Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

中文翻译：

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Pixelwise H评分：一种基于数字图像分析的新型度量，可从免疫组织化学图像量化膜生物标志物的表达

免疫组织化学（IHC）分析在评估组织切片中生物标志物的表达以进行诊断和研究应用中起着核心作用。众所周知，对IHC图像进行手动评分是当前的实践标准，在大规模研究的可重复性和可扩展性方面存在一些缺点。在这里，通过使用基于数字图像分析的方法，我们引入了一种新的度量标准，称为像素水平H分数（像素H分数），该度量可量化全幅扫描IHC图像中生物标志物的表达。pix H分数是一种无监督的算法，仅需要指定生物标记和核复数通道的强度阈值即可。我们介绍了在两种不同的全幻灯片图像分析软件包Visiopharm和HALO中pix H分数的详细实现。我们考虑了三种生物标志物P-钙黏着蛋白，PD-L1和5T4，并显示了pix H分数与多种生物标志物丰度的正交测量（如生物标志物mRNA转录本和病理学家H分数）表现出紧密的一致性。我们还将pix H分数与现有的自动图像分析算法进行比较，并证明pix H分数提供了与这些方法相当的性能或明显更好的性能。我们还介绍了经验重采样方法的结果，以评估pix H分数在估计相对于整个肿瘤切除的肿瘤组织内选定区域的生物标志物丰度方面的性能。我们预计，该新指标将广泛应用于量化各种IHC图像中的生物标志物表达。此外，