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• Clinical Article •
Differentiating salivary gland pleomorphic adenoma from basal cell adenoma based on multimodal magnetic resonance imaging radiomics
YAN Xiaofan  SHAO Shuo  ZHENG Ning  CUI Jingjing  YUAN Ziyin  LI Sen 

Cite this article as: Yan XF, Shao S, Zheng N, et al. Differentiating salivary gland pleomorphic adenoma from basal cell adenoma based on multimodal magnetic resonance imaging radiomics[J]. Chin J Magn Reson Imaging, 2022, 13(7): 22-28. DOI:10.12015/issn.1674-8034.2022.07.005.


[Abstract] Objective To explore the value of radiomics models based on ADC, T1WI and T2WI in differentiating salivary gland pleomorphic adenoma (PA) from basal cell adenoma (BCA).Materials and Methods The MR images of 129 cases with PA and 48 cases with BCA from Jining First People's Hospital from January 2015 to October 2021 were retrospectively analyzed, and then these data were randomly divided into training sets (n=141) and test sets (n=36) at a ratio of 8∶2. The three-dimensional volume region of interest of the tumor was manually delineated on the axial ADC, T1WI and T2WI images, and radiomics features were extracted; the variance threshold method, analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) based on 5-fold cross validation were used to single out the most valuable radiomic features, and these selected features were combined with two classifiers, logistic regression (LR) and support vector machine (SVM), for training the models, and then the models were verified in the test sets. ROC curve was drawn to evaluate the efficacy of LR and SVM models in differentiating PA from BCA. In addition, the Delong Test was used to compare the models, and the decision curve and calibration curve were used to evaluate the models.Results A total of 15, 3, 15 and 23 optimal features were obtained from ADC, T1WI, T2WI and combined sequence (ADC+T1WI+T2WI) image respectively. In the training set, the area under the curve (AUC) of the LR and SVM models constructed based on the ADC map, T1WI map, T2WI map, and joint model were 0.955, 0.961, 0.812, 0.813, 0.939, 0.949, 0.994, 0.995, respectively. The AUC values of the LR model constructed based on ADC, T1WI, T2WI and combined sequence image for differential diagnosis of PA and BCA were 0.906, 0.780, 0.868 and 0.972, respectively, and the AUC values of the SVM model were 0.924, 0.783, 0.847 and 0.959, respectively. In the training sets, the combined sequence models were better than the T1WI or T2WI-based radiomics models (P＜0.05), and there was no significant difference between the combined sequence models and the ADC-based radiomics models (P＞0.05), the accuracy, sensitivity and specificity of the combined sequence models were 98.6%-98.7%, 96.4%-98.4%, 98.8%-99.4% respectively, the accuracy, sensitivity and specificity of the ADC radiomics models were 91.4%-91.8%, 75.0%-79.7%, 95.7%-98.1% respectively. In the test sets, there was no significant difference in AUC between the models (P＞0.05).Conclusions The combined sequence models and ADC-based radiomics models were better than the T1WI and T2WI-based radiomics models in differentiating pleomorphic adenoma and basal cell adenoma. Compared with ADC-based radiomics models, the combined sequence models had higher accuracy, sensitivity and specificity.
[Keywords] salivary gland tumors；pleomorphic adenoma；basal cell adenoma；radiomics；magnetic resonance imagining

YAN Xiaofan1   SHAO Shuo2   ZHENG Ning2*   CUI Jingjing3   YUAN Ziyin2   LI Sen1  

1 Shandong First Medical University (Shandong Academy Of Medical Sciences), Jinan 250000, China

2 Magnatic Resonance Imaging Room, Jining First People's Hospital, Jining 272000, China

3 Shanghai United Imaging Intelligence Medical Technology Co., Ltd., Shanghai 200000, China

Zheng N, E-mail: zhengning_369@163.com

Conflicts of interest   None.

Received  2022-02-05
Accepted  2022-06-22
DOI: 10.12015/issn.1674-8034.2022.07.005
Cite this article as: Yan XF, Shao S, Zheng N, et al. Differentiating salivary gland pleomorphic adenoma from basal cell adenoma based on multimodal magnetic resonance imaging radiomics[J]. Chin J Magn Reson Imaging, 2022, 13(7): 22-28. DOI:10.12015/issn.1674-8034.2022.07.005.

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