Juan C.-JHuang T.-YLiu Y.-JShen W.-CWang C.-WHsu KShin NRUEY-FENG CHANG2022-04-252022-04-25202209523480https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118501999&doi=10.1002%2fnbm.4642&partnerID=40&md5=1a9b8c9d0d6d75bab5b4057868c6d224https://scholars.lib.ntu.edu.tw/handle/123456789/607448In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors. ? 2021 John Wiley & Sons, Ltd.head and neckmagnetic resonance imagingparotid gland tumorClassification (of information)DiagnosisMachine learningMagnetismResonanceSurface diffusionTumorsApparent diffusion coefficientDiffusion weighted imagingDiffusion-weighted magnetic resonanceHead and neckMalignant tumorsMultifeaturesMultiple featuresParotid gland tumorsParotid glandsPerformanceMagnetic resonance imagingjournal article10.1002/nbm.4642347386712-s2.0-85118501999