Imaging Features and Pathological Analysis of 43 Parotid Basal Cell Adenomas

Purpose. To investigate the correlation between sonographic and computed tomography and pathological features of basal cell adenomas (BCAs) of the parotid gland. Methods. This retrospective study included 41 patients (43 tumors) with BCAs. The tumors were divided into three types based on their location in the parotid gland and their imaging features. The features of the tumors were analyzed. Results. Imaging manifestations and corresponding pathological results of most BCAs of the parotid glands resembled those of benign parotid gland tumors. Malignant transformation occurred in membranous BCAs and in those with extensive cribriform structures. Type-II and type-III tumors accounted for 82.93% of the total proportion. Thirteen tumors showed cystic degeneration with 30.23%, among which type-III tumors could easily develop cystic degeneration. These cystic areas might correspond to cystic degeneration or focal necrosis. Cystic change was not dependent on the tumor size. The pathological features of the tumors were correlated to their imaging manifestations. Conclusion. Most BCAs of the parotid glands have imaging manifestations similar to those of benign parotid gland tumors. BCAs with extensive cribriform structures and of the membranous type can show malignant transformation and should be treated with caution in clinical practice.

Histogram analysis of dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid tumors

Objective Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) histograms were used to investigate whether their parameters can distinguish between benign and malignant parotid gland tumors and further differentiate tumor subgroups. Materials and methods A total of 117 patients (32 malignant and 85 benign) who had undergone DCE-MRI for pretreatment evaluation were retrospectively included. Histogram parameters including mean, median, entropy, skewness, kurtosis and 10th, 90th percentiles were calculated from time to peak (TTP) (s), wash in rate (WIR) (l/s), wash out rate (WOR) (l/s), and maximum relative enhancement (MRE) (%) mono-exponential models. The Mann–Whitney U test was used to compare the differences between the benign and malignant groups. The diagnostic value of each significant parameter was determined on Receiver operating characteristic (ROC) analysis. Multivariate stepwise logistic regression analysis was used to identify the independent predictors of the different tumor groups. Results For both the benign and malignant groups and the comparisons among the subgroups, the parameters of TTP and MRE showed better performance among the various parameters. WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors. Warthin’s tumors showed significantly lower values on 10th MRE and significantly higher values on skewness TTP and 10th WOR, and the combination of 10th MRE, skewness TTP and 10th WOR showed optimal diagnostic performance (AUC, 0.971) and provided 93.12% sensitivity and 96.70% specificity. After Warthin’s tumors were removed from among the benign tumors, malignant parotid tumors showed significantly lower values on the 10th TTP (AUC, 0.847; sensitivity 90.62%; specificity 69.09%; P < 0.05) and higher values on skewness MRE (AUC, 0.777; sensitivity 71.87%; specificity 76.36%; P < 0.05). Conclusion DCE-MRI histogram parameters, especially TTP and MRE parameters, show promise as effective indicators for identifying and classifying parotid tumors. Entropy TTP and kurtosis MRE were found to be independent differentiating variables for malignant parotid gland tumors. The 10th WOR can be used as an indicator to distinguish Warthin’s tumors from other tumors.

Diffusion kurtosis imaging and dynamic contrast-enhanced MRI for the differentiation of parotid gland tumors

Objective To assess the usefulness of combined diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of parotid gland tumors. Methods Seventy patients with 80 parotid gland tumors who underwent DKI and DCE-MRI were retrospectively enrolled and divided into four groups: pleomorphic adenomas (PAs), Warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). DCE-MRI and DKI quantitative parameters were measured. The Kruskal–Wallis H test and post hoc test with Bonferroni correction and ROC curve were used for statistical analysis. Results WTs demonstrated the highest Kep value (median 1.89, interquartile range [1.46–2.31] min−1) but lowest Ve value (0.20, [0.15–0.25]) compared with PAs (Kep, 0.34 [0.21–0.55] min−1; Ve, 0.36 [0.24–0.43]), OBTs (Kep, 1.22 [0.27–1.67] min−1; Ve, 0.28 [0.25–0.41]), and MTs (Kep, 0.71 [0.50–1.23] min−1; Ve, 0.35 [0.26–0.45]) (all p < .05). MTs had the lower D value (1.10, [0.88–1.29] × 10−3 mm2/s) compared with PAs (1.81, [1.60–2.20] × 10−3 mm2/s) and OBTs (1.57, [1.32–1.89] × 10−3 mm2/s) (both p < .05). PAs had the lower Ktrans value (0.12, [0.07–0.18] min−1) compared with OBTs (0.28, [0.11–0.50] min−1) (p < .05). The cutoff values of combined Kep and Ve, D, and Ktrans to distinguish WTs, MTs, and PAs sequentially were 1.06 min−1, 0.28, 1.46 × 10−3 mm2/s, and 0.21 min−1, respectively (accuracy, 89% [71/80], 91% [73/80], 78% [62/80], respectively). Conclusion The combined use of DKI and DCE-MRI may help differentiate parotid gland tumors. Key Points • The combined use of DKI and DCE-MRI could facilitate the understanding of the pathophysiological characteristics of parotid gland tumors. • A stepwise diagnostic diagram based on the combined use of DCE-MRI parameters and the diffusion coefficient is helpful for accurate preoperative diagnosis in parotid gland tumors and may further facilitate the clinical management of patients.

Diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis?

Objective: To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach. Methods: Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists’ diagnosis, and radiomic models were evaluated. Results: Final study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of T2 weighted sequence. A combined radiomics model obtained with all MRI sequences yielded a sensitivity of 91.5% for the diagnosis of pleomorphic adenoma. Conclusion: MRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist. Advances in knowledge: Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of T2 weighted images yields higher specificity for the diagnosis of pleomorphic adenoma compared to a radiologist non-subspecialized in head and neck radiology, thus minimizing false-positive pleomorphic adenoma diagnosis rate and reducing unnecessary surgical complications.