Department of Pathology, Stanford University School of Medicine, Stanford, CA.
Boston Cell Standards Corp., Boston, MA.
Appl Immunohistochem Mol Morphol. 2022 Aug 1;30(7):477-485. doi: 10.1097/PAI.0000000000001045. Epub 2022 Jul 13.
Assessment of automated immunohistochemical staining platform performance is largely limited to the visual evaluation of individual slides by trained personnel. Quantitative assessment of stain intensity is not typically performed. Here we describe our experience with 2 quantitative strategies that were instrumental in root cause investigations performed to identify the sources of suboptimal staining quality (decreased stain intensity and increased variability). In addition, these tools were utilized as adjuncts in validation of a new immunohistochemical staining instrument. The novel methods utilized in the investigation include quantitative assessment of whole slide images (WSI) and commercially available quantitative calibrators. Over the course of ~13 months, these methods helped to identify and verify correction of 2 sources of suboptimal staining. One root cause of suboptimal staining was insufficient/variable power delivery from our building's electrical circuit. This led us to use uninterruptible power managers for all automated immunostainer instruments, which restored expected stain intensity and consistency. Later, we encountered one instrument that, despite passing all vendor quality control checks and not showing error alerts was suspected of yielding suboptimal stain quality. WSI analysis and quantitative calibrators provided a clear evidence that proved critical in confirming the pathologists' visual impressions. This led to the replacement of the instrument, which was then validated using a combination of standard validation metrics supplemented by WSI analysis and quantitative calibrators. These root cause analyses document 2 variables that are critical in producing optimal immunohistochemical stain results and also provide real-world examples of how the application of quantitative tools to measure automated immunohistochemical stain output can provide a greater objectivity when assessing immunohistochemical stain quality.
评估自动化免疫组化染色平台的性能在很大程度上仅限于由经过培训的人员对单个幻灯片进行视觉评估。通常不进行染色强度的定量评估。在这里,我们描述了我们在 2 种定量策略方面的经验,这些策略对于确定导致染色质量不佳(染色强度降低和变异性增加)的根本原因的调查至关重要。此外,这些工具还被用于验证新的免疫组化染色仪器。调查中使用的新方法包括对全切片图像(WSI)和市售定量校准器进行定量评估。在大约 13 个月的时间里,这些方法有助于确定并验证了 2 种导致染色质量不佳的原因的纠正。导致染色质量不佳的一个根本原因是我们建筑物的电路供电不足/不稳定。这导致我们为所有自动化免疫组化仪器使用不间断电源管理器,这恢复了预期的染色强度和一致性。后来,我们遇到了一种仪器,尽管通过了所有供应商的质量控制检查,并且没有显示错误警报,但怀疑其产生的染色质量不佳。WSI 分析和定量校准器提供了确凿的证据,这对确认病理学家的视觉印象至关重要。这导致更换了仪器,然后使用标准验证指标与 WSI 分析和定量校准器相结合进行了验证。这些根本原因分析记录了产生最佳免疫组化染色结果的 2 个关键变量,并提供了实际应用示例,说明了如何应用定量工具来测量自动化免疫组化染色输出,从而在评估免疫组化染色质量时提供更大的客观性。