BackgroundDifferent parotid tumors differ in terms of treatment strategies due to their distinct biological behaviors. Time-dependent diffusion magnetic resonance imaging (td-dMRI) can characterize and quantify the cytological indexes, and then aid the differential diagnosis of various tumors. However, the value of td-dMRI in the analysis of parotid gland tumors remains unclear.PurposeTo investigate the value of quantitative parameters derived from td-dMRI in the analysis of parotid gland tumors.Material and MethodsIn total, 39 patients with parotid gland tumors were prospectively enrolled, including 24 patients with polymorphic adenomas (PAs), eight with Warthin's tumors (WTs), and seven with malignant tumors (MTs). Td-dMRI was performed for preoperative evaluation. Intracellular volume fraction (Vin), mean cell size (d), extracellular diffusion coefficient (Dex), and cellularity were obtained based on the Imaging Microstructural Parameters Using Limited Spectrally Edited Diffusion model, and compared among the three groups. One-way ANOVA, Kruskal-Wallis test, and receiver operating characteristic (ROC) curve analysis were performed for further statistical analysis as appropriate.ResultsSignificant differences were found in all td-dMRI-derived indexes among PAs, WTs, and MTs (all P < 0.05). Vin was the sole parameter with significant differences for all sub-group comparisons (PAs vs. WTs, P < 0.001; PAs vs. MTs, P = 0.031; WTs vs. MTs, P = 0.047). With Vin values of 0.267, 0.231, and 0.260 as threshold, respectively, optimal performance levels were obtained for differentiating PAs from WTs (area under the ROC curve [AUC]=0.932, sensitivity=0.917, and specificity=0.875), PAs from MTs (AUC=0.744, sensitivity=0.833, and specificity=0.714), and WTs from MTs (AUC=0.750, sensitivity=0.875, and specificity=0.714).ConclusionMicrostructural parameters derived from td-dMRI, especially Vin, might be promising imaging biomarkers for characterizing parotid gland tumors.

To investigate the characteristic ultrasonographic findings of adenoid cystic carcinoma (ACC) in major salivary glands and identify the value of polar vessel in color Doppler flow imaging (CDFI) for the diagnosis of ACC.

From January 2017 to December 2021, 76 patients with parotid and submandibular gland tumors, including 14 patients with ACC, as confirmed by surgery and histopathology, were enrolled. Their clinicopathologic information and ultrasound (US) features were recorded and analyzed. The performance of polar vessel in CDFI for differentiating ACC from non-ACC (benign tumors and mucoepidermoid carcinoma [MEC]) was analyzed.

ACC in the major salivary gland was more likely to be associated with pain symptoms (P = .027) and unclear borders and rough edges in grayscale US (P = .002, .015, respectively) than benign tumors. Compared to MEC, ACC tended to feature a homogeneous internal echo (P = .008). ACC of the major salivary gland had a significantly higher incidence of polar vessel sign than that of non-ACC (benign tumors and MEC) (P < .0001, .0001, respectively). The polar vessel sign showed good performance in distinguishing between ACC and non-ACC, with an area under the receiver operating characteristic curve of 0.857, a sensitivity of 71.4%, a specificity of 100%, and an accuracy of 94.7%. Positive predictive value and negative predictive value were calculated at 100% and 93.9%, respectively.

The US sign of polar vessel has high diagnostic efficiency, and it may have important potential for use as a new complementary sign for the diagnosis of ACC in major salivary glands.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors.

The measurements of apparent diffusion coefficient (ADC) with diffusion weighted magnetic resonance imaging (DW-MRI) is becoming a popular diagnostic and research tool for examination of parotid glands. However, there is little agreement between the reported ADC values of the parotid gland in published literature. In this review 43 studies on ADC measurement of the parotid glands were included. The analyses indicated several possible culprits of the observed ADC discrepancies. For example, DW-MRI examinations under gustatory stimulation gives higher ADC values compared to the unstimulated parotid gland (P=0.003). The diffusion weighting factors (b-values) can either increase (b-value <200 s/mm²) or decrease ADC values (b-values >1,000 s/mm²). The timing of follow-up DW-MRI after radiotherapy (RT) indicates correlation to the found ADC values (R² =0.39). Interestingly, the choice of regions of interest (ROI) appears not to affect the measurements of ADC (P=0.75). It can be concluded that there is a critical need for standardization of ADC measurement of the parotid glands to allow valid inter-study comparisons and eventually to reach consensus on the use of ADC as biomarker.

