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Purpose: Recent studies suggest an inverse association between cancer in general and Alzheimer’s disease (AD), suggesting that there are common factors in these diseases. In this study, we demonstrate a complete workflow to streamline the analysis of stained histological brain samples to reduce user-dependence. As a proof of concept, we investigate the influence of Amyloid Beta (Aβ) plaques and hyperphosphorylated Tau protein (pTau), hallmarks of AD, in Glioblastoma (GBM) patients. Methods: The automated digital histology processing workflow is demonstrated on a 10-patient cohort. First, tissue samples were taken at autopsy from regions known to be common Aβ and pTau hotspots. The tissue samples were then subclassified regionally using a ResNet50 neural network to separate gray and white matter, and tissue processing artifacts. Positively stained areas were then automatically detected and analyzed between subjects. We evaluated the automatic sample classification using a 5-fold cross validation. Results: Cross validation achieved an overall accuracy of 93.88%. Positively stained sample regions were automatically detected and present in six of ten patients for Aβ and in three of ten patients for pTau. General accumulation of both pTau and Aβ calculated over all samples correlated with the age of the patient, and showed decreased accumulation in the brain hemisphere where the primary tumor was located. Conclusion: The proposed method for processing histological samples of the brain automates the time consuming and error prone manual segmentation of grey matter and removal of artifacts. Our study highlights hemispheric differences in pTau and Aβ accumulation. Future studies of pTau and Aβ in the presence of GBM will help to understand how tumor location and growth affect micro-environmental factors in larger cohorts of patients.
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