Background Salivary gland tumours (SGT) are a relatively rare group of neoplasms with a wide range of histopathological appearance and clinical features. To date, most of the epidemiological studies on salivary gland tumours are limited for a variety of reason including being out of date, extrapolated from either a single centre or country studies, or investigating either major or minor glands only. Methods This study aimed to mitigate these shortcomings by analysing epidemiological data including demographic, anatomical location and histological diagnoses of SGT from multiple centres across the world. The analysed data included age, gender, location and histological diagnosis from fifteen centres covering the majority of the world health organisation (WHO) geographical regions between 2006 and 2019. Results A total of 5739 cases were analysed including 65% benign and 35% malignant tumours. A slight female predilection (54%) and peak incidence between the fourth and seventh decade for both benign and malignant tumours was observed. The majority (68%) of the SGT presented in major and 32% in the minor glands. The parotid gland was the most common location (70%) for benign and minor glands (47%) for malignant tumours. Pleomorphic adenoma (70%), and Warthin’s tumour (17%), were the most common benign tumours whereas mucoepidermoid carcinoma (26%) and adenoid cystic carcinoma (17%) were the most frequent malignant tumours. Conclusions This multicentre investigation presents the largest cohort study to date analysing salivary gland tumour data from tertiary centres scattered across the globe. These findings should serve as a baseline for future studies evaluating the epidemiological landscape of these tumours.

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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.

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Background: Salivary gland tumours (SGT) are a relatively rare group of neoplasms with a wide range of histopathological appearance and clinical features. To date, most of the epidemiological studies on salivary gland tumours are limited for a variety of reason including being out of date, extrapolated from either a single centre or country studies, or investigating either major or minor glands only. Methods: This study aimed to mitigate these shortcomings by analysing epidemiological data including demographic, anatomical location and histological diagnoses of SGT from multiple centres across the world. The analysed data included age, gender, location and histological diagnosis from fifteen centres covering the majority of the world health organisation (WHO) geographical regions between 2006 and 2019. Results: A total of 5798 cases were analysed including 65% benign and 35% malignant tumours. A slight female predilection (54%) and peak incidence between the fourth and seventh decade for both benign and malignant tumours was observed. The majority (69%) of the SGT presented in major and 31% in the minor glands. The parotid gland was the most common location (70%) for benign and minor glands (46%) for malignant tumours. Pleomorphic adenoma (70%), and Warthin tumour (17%), were the most common benign tumours whereas mucoepidermoid carcinoma (25%) and adenoid cystic carcinoma (16%) were the most frequent malignant tumours. Conclusions: This multicentre investigation presents the largest cohort study to date analysing salivary gland tumour data from tertiary centres scattered across the globe. These findings should serve as a baseline for future studies evaluating the epidemiological landscape of these tumours.