To develop and validate a triple-classification radiomics model for the preoperative differentiation of pleomorphic adenoma (PA), Warthin tumour (WT), and malignant salivary gland tumour (MSGT) based on diffusion-weighted imaging (DWI).

Data from 217 patients with histopathologically confirmed salivary gland tumours (100 PAs, 68 WTs, and 49 MSGTs) from January 2015 to March 2019 were analysed retrospectively and divided into a training set (n=173), and a validation set (n=44). A total of 396 radiomic features were extracted from the DWI of all patients. Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression were used to select radiomic features, which were then constructed using three classification models, namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN). The diagnostic performance of the radiomics model was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of the training and validation data sets.

The 20 most valuable features were investigated based on the LASSO regression. LR and SVM methods exhibited better diagnostic ability than KNN for multiclass classification. LR and SVM had the best performance and yielded the AUC values of 0.857 and 0.824, respectively, in the training data set and the AUC values of 0.932 and 0.912, respectively, in the validation data set of MSGT diagnosis.

DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.

Disorders affecting parotid gland represent a heterogeneous group comprising congenital, inflammatory and neoplastic diseases which show a focal or diffuse pattern of appearance. The differentiation of neoplastic from non-neoplastic conditions of parotid glands is pivotal for the diagnostic imaging. Frequently there is evidence of overlapping between the clinical and the imaging appearance of the various pathologies. The parotid gland is also often object of study with the combination of different techniques [ultrasound-computed tomography-magnetic resonance imaging (US-CT-MRI), ex.]. Compared to other dominant methods of medical imaging, US has several advantages providing images in real-time at lower cost, and without harmful use of ionizing radiation and of contrast enhancement. B-mode US, and the microvascular pattern color Doppler are usually used as first step evaluation of parotid lesions. Elastography and contrast-enhanced US (CEUS) has opened further possible perspectives to improve the differentiation between benign and malignant parotid lesions. The characterization of the parotid tumors plays a crucial role for their treatment planning and for the prediction of possible surgical complications. We present, here an updated review of the most recurrent pathologies of parotid gland focusing on the diagnostic power of multiparametric US including CEUS and ultrasound elastography (USE); limitations, advantages and the main key-points will be presented.

This study aimed to assess the diagnostic performance of diffusion-weighted imaging (DWI) for parotid gland malignancies.

Four databases (PubMed, the Cochrane Library, Embase, and Web of Science) were searched systematically and retrospectively by two researchers until May 18, 2020. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to pool the sensitivity and specificity data for the apparent diffusion coefficient (ADC). Summary receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated. The positive (LR+) and negative likelihood ratios (LR-) were also calculated. Subgroup and meta-regression analyses were performed to evaluate heterogeneity within studies.

Sixteen studies involving 1004 patients were included. The pooled sensitivity, specificity, and AUC for the ADC to distinguish malignant from begin parotid lesions were 89 %, 76 %, and 0.91, respectively. The LR + was 3.7 and LR- was 0.15, respectively. Subgroup analyses revealed that the applied cut-off b values and study size were sources of heterogeneity for the ADC. There were publication bias concerns.

Our meta-analysis suggests that the ADC value provides excellent sensitivity and moderate specificity for the diagnosis of malignant lesions in the parotid gland. However, substantial heterogeneity was found. Therefore, additional larger, prospective studies in combination with standard techniques focusing on parotid tumors should be conducted to determine the true performance of DWI for the differential diagnosis of parotid lesions.

To review, the surgical approaches available on diagnosing a patient with salivary gland malignancy in the infratemporal fossa (ITF). To comment on patient evaluation and method of treatment selection. To identify and report on patient outcome data and make recommendations on future needs.

There is a need to define the anatomic boundaries contents of the ITF, masticator space, parapharyngeal space (PPS), pterygopalatine fossa, ventral skull base, and paramedian skull base, as evidence from publications. The pathological subtypes identified mainly include adenoid cystic and mucoepidermoid carcinomas. The source of these tumours originates from primary disease in the sinonasal tract and nasopharynx superiorly, and the PPS/deep lobe of parotid inferiorly. Current surgical options available, in suitable selected patient, available in tertiary head and neck cancer hospitals, which have available facilities and staffing is the endoscopic endonasal approach. This approach offers patients a 'complete margin-free surgical excision', minimal complications, shorter hospital stay, and no delay with commencement of any adjuvant treatment compared with the traditional 'open transcutaneous' approach.

