一个临床风险模型,用于个性化查和预防乳腺癌
Mikael Eriksson1,2, Kamila Czene1, Celine Vachon3
1Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, 171 65 Stockholm, Sweden.
Cancers
|June 28, 2023
概括
结合生活方式和家庭因素的新人工智能 (AI) 风险模型显示,与仅用于成像的人工智能和临床模型相比,更优越的长期乳腺癌风险预测.
科学领域:
- 在瘤学瘤学.
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 使用乳房扫描图像的人工智能 (AI) 模型对短期乳腺癌风险评估充满希望.
- 人工智能风险模型的长期预测性能,特别是当扩展到临床因素时,仍未得到充分研究.
研究的目的:
- 评估图像衍生AI风险模型的长期性能,并增强了生活方式和家庭因素.
- 为了比较这个扩展的人工智能模型的预测准确度与仅用于成像的人工智能模型和已建立的泰勒-库兹克v8临床模型.
主要方法:
- 一个案例-队列研究涉及8110名年龄在40-74岁的瑞典乳腺癌查队列中的女性.
- 开发了两个人工智能模型:一个是使用乳房特征和年龄,另一个是结合额外的生活方式/家庭风险因素.
- 通过比较曲线下的面积 (AUC) 和正预测值 (PPV),在10年的随访期内评估模型的性能.
主要成果:
- 生活方式/家庭扩展的人工智能模型在1-10年内实现了从0.75到0.68的AUC,超过了仅用于成像的人工智能 (0.72-0.65) 和Tyrer-Cuzick v8 (0.62-0.60) 模型.
- 这种扩展的AI模型在各种风险亚组和乳腺癌亚型中显示出更好的表现.
- 在风险最高的5%的女性中,扩展AI模型产生了较高的PPV (5.8%),与仅成像AI (5.3%) 和Tyrer-Cuzick (4.6%) 相比.
结论:
- 生活方式/家庭扩展的AI风险模型在短期和长期乳腺癌风险评估方面表现出卓越的表现.
- 这种增强的AI方法比仅用于成像的AI和传统的临床风险模型提供了更好的准确性.
- 这些发现支持将生活方式和家庭数据集成到AI模型中,以实现更有效的乳腺癌风险分层.
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