基于深度学习的乳腺癌风险模型,其序列变化和乳腺癌死亡率
Sujeong Shin1, Yoosoo Chang2,3,4, Seungho Ryu5,6,7
1Department of Family Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, South Korea.
Breast cancer (Tokyo, Japan)
|September 3, 2025
概括
它可以准确预测乳腺癌死亡风险. 高Mirai分数和随着时间的推移而增加的风险与韩国女性更高的乳腺癌死亡率显著相关.
科学领域:
- 癌症学
- 医学成像
- 人工智能
背景情况:
- 现有的乳腺癌风险模型在预测死亡结果方面存在局限性.
- 在评估实际死亡率风险模型的预测准确性方面存在差距.
研究的目的:
- 研究Mirai深度学习模型与乳腺癌特异性死亡率之间的关联.
- 评估Mirai风险评分对韩国女性死亡率的预测价值.
主要方法:
- 从2009年至2020年对124,653名无癌症的韩国妇女 (年龄≥34) 的回顾性队列研究.
- 根据Mirai风险分数的分层和按时间风险变化进行分类.
- 分析与乳腺癌特异性死亡率的相关性.
主要成果:
- 与最低的三分之一相比,Mirai风险最高的三分之一显示出明显更高的死亡率 (HR 5. 34).
- 在Mirai得分的时间变化与死亡率相关;持续高风险或增加风险的个体的死亡率更高.
- 保持高风险 (HR 5. 92) 或从低风险转变为高风险 (HR 5.57) 个体的死亡率增加.
结论:
- 最初用于发病率的Mirai模型与乳腺癌特异性死亡率有显著关联.
- 随着时间的推移,Mirai风险得分的变化预测死亡率,支持风险分层查的AI.
- 基于人工智能的模型可以指导预防策略以减少乳腺癌死亡.
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