Tehran University of Medical Sciences Frontiers in Biomedical Technologies 2345-5837 4 3-4 2017 12 30 90 99 EN Anahita Fathi Kazerooni Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Tehran University of Medical Sciences, Tehran, Iran. AND Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran Sanam Assili Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran Mohammad Reza Alviri Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Tehran University of Medical Sciences, Tehran, Iran Mahnaz Nabil Department of Statistics, Faculty of Mathematical Science, University of Guilan, Rasht, Iran Jalil Pirayesh Islamian Medical Physics Department, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran Hamidreza Saligheh Rad Quantitative MR Imaging and Spectroscopy Group, Research Center for Cellular and Molecular Imaging, Tehran University of Medical Sciences, Tehran, Iran. AND Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran Leila Agha-Ghazvini Department of Radiology, Shariati hospital, Tehran University of medical sciences, Tehran, Iran.AND Department of Radiology, Amir Alam hospital, Tehran University of medical sciences, Tehran, Iran 2018 01 05 2018 03 17 Purpose: In this work, we aimed to propose an automatic classification scheme based on the parameters derived from apparent diffusion coefficient (ADC)-maps for discriminating benign and malignant parotid tumors. Methods: MRI was carried out prospectively on 41 patients presented with parotid tumors who underwent surgery and post-surgical histopathological assessment was provided for them (32 benign, 9 malignant). Based on anatomical images, regions of interest (ROIs) were selected on the most solid parts of tumors on ADC-maps. Three quantitative parameters, namely ADC-Mean, ADC-Max and ADC-Mean were calculated. Automatic classification of parotid tumors using ADC parameters was performed and assessed employing two different classifiers, namely, linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA). Results: Following statistical analysis, it was indicated that the ADC values in benign tumors are significantly higher than malignant tumors. ADC-Mean, and -Max presented statistically significant differences among benign and malignant parotid tumors (p<0.05). Among the extracted parameters, ADC-Max is the most relevant quantitative parameter for tumor classification with 82.9% accuracy, 84.4% specificity, 77.8% sensitivity, and 83.3% area under the ROC curve (AUC) by exploiting each of the automatic classifiers. This implies that this parameter is inherently accurate and adding further classification complexity does not improve the results. A linear classifier using LDA classification based on ADC-max is proposed, which indicates that ADC-Max under 1.48×10-3mm2/s is highly suggestive of malignancy (with 83% accuracy).   Conclusion: ADC-Max is a potential biomarker for discriminating benign and malignant parotid tumors. Using ADC-Max and LDA, a simple and clinically-feasible classifier is proposed. https://fbt.tums.ac.ir/index.php/fbt/article/view/160 https://fbt.tums.ac.ir/index.php/fbt/article/download/160/107