Preanalytic variable effects on segmentation and quantification machine learning algorithms for amyloid-β analyses on digitized human brain slides

Computational machine learning (ML)-based frameworks could be advantageous for scalable analyses in neuropathology. A recent deep learning (DL) framework has shown promise in automating the processes of visualizing and quantifying different types of amyloid-beta deposits as well as segmenting white matter (WM) from gray matter (GM) on digitized immunohistochemically stained slides. However, this framework has only been trained and evaluated on amyloid-beta-stained slides with minimal changes in preanalytic variables. In this study, we evaluated select preanalytical variables including magnification, compression rate, and storage format using three digital slides scanners (Zeiss Axioscan Z1, Leica Aperio AT2, and Leica Aperio GT 450), on over 60 whole slide images, in a cohort of 14 cases having a spectrum of amyloid-beta deposits. We conducted statistical comparisons of preanalytic variables with repeated measures analysis of variance evaluating the outputs of two DL frameworks for segmentation and object classification tasks. For both WM/GM segmentation and amyloid-beta plaque classification tasks, there were statistical differences with respect to scanner types (p < 0.05) and magnifications (p < 0.05). Although small numbers of cases were analyzed, this pilot study highlights the significance of preanalytic variables that may alter the performance of ML algorithms.

Automated summary

A recent study evaluated the impact of preanalytic variables on the performance of a deep learning framework for segmenting and classifying neuropathological features. The study found that scanner type and magnification had statistically significant effects on the segmentation of white matter/gray matter and the classification of amyloid-beta plaques. These findings highlight the importance of considering preanalytic variables in machine learning algorithms.