Soroush Zaghi1, Leenoy Hendizadeh2, Tony Hung2, Salar Farahvar2, Elliot Abemayor2, Ali R Sepahdari3. 1. Department of Head and Neck Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. Electronic address: soroush.zaghi@gmail.com. 2. Department of Head and Neck Surgery, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA. 3. Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA.
Abstract
OBJECTIVES: To validate an MRI algorithm characteristic of pleomorphic adenoma (PA). STUDY DESIGN: Cross-sectional analysis. SETTING: Academic tertiary-care medical center. METHODS: A radiologic algorithm for the MRI diagnosis of PA was developed on the basis of five "high probability" criteria that all must be fulfilled for the MRI to qualify as a positive test result: bright T2-signal, sharp margins, heterogeneous nodular enhancement, lobulated contours, T2-dark rim. We then identified MRI images from our institutional database to test the diagnostic accuracy of the proposed algorithm. RESULTS: A total of 103 parotidectomy cases with adequate MRI studies were identified (pleomorphic adenoma n=41, mucoepidermoid carcinoma n=11, Warthin's tumor n=8, adenoid cystic carcinoma n=6, oncocytoma n=6, acinic cell carcinoma n=5, salivary duct carcinoma n=5, and other n=21). Eighteen of 21 cases that met all five "high probability" MRI criteria were consistent with PA on final histopathology; 3 were consistent with carcinoma. MRI had a specificity of 95.1% [95% confidence interval: 85.6-98.7%] and sensitivity of 43.9% [95% C.I.: 28.8-60.1%] for PA. The positive predictive value was 85.7% [95% C.I.: 70.4-100%] and the negative predictive value was 71.9% [95% C. I.: 62.0-81.9%]. The overall diagnostic accuracy was 74.8% [95% C.I.: 66.2-83.3%]. CONCLUSION: A "high probability" MRI is about 95% specific for pleomorphic adenoma. A subset of patients with MRI imaging that is highly suggestive of PA may reliably avoid further workup. The value of MRI in this setting is especially useful if preoperative fine needle aspiration is not readily available. A significant proportion of PAs, however, have indeterminate imaging features that overlap considerably with other benign and malignant lesions. Published by Elsevier Inc.
OBJECTIVES: To validate an MRI algorithm characteristic of pleomorphic adenoma (PA). STUDY DESIGN: Cross-sectional analysis. SETTING: Academic tertiary-care medical center. METHODS: A radiologic algorithm for the MRI diagnosis of PA was developed on the basis of five "high probability" criteria that all must be fulfilled for the MRI to qualify as a positive test result: bright T2-signal, sharp margins, heterogeneous nodular enhancement, lobulated contours, T2-dark rim. We then identified MRI images from our institutional database to test the diagnostic accuracy of the proposed algorithm. RESULTS: A total of 103 parotidectomy cases with adequate MRI studies were identified (pleomorphic adenoma n=41, mucoepidermoid carcinoma n=11, Warthin's tumor n=8, adenoid cystic carcinoma n=6, oncocytoma n=6, acinic cell carcinoma n=5, salivary duct carcinoma n=5, and other n=21). Eighteen of 21 cases that met all five "high probability" MRI criteria were consistent with PA on final histopathology; 3 were consistent with carcinoma. MRI had a specificity of 95.1% [95% confidence interval: 85.6-98.7%] and sensitivity of 43.9% [95% C.I.: 28.8-60.1%] for PA. The positive predictive value was 85.7% [95% C.I.: 70.4-100%] and the negative predictive value was 71.9% [95% C. I.: 62.0-81.9%]. The overall diagnostic accuracy was 74.8% [95% C.I.: 66.2-83.3%]. CONCLUSION: A "high probability" MRI is about 95% specific for pleomorphic adenoma. A subset of patients with MRI imaging that is highly suggestive of PA may reliably avoid further workup. The value of MRI in this setting is especially useful if preoperative fine needle aspiration is not readily available. A significant proportion of PAs, however, have indeterminate imaging features that overlap considerably with other benign and malignant lesions. Published by Elsevier Inc.