Journal of Pathology Informatics (Jan 2023)
Validation of automated positive cell and region detection of immunohistochemically stained laryngeal tumor tissue using digital image analysis
Abstract
Objectives: This study aimed to validate a digital image analysis (DIA) workflow for automatic positive cell detection and positive region delineation for immunohistochemical hypoxia markers with a nuclear (hypoxia-inducible factor 1α [HIF-1α]) and a cytoplasmic (pimonidazole [PIMO]) staining pattern. Materials and methods: 101 tissue fragments from 44 laryngeal tumor biopsies were immunohistochemically stained for HIF-1α and PIMO. QuPath was used to determine the percentage of positive cells and to delineate positive regions automatically. For HIF-1α, only cells with strong staining were considered positive. Three dedicated head and neck pathologists scored the percentage of positive cells using three categories (0: 33%;). The pathologists also delineated the positive regions on 14 corresponding PIMO and HIF-1α-stained fragments. The consensus between observers was used as the reference standard and was compared to the automatic delineation. Results: Agreement between categorical positivity scores was 76.2% and 65.4% for PIMO and HIF-1α, respectively. In all cases of disagreement in HIF-1α fragments, the DIA underestimated the percentage of positive cells. As for the region detection, the DIA correctly detected most positive regions on PIMO fragments (false positive area=3.1%, false negative area=0.7%). In HIF-1α, the DIA missed some positive regions (false positive area=1.3%, false negative area=9.7%). Conclusions: Positive cell and region detection on biopsy material is feasible, but further optimization is needed before unsupervised use. Validation at varying DAB staining intensities is hampered by lack of reliability of the gold standard (i.e., visual human interpretation). Nevertheless, the DIA method has the potential to be used as a tool to assist pathologists in the analysis of IHC staining.
