Differentiating parotid gland pleomorphic adenoma from adenolymphoma based on T1WI and IDEAL-T2WI radiomics models
10.13929/j.issn.1003-3289.2020.05.008
- VernacularTitle: 基于T1WI及IDEAL-T2WI影像组学模型鉴别腮腺多形性腺瘤和腺淋巴瘤
- Author:
Yukun ZHOU
1
Author Information
1. Department of Medical Imaging, Shanxi Medical University
- Publication Type:Journal Article
- Keywords:
Adenolymphoma;
Adenoma, pleomorphic;
Machine learning;
Magnetic resonance imaging;
Parotid gland;
Radiomics
- From:
Chinese Journal of Medical Imaging Technology
2020;36(5):675-679
- CountryChina
- Language:Chinese
-
Abstract:
Objective: To explore the value of radiomics features and machine learning models based on T1WI and IDEAL-T2WI in differential diagnosis of parotid gland pleomorphic adenoma (PA) from adenolymphoma (AL). Methods: Clinical and imaging data of 58 patients with parotid tumors, including 33 with PA and 25 with AL were retrospectively analyzed. Axial T1WI and IDEAL-T2WI were manually segmented, and radiomics features were extracted using the radcloud software. Effective radiomics features were selected with the variance threshold method, SelectKBest and Lasso algorithm. The machine learning models were established by using random forest and Logistic regression algorithm, and the ROC curves were drawn to analyze the diagnostic performance. The ability of T1WI, IDEAL-T2WI and image combination in diagnosis of PA from AL were analyzed. Results: T1WI, IDEAL-T2WI and IDEAL-T2WI combined with T1WI obtained 6, 9 and 12 effective features. The random forest model based on IDEAL-T2WI combined with T1WI sequences had the highest diagnostic performance, with AUC of 0.87 (95%CI[0.59,1.00]) and accuracy of 0.83. Conclusion: Radiomics features and machine learning models based on T1WI and IDEAL-T2WI can provide important references for differentiation of PA and AL.
