Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid

Abstract

Objectives The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. Methods A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics–clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared. Results In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics–clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively). Conclusion The radiomics–clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland. Key points • The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics–clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

Radiomics Analysis in Characterization of Salivary Gland Tumors on MRI: A Systematic Review

“…Similar to the BT vs. MT studies, three studies indicated that models incorporating clinical and radiomics features outperformed those constructed using only clinical or radiomics features [15,22,25]. One study revealed that radiomics models comprising features extracted from multiple MRI sequences performed better than those utilizing a single sequence [22].…”

“…MRI has also demonstrated comparable efficacy to FNAC in characterizing SGTs [9][10][11]. More recently, the introduction of radiomics analysis to medical imaging has brought new approaches for quantitative imaging analysis [12], so it is not surprising that researchers have investigated the performance of radiomics analysis in characterizing SGTs on MRI [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. Although these studies showed that radiomics analysis offers great potential for characterizing SGTs on MRI, variations in the proposed radiomics models limit its application in clinical practice.…”

Semiquantitative magnetic resonance imaging parameters for differentiating parotid pleomorphic adenoma from Warthin tumor

“…Gabelloni et al ( 29 ) used a support vector machine classifier to differentiate between malignant and benign parotid neoplasms on T 2 WI, with high sensitivity (0.8695), specificity (0.9062), and accuracy (0.8909) in distinguishing PA from WT. However, the application of radiomics or deep learning is complicated and large datasets are required; image segmentation was based on visual inspection and manual delineation was time-consuming and laborious ( 32 , 33 ). The widespread application of radiomics in clinical practice needs to be further optimized.…”