Lan-Anh Dang, Emmanuel Chazard, Edouard Poncelet, Teodora Serb, Aniela Rusu, Xavier Pauwels, Clémence Parsy, Thibault Poclet, Hugo Cauliez, Constance Engelaere, Guillaume Ramette, Charlotte Brienne, Sofiane Dujardin, Nicolas Laurent
{"title":"人工智能对乳腺x光筛查的影响。","authors":"Lan-Anh Dang, Emmanuel Chazard, Edouard Poncelet, Teodora Serb, Aniela Rusu, Xavier Pauwels, Clémence Parsy, Thibault Poclet, Hugo Cauliez, Constance Engelaere, Guillaume Ramette, Charlotte Brienne, Sofiane Dujardin, Nicolas Laurent","doi":"10.1007/s12282-022-01375-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.</p><p><strong>Methods: </strong>A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or \"continuous BI-RADS 100\". Cohen's kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.</p><p><strong>Results: </strong>On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528-0.571) without AI and κ = 0.626, 95% CI (0.607-0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).</p><p><strong>Conclusions: </strong>When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.</p>","PeriodicalId":520574,"journal":{"name":"Breast cancer (Tokyo, Japan)","volume":" ","pages":"967-977"},"PeriodicalIF":2.9000,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587927/pdf/","citationCount":"9","resultStr":"{\"title\":\"Impact of artificial intelligence in breast cancer screening with mammography.\",\"authors\":\"Lan-Anh Dang, Emmanuel Chazard, Edouard Poncelet, Teodora Serb, Aniela Rusu, Xavier Pauwels, Clémence Parsy, Thibault Poclet, Hugo Cauliez, Constance Engelaere, Guillaume Ramette, Charlotte Brienne, Sofiane Dujardin, Nicolas Laurent\",\"doi\":\"10.1007/s12282-022-01375-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.</p><p><strong>Methods: </strong>A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or \\\"continuous BI-RADS 100\\\". Cohen's kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.</p><p><strong>Results: </strong>On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528-0.571) without AI and κ = 0.626, 95% CI (0.607-0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).</p><p><strong>Conclusions: </strong>When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.</p>\",\"PeriodicalId\":520574,\"journal\":{\"name\":\"Breast cancer (Tokyo, Japan)\",\"volume\":\" \",\"pages\":\"967-977\"},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2022-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9587927/pdf/\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Breast cancer (Tokyo, Japan)\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s12282-022-01375-9\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2022/6/28 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breast cancer (Tokyo, Japan)","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12282-022-01375-9","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2022/6/28 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9
摘要
目的:证明放射科医生在人工智能(AI)的帮助下,能够更好地将筛查乳房x光片分类到正确的乳房成像报告和数据系统(BI-RADS)类别中,并作为次要目标,探讨AI对癌症检测和乳房x光片解释时间的影响。方法:采用交叉设计的多读者、多病例研究,包括314张乳房x线照片。在有和没有人工智能支持的情况下,12名放射科医生在两次会议中解释了4周的洗脱期。对于每次乳房x光检查的每个乳房,他们必须标记最可疑的病变(如果有的话),并将其分配给强制BI-RADS类别和可疑水平或“连续BI-RADS 100”。以评估每个乳房BI-RADS类别的观察者间一致性的Cohen's kappa相关系数和受试者工作特征曲线下的面积(AUC)作为指标并进行分析。结果:平均而言,使用人工智能时,所有读者的二次卡帕系数显著增加[未使用人工智能时κ = 0.549, 95% CI(0.528-0.571),使用人工智能时κ = 0.626, 95% CI(0.607-0.6455)]。人工智能显著改善了AUC (0.74 vs 0.77, p = 0.004)。所有读者的阅读时间都没有明显的影响(没有人工智能的106秒和有人工智能的102秒;p = 0.754)。结论:当使用人工智能时,放射科医生能够更好地分配具有正确BI-RADS类别的乳房x线照片,而不会减慢解释时间。
Impact of artificial intelligence in breast cancer screening with mammography.
Objectives: To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.
Methods: A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or "continuous BI-RADS 100". Cohen's kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.
Results: On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528-0.571) without AI and κ = 0.626, 95% CI (0.607-0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).
Conclusions: When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.