AUTHOR=Wen Baohong , Zhang Zanxia , Zhu Jing , Liu Liang , Li Yinhua , Huang Haoyu , Zhang Yong , Cheng Jingliang 

TITLE=Apparent Diffusion Coefficient Map–Based Radiomics Features for Differential Diagnosis of Pleomorphic Adenomas and Warthin Tumors From Malignant Tumors

JOURNAL=Frontiers in Oncology

VOLUME=Volume 12 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/oncology/articles/10.3389/fonc.2022.830496

DOI=10.3389/fonc.2022.830496

ISSN=2234-943X

ABSTRACT=Abstract
Purpose: The MRI findings may overlap due to the complex content of parotid gland tumors and the differentiation level of malignant tumor (MT); consequently, patients may undergo diagnostic lobectomy. This study assessed whether radiomics features could noninvasively stratify parotid gland tumors accurately based on apparent diffusion coefficient (ADC) maps.
Methods: This study examined diffusion-weighted image (DWI) obtained with echo planar imaging sequences. 88 benign tumors (BT) (54 pleomorphic adenomas (PA), 34 Warthin tumors (WT), and 42 malignant tumors (MT) of the parotid gland were enrolled. Each case were randomly divided into training and testing cohorts at a ratio of 7:3 and then were compared with each other respectively. ADC maps were digitally transferred to ITK SNAP (www.itksnap.org). The ROI was manually drawn around the whole tumor margin on each slice of ADC maps. After feature extraction, The Synthetic Minority Oversampling TEchnique (SMOTE) was used to remove the unbalance of the training data set. Then we applied the normalization process to the feature matrix. To reduce the similarity of each feature pair, we calculated the Pearson correlation coefficient (PCC) value of each feature pair and eliminated one of them if the PCC value was larger than 0.95. Then recursive feature elimination (RFE) was used to process feature selection. After that, we used linear discriminant analysis (LDA) as the classifier. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the ADC. 
Results: The LDA model based on 13, 8, 3, and 1 features can get the highest AUC in differentiating BT from MT,  PA from WT, PA from MT, and WT from MT on the validation data set, respectively. Accordingly, the AUC and the accuracy of the model on the testing set achieve 0.7637 and 73.17%, 0.925 and 92.31%, 0.8077 and 75.86%,  0.5923 and 65.22%, respectively. 
Conclusion: The ADC-maps-based radiomics features may be used to assist clinicians in the differential diagnosis of parotid gland tumors.