Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging for Differentiation Between Parotid Neoplasms

Time-dependent diffusion magnetic resonance imaging for the analysis of parotid gland tumors

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.

A nomogram model based on MRI for discriminating Warthin's tumor from pleomorphic adenomas: a retrospective observational study

To establish a nomogram model using MRI characteristics for differentiating Warthin's tumors (WT) from pleomorphic adenomas (PA) in parotid gland tumors, aiming to enhance management strategies. A retrospective observational study included patients with histopathologically confirmed WT or PA who underwent preoperative enhanced MRI from 2010 to 2023. Clinical and MRI data were analyzed to identify independent risk factors using multivariable logistic regression. A nomogram was constructed and internally validated using bootstrap resampling. 56 patients (32 WT, 24 PA) were analyzed. Significant differences in gender, age, degree of enhancement, and enhancement pattern were identified. The nomogram based on these factors demonstrated high predictive accuracy with an area under the ROC curve of 0.9909. The nomogram model presents a standardized quantitative method to differentiate WT from PA based on MRI characteristics, potentially improving diagnostic accuracy and patient management in parotid gland tumor diagnosis.

Comparative Diagnostic Accuracy of Ultrasound, MRI, and Fine-Needle Aspiration Biopsy in the Preoperative Evaluation of Parotid Gland Tumors

Background: The objective of this study was to compare the value of ultrasound (US), magnetic resonance imaging (MRI), and US-guided fine-needle aspiration biopsy (FNAB) in the preoperative evaluation of parotid tumors. Methods: A three-year prospective study, including 35 patients, was conducted. Preoperative ultrasound, MRI, and US-guided FNAB were performed on each patient, after which an imaging and cytological diagnosis was obtained. Each patient underwent surgical treatment. The imaging and cytological diagnoses were compared with the histopathological reports. Results: Ultrasound and MRI showed the same diagnostic performance in discriminating benign from malignant parotid tumors: sensitivity-80%, specificity-97%, and accuracy-94%. In this regard, FNAB registered a sensitivity, specificity, and accuracy of 100%, 97%, and 97%, respectively. US, MRI, and FNAB were recorded as having high diagnostic accuracy in the detection of pleomorphic adenoma and Warthin tumors. Conclusions: Ultrasound and US-guided FNAB allow for the preoperative differential diagnosis of parotid tumors located in the superficial lobe. When US and FNAB results are inconclusive, MRI becomes mandatory.

Utility of diffusion tensor imaging in differentiating benign from malignant thyroid nodules

Purpose: To assess diffusion tensor imaging (DTI) in differentiating benign from malignant thyroid nodules. Methods: A retrospective analysis was done on 55 patients with thyroid nodules who had undergone DTI. The fraction anisotropy (FA) and mean diffusivity (MD) of the thyroid nodules were measured using region of interest (ROI) by two observers. The final diagnosis was malignant and benign, as proved by pathological examination. Results: The mean MD of benign thyroid nodules (1.84 ± 0.42 and 1.90 ± 0.37 × 10-3mm2/s) was significantly higher (p < .001) than malignant nodules (0.95 ± 0.46 and 0.97 ± 0.41 × 10-3mm2/s) as scored by both observers. The cut-off values of 1.45 and 1.50 × 10-3mm2/s were used to differentiate malignant from benign thyroid nodules with the areas under the curve (AUC) of 0.926 and 0.937, respectively. The mean FA of benign thyroid nodules (0.23 ± 0.07 and 0.24 ± 0.08) was significantly lower (p < .001) than malignant nodules (0.48 ± 0.21 and 0.49 ± 0.18). The FA cut-off value of ≤0.32 and 0.33 was used for differentiating malignant from benign thyroid nodules with an AUC of 0.877 and 0.881, respectively. A combination of MD and FA values was used to differentiate benign from malignant thyroid nodules with an AUC of 0.932 and an accuracy of 87%. There was an excellent agreement between both observers for FA and MD (K = 0.939, 0.929). Conclusion: The DTI is a non-invasive, non-contrast imaging tool that can differentiate benign from malignant thyroid nodules.

Discriminating atypical parotid carcinoma and pleomorphic adenoma utilizing extracellular volume fraction and arterial enhancement fraction derived from contrast-enhanced CT imaging: A multicenter study

OBJECTIVES

To investigate the added value of extracellular volume fraction (ECV) and arterial enhancement fraction (AEF) derived from enhanced CT to conventional image and clinical features for differentiating between pleomorphic adenoma (PA) and atypical parotid adenocarcinoma (PCA) pre-operation.

