Comparison of multiple quantitative strategies for neuropathologic image analyses

The traditional semiquantitative (SQ) scoring system for neuropathologic assessment, although widely used, is prone to variability among assessors and does not capture the full spectrum of pathological changes. To address these limitations, digital pathology-based strategies like positive pixel quantitation or advanced artificial intelligence (AI) techniques have been developed. However, a comprehensive comparison of these measures has never been performed. Using 1412 cases from Boston University brain banks, human-driven SQ scoring was compared with computer-driven percent area-stained measures and AI-driven cellular density quantitation of tau pathology in the dorsolateral frontal cortex. When comparing each measure directly in all cases, we observed general agreement between measures. Because the full dataset included a large range of different neuropathologies, to reduce noise we performed a subanalysis in cases with the neurodegenerative disease chronic traumatic encephalopathy (CTE) and examined correlations with clinical and neuropathologic variables. While all methods demonstrated significant ability to predict CTE neuropathology, inconsistent background, noncellular elements, and artifacts increased variability for the positive pixel method. Thus, the AI-driven method was better at identifying pathological changes associated with sparse pathology. Overall, our results demonstrate important differences among neuropathologic assessment techniques and highlight the need for careful consideration when selecting analysis methods.