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tau研究中的数字病理学:QuPath与HALO的比较

Digital pathology in tau research: A comparison of QuPath and HALO.

作者信息

Gonzalez Angelique D, Wadop Yannick N, Danner Benjamin, Clarke Kyra M, Dopler Matthew B, Ghaseminejad-Bandpey Ali, Babu Sahana, Parker-Garza Julie, Corbett Cole, Alhneif Mohammad, Keating Mallory, Bieniek Kevin F, Maestre Gladys E, Seshadri Sudha, Etemadmoghadam Shahroo, Fongang Bernard, Flanagan Margaret E

机构信息

Glenn Biggs Institute for Alzheimer's and Neurodegenerative Diseases, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.

Department of Pathology and Laboratory Medicine, University of Texas Health Science Center at San Antonio, San Antonio, TX, United States.

出版信息

J Neuropathol Exp Neurol. 2025 Apr 16. doi: 10.1093/jnen/nlaf026.

Abstract

The application of digital pathology tools has expanded in recent years, but non-neoplastic human brain tissue presents unique challenges due to its complexity. This study evaluated HALO and QuPath tau quantification performance in the hippocampus and mid-frontal gyrus across various tauopathies. Percent positivity emerged as the most reliable measure, showing strong correlations with Braak stages and CERAD scores, outperforming object and optical densities. QuPath demonstrated superior correlations with Braak stages, while HALO excelled in aligning with CERAD scoring. However, HALO's optical density was less consistent. Paired t-tests revealed significant differences in object and optical densities between platforms, though percent positivity was consistent across both. QuPath's threshold-based object density showed similar agreement with manual counts compared to HALO's AI-dependent approach (all ρ > 0.70). Reanalysis of QuPath further improved its agreement with manual measurements and correlations with Braak and CERAD scores (all ρ > 0.70). HALO offers a user-friendly interface and excels in certain metrics but is hindered by frequent software malfunctions and more limited flexibility. In contrast, QuPath's customizable workflows and superior performance in Braak staging make it more suitable for advanced and larger-scale analyses. Overall, our study highlights the strengths and limitations of these platforms, helping guide their application in neuropathology.

摘要

近年来,数字病理学工具的应用有所扩展,但非肿瘤性人类脑组织因其复杂性而带来了独特的挑战。本研究评估了HALO和QuPath在各种tau蛋白病中对海马体和额中回的tau蛋白定量性能。阳性百分比成为最可靠的测量指标,与Braak分期和CERAD评分显示出强相关性,优于目标密度和光密度。QuPath与Braak分期表现出更好的相关性,而HALO在与CERAD评分的一致性方面表现出色。然而,HALO的光密度不太一致。配对t检验显示不同平台之间目标密度和光密度存在显著差异,不过阳性百分比在两个平台上是一致的。与HALO依赖人工智能的方法相比,QuPath基于阈值的目标密度与手动计数的一致性相似(所有ρ>0.70)。对QuPath的重新分析进一步提高了其与手动测量的一致性以及与Braak和CERAD评分的相关性(所有ρ>0.70)。HALO提供了用户友好的界面,在某些指标上表现出色,但受到频繁软件故障和灵活性更有限的阻碍。相比之下,QuPath可定制的工作流程以及在Braak分期方面的卓越性能使其更适合进行高级和大规模分析。总体而言,我们的研究突出了这些平台的优势和局限性,有助于指导它们在神经病理学中的应用。

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