Using Artificial Intelligence for Analysis of Histological and Morphological Diversity in Salivary Gland Tumors

Abstract Salivary gland tumors (SGT) are aheterogeneous neoplasms with large morphological diversity and overlapping features. Recently, numerous artificial intelligence (AI) methods shown for reproducible histological diagnosis and prognosis. However, their application to SGT has not been reported to date. This study aims to examine if AI can be used to differentiate between different SGT subtypes based on the analysis of digitized whole-slide images (WSIs) of Haematoxylin and Eosin (H&E) stained slides. A two-stage machine learning (ML) algorithm was developed and tested on 240 scanned H&E WSIs of SGT cases using an open-source bioimage analysis software (QuPath) to train and analyze features on representative regions of interest. The first classifier was designed to differentiate between two benign and four malignant SGT subtypes with an equal split between benign and malignant SGTs (n = 120 each), while the second classifier was used for malignant SGT subtyping (n = 120). Features extracted using the ML classifiers were also analysed using deep learning (DL) networks to determine any performance improvements. Our first classifier showed excellent accuracy for automated differentiation between benign and malignant SGTs (F1-score = 0.90). The second classifier also performed well for differentiation between four different malignant SGTs (average F1 = 0.92). Significant differences between cellularity, nuclear hematoxylin, cytoplasmic eosin, and nucleus/cell ratio (p < 0.05) were seen between tumors in both experiments. Most of the DL networks also achieved high F1-scores for benign versus malignant differentiation (> 0.80), with EfficientNet-B0 giving the best performance (F1 = 0.87) but with inferior accuracy than the ML classifier for malignant subtyping (highest F1 = 0.60 for ResNet-18 and ResNet-50). Our novel findings show that AI can be used for automated differentiation between benign and malignant SGT and tumor subtyping on H&E images. Analysis of a larger multicentre cohort using ML and DL at the WSI level is required to establish the significance and clinical usefulness of these findings.