconds; P =.15). The AUC with the AI system alone was similar to the average AUC of the radiologists (0.89 vs 0.87).Conclusion: Radiologists improved their cancer detection at mammography when using an artificial intelligence system for support, without requiring additional reading time.