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使用HistoQC预测人类表皮生长因子受体2 (HER2) 评估上的分歧

Laura Verzellesi¹, Moira Ragazzi², Andrea Botti³

¹Medical Physics Unit, Azienda Unità Sanitaria Locale-IRCCS di Reggio Emilia, Reggio Emilia, Italy; Department of Computer Sciences, University of Pisa, Pisa, Italy; Enzo Ferrari Department of Engineering, University of Modena and Reggio Emilia, Modena, Italy.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

|August 15, 2025

概括

这项研究开发了一个机器学习模型来识别导致人类表皮生长因子受体2 (HER2) 评估差异的整个幻灯片图像 (WSIs). 该工具旨在提高乳腺癌治疗中的诊断准确性.

科学领域:

计算病理学计算病理学
机器学习在诊断中的应用.
生物标志物的评估评估.

背景情况:

人类表皮生长因子受体2 (HER2) 对乳腺癌预后和治疗反应至关重要.
准确的HER2评估对于向治疗至关重要,但观察者之间的变异性是一个挑战.

研究的目的:

开发一种机器学习 (ML) 系统,用于识别可能导致观察者在HER2评估中的差异的整个幻灯片图像 (WSI).
提高HER2诊断过程的可靠性.

主要方法:

使用了132个病理学幻灯片和双盲HER2评估的数据集.
HistoQC软件分析了幻灯片的质量相关特征.
一个XGBoost ML模型被训练在HistoQC指标上,以预测观察者之间的分歧.

主要成果:

在交叉验证过程中,ML模型在训练集上实现了0.86 (SD=0.09) 的平均AUC.
该模型在看不见的测试集上显示了0.81的AUC (CI=[0.82-0.94]),表明了强大的预测性能.

结论:

成功开发了一种机器学习模型,以标记HER2病理学的潜在诊断分歧.
幻灯片质量指标与诊断结果相关,这表明了质量保证的途径.
该工具有可能被整合到解剖病理学质量保证系统中,以改进HER2诊断.

关键词:

诊断质量控制评估的评估.数字病理学数字病理学不同意见的不同意见.在HER2中,HER2是HER2.

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