用计算方法计算SISH放大信号,用于评估HER2状态
Zaka Ur Rehman1, Mohammad Faizal Ahmad Fauzi1, Wan Siti Halimatul Munirah Wan Ahmad1,2
1Faculty of Engineering, Multimedia University, Cyberjaya, Selangor, Malaysia.
PeerJ. Computer science
|December 9, 2024
概括
本研究介绍了一种自动化的深度学习方法,用于评估乳腺癌中的HER2基因状态,使用SISH全幻灯片图像. 计算机辅助方法准确量化了HER2放大,为手工分析提供了更快,更可重复的替代方案.
科学领域:
- 在瘤学瘤学.
- 生物标记分析 生物标记分析
- 计算病理学计算病理学
背景情况:
- 人类表皮生长因子受体2 (HER2) 基因对于乳腺癌治疗决策至关重要.
- 准确的HER2基因放大状态对于有针对性的治疗选择至关重要.
- 目前用于HER2评估的手动方法耗时且容易变化.
研究的目的:
- 开发和验证计算机辅助的深度学习方法,用于自动化 HER2 基因状态量化.
- 与手动计数相比,评估自动化方法的准确性和可重复性.
- 为临床HER2测试提供一个强大而高效的替代方案.
主要方法:
- 使用整个幻灯片图像 (WSI) 与HER2和CEP17信号的现场混合化银 (SISH) 染色.
- 采用图像处理和Stardist深度学习模型来识别和隔离核.
- 应用了信号分割的常规值,并根据ASCO/CAP 2018标准计算了HER2-CEP17比率.
主要成果:
- 在自动化方法和病理学家手动计数之间实现了0.91的相关系数.
- 自动化结果与参考标准之间显示的统计学上微不足道的差异 (p > 0.05).
- 显示了量化工作和时间的显著减少.
结论:
- 深度学习有效地自动化了乳腺癌中HER2状态的确定.
- 拟议的方法提供了一种可复制和改进的替代现有手动量化技术.
- 这种自动化方法有可能增强针对HER2向治疗的临床实践.
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