Radiomics analysis can potentially characterize salivary gland tumors (SGTs) on magnetic resonance imaging (MRI). The procedures for radiomics analysis were various, and no consistent performances were reported. This review evaluated the methodologies and performances of studies using radiomics analysis to characterize SGTs on MRI. We systematically reviewed studies published until July 2023, which employed radiomics analysis to characterize SGTs on MRI. In total, 14 of 98 studies were eligible. Each study examined 23–334 benign and 8–56 malignant SGTs. Least absolute shrinkage and selection operator (LASSO) was the most common feature selection method (in eight studies). Eleven studies confirmed the stability of selected features using cross-validation or bootstrap. Nine classifiers were used to build models that achieved area under the curves (AUCs) of 0.74 to 1.00 for characterizing benign and malignant SGTs and 0.80 to 0.96 for characterizing pleomorphic adenomas and Warthin’s tumors. Performances were validated using cross-validation, internal, and external datasets in four, six, and two studies, respectively. No single feature consistently appeared in the final models across the studies. No standardized procedure was used for radiomics analysis in characterizing SGTs on MRIs, and various models were proposed. The need for a standard procedure for radiomics analysis is emphasized.

Accurate and automatic segmentation of medical images is in increasing demand for assisting disease diagnosis and surgical planning. Although Convolutional Neural Networks (CNNs) have shown great promise in medical image segmentation, they prefer to learn texture features over shape information. Moreover, recent studies have shown the promise that learning the data in a meaningful order can make the network perform better. Inspired by these points, we aimed to propose a two-stage medical image segmentation framework based on contour-aware CNN and voting strategy, which could consider the contour information and a meaningful learning order. In the first stage, we introduced a plug-and-play contour enhancement module that could be integrated into the encoder–decoder architecture to assist the model in learning boundary representations. In the second stage, we employed a voting strategy to update the model using easy samples in order to further increase the performance of our model. We conducted studies of the two publicly available CHAOS (MR) and hippocampus MRI datasets. The experimental results show that, when compared to the recent and popular existing models, the proposed framework can boost overall segmentation accuracy and achieve compelling performance, with dice coefficients of 91.2 ± 2.6% for the CHAOS dataset and 88.2 ± 0.4% for the hippocampus dataset.