Impact of Tissue Thickness on Computational Quantification of Features in Whole Slide Images for Diagnostic Pathology

Tissue section thickness (TST) is an understudied variable in digital pathology that significantly impacts both visual assessments and computational analyses. This study systematically examines the effects of TST on whole slide images (WSIs) and nuclear-level features using thyroid tissue samples (n = 144) prepared at thicknesses ranging from 0.5 to 10 µm. By minimizing preanalytical variables and batch effects, we aimed to isolate TST as the primary factor in our experiment. Visual assessments indicated that thinner Sects. (0.5-3 µm) were more transparent with distinct cellular features, while thicker Sects. (5-10 µm) appeared darker with increased staining intensity and artifacts. Quantitative analyses were performed using open-source tools such as HistoQC for WSI quality control, HoverNet for nuclear segmentation, and feature extraction with Scikit-learn and Mahotas. Both WSI and nuclear-level metrics were significantly influenced by TST. The Haralick texture feature of difference entropy, which measures texture complexity, showed a 13.7% decrease in nuclei as TST increased, indicating fewer complex textures in thicker sections. Additionally, intensity decreased substantially with thicker tissue, dropping by 26.1% at the WSI level and 30.4% at the nuclear level. WSI contrast exhibited an increase of 92.6% when transitioning from 0.5 to 10 µm. These findings demonstrate that variations in TST can obscure or alter the appearance of biological signals, complicating both visual diagnostics and computationally extracted features. The study highlights the need for standardized tissue section thickness protocols, alongside consistent reporting of these standards, to ensure accuracy and reliability in both visual evaluations and computational analyses within digital pathology workflows.