Chen Yijiang, Zee Jarcy, Smith Abigail, Jayapandian Catherine, Hodgin Jeffrey, Howell David, Palmer Matthew, Thomas David, Cassol Clarissa, Farris Alton B, Perkinson Kathryn, Madabhushi Anant, Barisoni Laura, Janowczyk Andrew
Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
Arbor Research Collaborative for Health, Ann Arbor, MI, USA.
J Pathol. 2021 Mar;253(3):268-278. doi: 10.1002/path.5590. Epub 2021 Jan 5.
Inconsistencies in the preparation of histology slides and whole-slide images (WSIs) may lead to challenges with subsequent image analysis and machine learning approaches for interrogating the WSI. These variabilities are especially pronounced in multicenter cohorts, where batch effects (i.e. systematic technical artifacts unrelated to biological variability) may introduce biases to machine learning algorithms. To date, manual quality control (QC) has been the de facto standard for dataset curation, but remains highly subjective and is too laborious in light of the increasing scale of tissue slide digitization efforts. This study aimed to evaluate a computer-aided QC pipeline for facilitating a reproducible QC process of WSI datasets. An open source tool, HistoQC, was employed to identify image artifacts and compute quantitative metrics describing visual attributes of WSIs to the Nephrotic Syndrome Study Network (NEPTUNE) digital pathology repository. A comparison in inter-reader concordance between HistoQC aided and unaided curation was performed to quantify improvements in curation reproducibility. HistoQC metrics were additionally employed to quantify the presence of batch effects within NEPTUNE WSIs. Of the 1814 WSIs (458 H&E, 470 PAS, 438 silver, 448 trichrome) from n = 512 cases considered in this study, approximately 9% (163) were identified as unsuitable for subsequent computational analysis. The concordance in the identification of these WSIs among computational pathologists rose from moderate (Gwet's AC1 range 0.43 to 0.59 across stains) to excellent (Gwet's AC1 range 0.79 to 0.93 across stains) agreement when aided by HistoQC. Furthermore, statistically significant batch effects (p < 0.001) in the NEPTUNE WSI dataset were discovered. Taken together, our findings strongly suggest that quantitative QC is a necessary step in the curation of digital pathology cohorts. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
组织学切片和全切片图像(WSIs)制备过程中的不一致性可能会给后续的图像分析以及用于研究WSIs的机器学习方法带来挑战。这些变异性在多中心队列中尤为明显,其中批次效应(即与生物变异性无关的系统性技术伪像)可能会给机器学习算法引入偏差。迄今为止,手动质量控制(QC)一直是数据集整理的实际标准,但鉴于组织切片数字化工作规模的不断扩大,其主观性仍然很强且过于繁琐。本研究旨在评估一种计算机辅助的QC流程,以促进WSIs数据集可重复的QC过程。使用一个开源工具HistoQC来识别图像伪像,并计算描述WSIs视觉属性的定量指标,应用于肾病综合征研究网络(NEPTUNE)数字病理学存储库。对HistoQC辅助和非辅助整理之间的阅片者间一致性进行了比较,以量化整理可重复性的改善情况。此外,还使用HistoQC指标来量化NEPTUNE WSIs中批次效应的存在情况。在本研究中考虑的来自n = 512例病例的1814张WSIs(458张苏木精和伊红染色、470张过碘酸雪夫染色、438张银染色、448张三色染色)中,约9%(163张)被确定为不适合后续的计算分析。在HistoQC的辅助下,计算病理学家对这些WSIs识别的一致性从中度(不同染色的Gwet's AC1范围为0.43至0.59)提高到了优秀(不同染色的Gwet's AC1范围为0.79至0.93)。此外,在NEPTUNE WSI数据集中发现了具有统计学意义的批次效应(p < 0.001)。综上所述,我们的研究结果强烈表明,定量QC是数字病理学队列整理中的必要步骤。© 2020英国和爱尔兰病理学会。由John Wiley & Sons, Ltd.出版。