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Abstract
Alzheimer's disease neuropathological changes (ADNC)—operationalized with semi-quantitative parameters—represent the consensus-based gold standard for diagnostic evaluation of disease severity. Although useful, ADNC diagnostic frameworks have limitations, particularly in advanced disease stages where pathological severity varies widely within a given diagnostic category. Further, some individuals lacking cognitive impairment are inappropriately categorized as having severe ADNC. In this study, quantitative pathology metrics and alternative tissue sampling schemes were integrated with data about premortem cognitive status, in order to derive clinically informed neuropathologic diagnostic thresholds. Specific goals of the current study were to generate data-driven, standardized diagnostic cut-points, with the most severe stages of ADNC having consistent implications: Braak neurofibrillary tangle (NFT) stage V cases being always impaired (MCI or demented) and Braak NFT stage VI cases being always demented. Utilizing whole-slide imaging and AI-based image analysis, object-based (NFT counts) and pixel-based (phosphorylated tau [pTau] burden) quantifications were compared across neocortical regions in three subsamples of cases from the University of Kentucky ADRC autopsy cohort (n = 329, all with clinical evaluations within 2 years of death). We also compared between HALO- and Aperio-based platform results, and between AT8 and PHF-1 pTau antibodies. Applying refined thresholds enabled reclassification of cases previously misaligned with their digitally determined appropriate status: 17% of cases were thus reclassified. The use of commercially available software, standardized classifier architectures, and interoperable analysis pipelines facilitated scalable and reproducible digital quantification. Cross-institutional validation at University of Texas San Antonio, with the same algorithms applied in both research centers, confirmed near-perfect agreement of pathology counts, underscoring shareability and the feasibility of harmonized digital workflows for collaborative research and diagnostic purposes. These findings support the integration of quantitative digital pathology into standard neuropathological protocols and provide a scalable model for future multi-site studies. Enabling comparisons of analytical platforms, pTau antibodies, and anatomical sampling strategies, an updated workflow demonstrated high reproducibility and consistent clinical-pathological correlations.
Document Type
Article
Publication Date
2026
Digital Object Identifier (DOI)
https://doi.org/10.1111/bpa.70102
Funding Information
National Institutes of Health, Grant/Award Numbers: P30 AG072946, T32 AG078110, U24 NS133945, U24 NS133949, R01 AG054459
Repository Citation
Shahidehpour, Ryan K.; Neltner, Allison M.; Klusty, Mitchell A.; Corbett, Cole; Gonzalez, Angelique D.; Gutman, David A.; Fardo, David W.; Bachstetter, Adam D.; Bumgardner, Cody; Flanagan, Margaret E.; and Nelson, Peter T., "Data-driven thresholds for standardized classification of severe Alzheimer’s disease neuropathology using digital neuropathology" (2026). Sanders-Brown Center on Aging Faculty Publications. 220.
https://uknowledge.uky.edu/sbcoa_facpub/220
Notes/Citation Information
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2026 The Author(s). Brain Pathology published by John Wiley & Sons Ltd on behalf of International Society of Neuropathology.