Background Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared. Results The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts. Conclusions The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

Background: Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods: The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.Results: The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity= 0.90 and 0.88, specificity=0.82 and 0.80, positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p>0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p=0.000) and validation (0.90 vs. 0.68, p=0.001) cohorts.Conclusions: The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

Background: Magnetic resonance imaging (MRI) is used routinely in the clinical management, and we explored the diagnostic value of radiomic signatures based on MRI to distinguish parotid pleomorphic adenoma from parotid adenolymphoma. Methods: The clinical characteristics and images data were retrospectively collected from 252 cases (126 cases in training cohort and 76 patients in verification cohort) in this study. And 429 radiomic features of T1-weighted imaging (T1WI) sequence and 414 radiomic features of T2-weighted imaging (T2WI) were extracted from MRI images. We selected the radiomic features from three sequences (T1WI, T2WI and T1-2WI) by univariate analysis, lasso correlation and spearman correlation. Then we built six quantitative radiological models based on the selected radiomic features using two machine learning methods (multivariable logistic regression, MLR and support vector machine, SVM). We assessed the performance of the six radiomic models, and an ideal radiomic signature was chosen to compare its diagnosis efficacy with that of the clinical model. Results: The radiomic model based on features of T1-2WI sequence by MLR showed optimal discriminatory (accuracy = 0.87 and 0.86, F-1score = 0.88 and 0.86, Sensitivity= 0.90 and 0.88, Specificity=0.82 and 0.80 positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in training and validation cohorts, respectively) and its good calibration was also observed (p>0.05). The area under the receiver operating characteristic curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.74, p=0.000) and validation (0.90 vs. 0.73, p=0.001) cohorts. Conclusions: The radiomic model based on MRI in our study is complementary to the current knowledge of differential diagnosis for parotid pleomorphic adenoma and parotid adenolymphoma.

Background: Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA. Methods: The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.Results: The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity= 0.90 and 0.88, specificity=0.82 and 0.80, positive predictive value=0.86 and 0.84, negative predictive value=0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p>0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p<0.001) and validation (0.90 vs. 0.68, p=0.001) cohorts.Conclusions: The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.