人类表皮生长因子受体2 (HER2) 表达动态在诊断和复发之间的乳腺癌患者使用人工智能和电子健康记录:RosHER研究
E López-Miranda1,2, P Tolosa-Ortega3, M A Perelló-Martorell4
1Hospital Universitario Ramón y Cajal, Madrid, Spain.
概括
这项研究揭示了乳腺癌患者随着时间的推移,人类表皮生长因子受体2 (HER2) 表达的显著变化. 了解这些HER2动态对于个性化治疗策略至关重要.
科学领域:
- 在瘤学瘤学.
- 生物标志物 生物标志物
- 医疗信息学 医疗信息学
背景情况:
- 人类表皮生长因子受体2 (HER2) 是乳腺癌 (BC) 的关键治疗标.
- 了解HER2表达的时间变化对于有效的个性化医学至关重要.
- 现实世界的数据和自然语言处理 (NLP) 为HER2动态提供了新的见解.
研究的目的:
- 用现实数据评估乳腺癌患者HER2表达的动态.
- 评估在初始诊断和疾病复发或进展之间HER2状态和表达的变化.
- 为了识别HER2不一致及其随时间变化的模式.
主要方法:
- 在RosHER研究中,使用了从电子健康记录 (EHR) 中获取数据的回顾性,观察性,纵向设计.
- 一个NLP工具从2005年至2021年间诊断的成人BC患者中提取了HER2信息.
- 在初始诊断和复发/进展之间分析了HER2动态,至少6个月的间隔.
主要成果:
- 随着时间的推移,在HER2状态 (10.6%) 和表达 (34.0%) 中观察到显著的不一致.
- HER2表达经常在HER2-零,HER2-低和HER2-阳性类别之间转移.
- 特别的转化率被注意到,例如,HER2-低到HER2-零 (32.0%) 和HER2-阳性到HER2-低 (20.8%).
结论:
- 这是第一个使用NLP量化乳腺癌中HER2不一致性的研究.
- 观察到的HER2表达动态凸显了进一步研究和治疗策略的潜在重新评估的需要.
- 人工智能和标准化EHR结构的进步是改善临床数据提取的建议.
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