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基于人工智能的BRAIx风险评分用于乳腺癌的中期预测:一个人口队列研究

Helen M L Frazer¹, John L Hopper², Tuong L Nguyen²

¹Faculty of Medicine, Dentistry and Health Sciences, University of Melbourne, Melbourne, VIC, Australia; St Vincent's BreastScreen, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia; Department of Radiology, St Vincent's Hospital Melbourne, Melbourne, VIC, Australia; BreastScreen Victoria, Melbourne, VIC, Australia.

The Lancet. Digital health

|March 4, 2026

概括

一个人工智能 (AI) 算法,BRAIx,准确地预测乳腺癌风险提前四年使用乳房影像. 这种人工智能驱动的风险评分可以实现个性化查,通过识别高风险个体,可能挽救生命.

科学领域:

医学成像和人工智能医学成像和人工智能
流行病学风险预测预测
瘤学和乳腺癌查查

背景情况:

人工智能 (AI) 算法越来越多地用于乳腺癌检测的乳房造影.
需要利用人工智能进行个性化乳腺癌风险评估,超出即时检测范围.

研究的目的:

应用流行病学方法,使用人工智能癌症检测算法 (BRAIx AI Reader) 作为乳腺癌风险的中期预测指标 (当前和4年风险).
为个性化查开发一个基于乳房镜的女性特异性风险评分 (BRAIx风险评分).

主要方法:

利用来自BRAIx AI Reader的检测得分,在一个大型训练数据集 (397,648名女性) 上进行校准.
对独立的澳大利亚 (96,348名女性) 和外部瑞典数据集进行了BRAIx风险评分的评估.
应用后勤回归来估计当前和4年侵袭性乳腺癌风险.

主要成果:

在澳大利亚和瑞典数据集 (p<0.0001) 中,BRAIx风险得分显著预测了当前和未来的乳腺癌风险 (p<0.0001).
它是癌症检测在入门和中期风险的最强预测因素,赔率比例分别高达13.80和2.29.
具有BRAIx风险评分>2的女性中期癌症风险显著增加 (OR高达44.7),而前2%的女性4年癌症诊断概率为9.7%.

结论:

BRAIx风险评分是中期乳腺癌风险 (目前为4年) 的可靠预测指标.
人口特异性校准允许从乳房影像计算个人风险得分,为个性化查途径铺平道路.
识别高风险女性,即使是最初清晰的女性,也可以提供有关家族性和非家族性乳腺癌原因的见解,有可能改变查并挽救生命.

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