A tree-based machine learning model to approach morphologic assessment of malignant salivary gland tumors

Malignant salivary gland tumors represent a challenge for pathologists due to their low frequency and morphologic overlap. In recent years machine learning techniques have been applied to the field of pathology to improve diagnostic performance. In the present work, we fitted a machine learning algorithm to approach the diagnosis of malignant salivary gland tumors. Twelve morphologic variables were scored across 115 samples representing the most commonly encountered malignant salivary gland tumors. The sample was randomly split into a discovery and validation set. A recursive partitioning algorithm was used to systematically screen and organize candidate variables into a classification tree using the discovery set. A cross-validation strategy was used to tune the algorithm hyperparameters. Inter-observer concordance was calculated by independent evaluation of 26 randomly selected cases. The five-tiered tree built, required the evaluation of 6 morphological variables. Basaloid appearance, presence of mucous cells, necrosis, cribriform pattern, clear cells and keratinization were selected by the algorithm to build the tree. This diagnostic tool correctly classified 89.9% and 84.6% of the samples in the discovery and validation sets respectively. Misclassification pattern was consistent between both sets. Misclassified tumors belonged to one of three histologic types: epithelial-myoepithelial, polymorphous and mucoepidermoid carcinomas. Other histotypes demonstrated perfect recall in both the discovery and validation sets. Overall inter-observer concordance was good, with median kappa scores between the expert evaluator and training pathologists being 0.81. Overall, our classification tool developed using a recursive partitioning algorithm can effectively guide the morphological approach to malignant salivary gland tumors.