The current evidence specifically to the surgical management of salivary gland malignancy involving the ITF is sparse, with great difficult identifying treated patients and their details among a heterogeneous group of patients with many lesions. There is a need for patient data that have specific pathologic conditions to be amalgamated from such centers and publish on outcome events.

The majority of salivary gland tumors occur in the parotid glands. Characterization (ie, benign or malignant, and histological type), location (deep or superficial), and invasion into the neighboring tissues of parotid tumors determine preoperative treatment planning. MRI gives more information than other imaging methods about the internal structure, localization, and relationship with other tissues of parotid tumors. Functional MRI methods (diffusion-weighted imaging, dynamic contrast-enhanced MRI, perfusion-weighted MRI, MR spectroscopy, etc.) have been increasingly used recently to increase the power of radiologists to characterize the tumors. Although they increase the workload of radiologists, the combined use of functional MRI methods improves accuracy in the differentiation of the tumors. There are a wide range of studies in the literature dealing with the combined use of different functional imaging methods in combination with conventional sequences. The aim of the present review is to evaluate conventional and functional/advanced MR methods, as well as multiparametric MRI applications combining them in the diagnosis of parotid gland tumors. Evidence Level: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;52:11-32.

To evaluate the utility of radiomics analysis for differentiating benign and malignant epithelial salivary gland tumors on diffusion-weighted imaging (DWI).

A retrospective dataset involving 218 and 51 patients with histology-confirmed benign and malignant epithelial salivary gland tumors was used in this study. A total of 396 radiomic features were extracted from the DW images. Analysis of variance (ANOVA) and least-absolute shrinkage and selection operator regression (LASSO) were used to select optimal radiomic features. The selected features were used to build three classification models namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN) by using a five-fold cross validation strategy on the training dataset. The diagnostic performance of each classification model was quantified by receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) in the training and validation datasets.

Eight most valuable features were selected by LASSO. LR and SVM models yielded optimally diagnostic performance. In the training dataset, LR and SVM yielded AUC values of 0.886 and 0.893 via five-fold cross validation, respectively, while KNN model showed relatively lower AUC (0.796). In the testing dataset, a similar result was found, where AUC values for LR, SVM, and KNN were 0.876, 0.870, and 0.791, respectively.

Classification models based on optimally selected radiomics features computed from DW images present a promising predictive value in distinguishing benign and malignant epithelial salivary gland tumors and thus have potential to be used for preoperative auxiliary diagnosis.

BACKGROUND The aim of this study was to investigate the diagnostic value of diffusion-weighted imaging (DWI) in combination with susceptibility-weighted imaging (SWI) for differentiating benign parotid gland lesions from malignant ones. MATERIAL AND METHODS This retrospective study was approved by the Ethics Committee of our hospital. A total of 36 patients (26 benign cases and 10 malignant cases) were confirmed by surgical pathology. The apparent diffusion coefficient (ADC), normalized ADC (ADCNormalized), intratumoral susceptibility signals (ITSS), and morphological characteristics were analyzed with SPSS 19.0 software. RESULTS The mean ADC values of parotid gland lesions was not different between malignant and benign lesions (P=0.07), while the differences between ADCNormalized (P=0.026) and ITSS grading (P=0.014) were statistically significant. Logistic regression analysis identified use of ADCNormalized and ITSS as the only independent predictor of malignant lesions (odds ratio 0.038; 95% confidence interval 0.001~0.988; P=0.011) and (odds ratio 4.867; 95% confidence interval 1.442~16.423; P=0.049), respectively. The optimum threshold of the ADCNormalized values was -0.45%, ITSS grade was 2, the corresponding areas under the receiver operating characteristic curve (AUC) were 0.750 and 0.787 respectively, and the combination of the 2 was 0.846. CONCLUSIONS DWI integrated with SWI can significantly improve the diagnostic efficacy in distinguishing benign from malignant parotid lesions.