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基于乳腺造影的AI风险评估,对乳房密度较高的患者进行补充分子乳房成像
Samuel B Ogunlade1, Lian Wang2, Santo Maimone2
1Division of Interventional Radiology, Department of Radiology, Mayo Clinic, Jacksonville, FL, USA.
Quantitative imaging in medicine and surgery
|February 11, 2026
概括
一个人工智能风险模型显示,它有望在乳腺密度较高的女性中识别乳腺癌,在特定子组中表现优于传统方法. 这项技术可以完善风险评估,并指导诸如分子乳腺成像 (MBI) 等补充查.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 基于图像的AI风险模型为估计短期乳腺癌风险的传统工具提供了替代方案.
- 风险分层在没有符合高风险标准的女性胸部密度较高的女性中具有挑战性.
- 分子乳房成像 (MBI) 被用作女性密集乳房的补充查.
研究的目的:
- 评估基于乳房镜的人工智能风险模型 (iCAD ProFound AI® Risk) 的性能和临床实用性.
- 评估人工智能模型在经历MBI的女性中预测短期乳腺癌风险的能力.
主要方法:
- 从2018-2023年对416个查数字乳腺图解合成乳房造影 (BI-RADS C-D密度) 的回顾性研究,所有这些都在一年内被MBI跟踪.
- 使用ProFound AI®风险模型处理的乳房影像;Tyrer-Cuzick和Gail模型计算用于比较.
- 使用ROC分析 (AUC,灵敏度,特异性) 评估的模型歧视.
主要成果:
- 人工智能模型在乳房密度增加时显示出更强的歧视性表现,在极密的乳房 (D类) 中达到0.75的AUC.
- 在所有密度类别中,人工智能模型的表现优于传统的泰勒-库兹克和盖尔模型.
- 人工智能模型显示,侵袭性叶状癌 (ILC) 和更高度瘤的风险得分更高.
结论:
- 基于乳房扫描的人工智能风险模型显示,乳房极密的女性存在明显的歧视,其表现优于传统模型.
- 人工智能工具可以完善乳房密度较高的女性的风险评估,指导针对性补充MBI查.
- 人工智能显示出改善乳腺癌风险分层对具有挑战性的群体的潜力.
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