跨国放射学验证使用乳房影像预测阴暗入侵管道癌的情况
Rui Hou1, Lars J Grimm2, Jeffrey R Marks3
1School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
Radiology
|November 18, 2025
概括
使用乳房扫描化的放射性模型显示,在不同国家的管道癌在位 (DCIS) 患者中预测隐性侵袭性乳腺癌的潜力. 这些发现可能有助于为患有早期乳腺癌的女性做出治疗决定.
科学领域:
- 放射学 放射学是一门学科.
- 在瘤学瘤学.
- 机器学习在医学中的应用
背景情况:
- 在位管道癌 (DCIS) 诊断可能会掩盖潜在的侵入性乳腺癌.
- 乳房摄影化的放射性特征可以预测隐性侵入性疾病.
- 预测模型的通用性需要在不同人群中进行验证.
研究的目的:
- 评估放射性模型的通用性,以预测隐藏的入侵性癌症.
- 模型是基于诊断为DCIS的女性的乳房镜特征.
- 在三个国家数据集中进行的评估.
主要方法:
- 来自美国,英国和荷兰的数字乳房造影的回顾性跨国研究.
- 包括无症状的女性,有化,但没有质量或扭曲.
- 使用交叉验证后勤回归和循环测试开发的放射性模型.
主要成果:
- 其中包括1498名女性;升级率在14.1%至16.7%之间.
- 内部交叉验证的AUC范围在0.603到0.701.
- 跨国验证的AUC证明了模型在数据集中的适用性.
结论:
- 放射性机器学习模型在预测DCIS患者的隐性侵入性癌症方面表现有前途.
- 模型在各种国家设置中显示了可通用性.
- 这些发现支持治疗建议中的潜在临床实用性.
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