METHODS

From January 2010 to October 2023, a total of 187 cases of parotid tumors were recruited, and divided into training cohort (102 PAs and 51 PCAs) and testing cohort (24 PAs and 10 atypical PCAs). Clinical and CT image features of tumor were assessed. Both enhanced CT-derived ECV and AEF were calculated. Univariate analysis identified variables with statistically significant differences between the two subgroups in the training cohort. Multivariate logistic regression analysis with the forward variable selection method was used to build four models (clinical model, clinical model+ECV, clinical model+AEF, and combined model). Diagnostic performances were evaluated using receiver operating characteristic (ROC) curve analyses. Delong's test compared model differences, and calibration curve and decision curve analysis (DCA) assessed calibration and clinical application.

RESULTS

Age and boundary were chosen to build clinical model, and to construct its ROC curve. Amalgamating the clinical model, ECV, and AEF to establish a combined model demonstrated superior diagnostic effectiveness compared to the clinical model in both the training and test cohorts (AUC = 0.888, 0.867). There was a significant statistical difference between the combined model and the clinical model in the training cohort (p = 0.0145).

CONCLUSIONS

ECV and AEF are helpful in differentiating PA and atypical PCA, and integrating clinical and CT image features can further improve the diagnostic performance.

Value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging in differentiating parotid gland tumors

PURPOSE

This study aimed to explore the value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging (RESOLVE-DWI) for the differential diagnosis of parotid gland tumors.

METHODS

A total of 128 patients with histopathologically confirmed parotid gland tumors [86 benign tumors (BTs) and 42 malignant tumors (MTs)] were retrospectively recruited. BTs were further divided into pleomorphic adenomas (PAs, n = 57) and Warthin's tumors (WTs, n = 15). MRI examinations were performed before and after contrast injection to measure the longitudinal relaxation time (T1) value (T1p and T1e, respectively) and the apparent diffusion coefficient (ADC) value of the parotid gland tumors. The reduction in T1 (T1d) values and the percentage of T1 reduction (T1d%) were calculated.

RESULTS

The T1d and ADC values of the BTs were considerably higher than those of the MTs (all P <.05). The area under the curve (AUC) of the T1d and ADC values for differentiating between BTs and MTs of the parotid was 0.618 and 0.804, respectively (all P <.05). The AUC of the T1p, T1d, T1d%, and ADC values for differentiating between PAs and WTs was 0.926, 0.945, 0.925, and 0.996, respectively (all P >.05). The ADC and T1d% + ADC values performed better in differentiating between PAs and MTs than the T1p, T1d, and T1d% (AUC values: 0.902, 0.909, 0.660, 0.726, and 0.736, respectively). The T1p, T1d, T1d%, and T1d% + T1p values all had high diagnosis efficacy in differentiating WTs from MTs (AUC values: 0.865, 0.890, 0.852, and 0.897, respectively, all P >.05).

CONCLUSION

T1 mapping and RESOLVE-DWI can be used to differentiate parotid gland tumors quantitatively and can be complementary to each other.

The Value of Multiparametric Magnetic Resonance Imaging in the Preoperative Differential Diagnosis of Parotid Gland Tumors

BACKGROUND

The aim of the present study was to determine the value of multiparametric MRI in the preoperative differential diagnosis of parotid tumors, which is essential for therapeutic strategy selection.

METHODS

A three-year prospective study was conducted with 65 patients. Each patient was investigated preoperatively with multiparametric MRI and surgical excision of the tumor was performed. The preoperative imaging diagnosis was compared with the histopathological report. Several MRI parameters were analyzed, including T1 and T2 weighted image (WI), apparent diffusion coefficient (ADC), time to peak (TTP), and the time intensity curve (TIC).

RESULTS

In the differential diagnosis of benign from malignant tumors, T2WI and ADC showed statistically significant differences. Multiparametric MRI had a sensitivity, specificity, and accuracy of 81.8%, 88.6% and 92.3%, respectively. All of the studied parameters (T1, T2, TIC, TTP, ADC) were significantly different in the comparison between pleomorphic adenomas and Warthin tumors. With reference to the scope of this study, the conjunction of multiparametric and conventional MRI demonstrated a sensitivity, specificity, and accuracy of 94.1%, 100%, and 97.8%, respectively.

