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使用人工智能来选择乳腺癌中级风险的女性进行MRI乳腺查

Suzanne L van Winkel¹, Riccardo Samperna¹, Elizabeth A Loehrer¹

¹From the Department of Medical Imaging, Radboud University Medical Center, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, the Netherlands (S.L.v.W., R.S., E.A.L., R.M.M.); Department of Ethics, Law and Humanities, Amsterdam University Medical Centers, Amsterdam, the Netherlands (S.L.v.W.); Department of Clinical Genetics, Erasmus University Medical Center, Rotterdam, the Netherlands (E.A.L.); ScreenPoint Medical, Nijmegen, the Netherlands (J.K., A.R.R.); and Department of Radiology, Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands (R.M.M.).

|February 4, 2025

概括

应用于乳房造影的人工智能 (AI) 可以有效地识别具有中级乳腺癌风险的妇女,她们可以从补充MRI查中获益. 这种人工智能驱动的方法有助于优化资源配置,并保持高癌症检测率.

科学领域:

放射学和医学成像学医学成像学
医疗保健中的人工智能
在瘤学瘤学.

背景情况:

结合性乳房影像和MRI查受限于MRI资源的可用性,对于中等风险的女性.
使用人工智能 (AI) 进行乳房影像评估可能使查选择更有效.
有限的MRI资源需要对中等风险人群进行优化查策略.

研究的目的:

评估使用商业人工智能系统与乳房镜相结合的可行性,以对中等风险女性进行分层,以补充MRI.
为了确定AI是否可以有效地识别那些可以从额外的MRI查中受益的女性.
评估AI在优化乳腺癌查资源配置方面的潜力.

主要方法:

从760名中等风险妇女 (2003年1月至2020年1月) 的乳房影像和MRI查数据的回顾性分析.
使用商业人工智能系统对乳房影像进行独立评估,以生成恶性瘤概率得分 (1-10).
测试各种AI值,以平衡癌症检测与需要MRI的女性数量,使用因子关联的单变量分析.

主要成果:

人工智能系统在接受器操作特征曲线下的面积为0.72对于中等风险人群和0.81对于以前患有乳腺癌的人群.
值分数为5检测到84%的乳腺癌 (包括68%的乳腺癌),同时需要MRI检查50%的检查.
没有发现乳腺密度或年龄与虚假阴性AI结果之间有显著的关联.

结论:

应用于乳房造影的AI可以有效地识别中等风险人群中的高风险女性,以补充MRI.
人工智能选择有可能降低查负担和成本,同时保持高癌症检测率.
这种人工智能驱动的方法为资源有效的乳腺癌查提供了一个有希望的策略.

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