开发和验证激素受体阳性早期乳腺癌复发的术后预后模型
Ruixin Pan1, Haoting Shi1, Yiqing Shen2
1Department of General Surgery, Comprehensive Breast Health Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 20025, China.
Scientific reports
|March 23, 2025
概括
一个新的深度学习模型,HERPAI,准确地预测早期激素受体阳性,HER2-阴性乳腺癌患者的复发风险. 该工具有助于识别可能受益于强化辅助治疗的个体.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 医疗人工智能 医疗人工智能
背景情况:
- 准确预测早期激素受体阳性,人体表皮生长因子受体阴性 (HR+/HER2-) 乳腺癌 (BC) 复发的情况对于定制辅助治疗至关重要.
- 现有的研究往往缺乏对复发风险较低的患者的足够数据,这突显了需要改进预后工具的需要.
研究的目的:
- 开发和验证一个名为HERPAI的深度学习预后模型,用于预测早期HR+/HER2-BC患者无侵袭性疾病生存 (iDFS) 的风险.
- 确定患有复发高风险的患者,他们可能会从扩大辅助治疗策略中受益.
主要方法:
- 一项回顾性研究包括来自四个医疗中心的6340名早期 (T1-2N0-1) HR+/HER2-侵入性BC患者,接受手术和内分泌治疗.
- 一个基于变压器的深度学习模型 (HERPAI) 使用从衍生队列 (n=5424) 的临床和病理数据开发,并在外部队列 (n=916) 上验证.
- 使用C指数评估模型性能,并计算危险比率 (HR) 来比较已识别的患者群体之间的复发风险.
主要成果:
- 在验证 (C指数=0.73),内部测试 (C指数=0.73) 和外部测试 (C指数=0.68) 队列中,HERPAI表现一致.
- 由 HERPAI 分类为高风险的患者在验证 (HR=2.56,P=0.01) 和外部测试 (HR=1.94,P=0.049) 队列中表现出明显增加的复发风险.
- 该模型确定了超过10%的5年复发风险值,有助于风险分层.
结论:
- HERPAI 是一个有前途的深度学习工具,用于准确识别早期HR+/HER2-BC患者的复发风险高.
- 这种模式可以帮助临床医生优先考虑那些可能从强化辅助治疗中获得更大的益处的患者,从而优化治疗决策.
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