CONCLUSIONS

Morphological analysis using conventional MRI combined with diffusion-weighted imaging (DW) and dynamic contrast-enhanced (DCE) multiparametric MRI improved the preoperative differential diagnosis of parotid gland tumors.

A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences

OBJECTIVE

To design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings.

METHODS

First, multimodal MRI sequences were collected from 266 patients with parotid neoplasms, and an artificial intelligence (AI)-based deep learning model was designed from scratch, combining the image classification network of Resnet and the Transformer network of Natural language processing. Second, the effectiveness of the deep learning model was improved through the multi-modality fusion of MRI sequences, and the fusion strategy of various MRI sequences was optimized. In addition, we compared the effectiveness of the model in the parotid neoplasm classification with experienced radiologists.

RESULTS

The deep learning model delivered reliable outcomes in differentiating benign and malignant parotid neoplasms. The model, which was trained by the fusion of T2-weighted, postcontrast T1-weighted, and diffusion-weighted imaging (b = 1000 s/mm² ), produced the best result, with an accuracy score of 0.85, an area under the receiver operator characteristic (ROC) curve of 0.96, a sensitivity score of 0.90, and a specificity score of 0.84. In addition, the multi-modal paradigm exhibited reliable outcomes in diagnosing the pleomorphic adenoma and the Warthin tumor, but not in the identification of the basal cell adenoma.

CONCLUSION

An accurate and efficient AI based classification model was produced to classify parotid neoplasms, resulting from the fusion of multimodal MRI sequences. The effectiveness certainly outperformed the model with single MRI images or single MRI sequences as input, and potentially, experienced radiologists.

LEVEL OF EVIDENCE

3 Laryngoscope, 133:327-335, 2023.

Discriminating between benign and malignant salivary gland tumors using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla

PURPOSE

The purpose of this study was to retrospectively evaluate the diagnostic performances of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for discriminating between benign and malignant salivary gland tumors (SGTs).

MATERIALS AND METHODS

Sixty-seven patients with 71 SGTs who underwent MRI examination at 3 Tesla were included. There were 34 men and 37 women with a mean age of 57 ± 17 (SD) years (age range: 20-90 years). SGTs included 21 malignant tumors (MTs) and 50 benign SGTs (33 pleomorphic adenomas [PAs] and 17 Warthin's tumors [WTs]). For each SGT, DWI and IVIM parameters, mean, skewness, and kurtosis of apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion volume fraction (f) were calculated and further compared between SGTs using univariable analysis. Areas under the curves (AUC) of receiver operating characteristic of significant parameters were compared using the Delong test.

RESULTS

Significant differences in ADC_mean, D_mean and D*_mean were found between SGTs (P < 0.001). The highest AUC values were obtained for ADC_mean (0.949) for identifying PAs and D*_mean (0.985) for identifying WTs and skewness and kurtosis did not outperform mean. To discriminate benign from malignant SGTs with thresholds set to maximize Youden index, IVIM and DWI produced accuracies of 85.9% (61/71; 95% CI: 75.6-93.0) and 77.5% (55/71; 95% CI: 66.0-86.5) but misdiagnosed MTs as benign in 28.6% (6/21) and 61.9% (13/21) of SGTs, respectively. After maximizing specificity to 100% for benign SGTs, the accuracies of IVIM and DWI decreased to 76.1% (54/71; 95% CI: 64.5-85.4) and 64.8% (46/71; 95% CI: 52.5-75.8) but no MTs were misdiagnosed as benign. IVIM and DWI correctly diagnosed 66.0% (33/50) and 50.0% (25/50) of benign SGTs and 46.5% (33/71) and 35.2% (25/71) of all SGTs, respectively.

CONCLUSION

IVIM is more accurate than DWI for discriminating between benign and malignant SGTs because of its advantage in detecting WTs. Thresholds set by maximizing specificity for benign SGTs may be advantageous in a clinical setting.

Diagnostic efficacy of diffusion-weighted imaging and semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging in salivary gland tumors

BACKGROUND

Increased use of functional magnetic resonance imaging (MRI) methods such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI consisting of sequential contrast series, allows us to obtain more information on the microstructure, cellularity, interstitial distance, and vascularity of tumors, which has increased the discrimination power for benign and malignant salivary gland tumors (SGTs). In the last few years, quantitative DCE MRI data containing T1 perfusion parameters (K_trans, K_ep and V_e), were reported to contribute to the differentiation of benign or malignant subtypes in SGTs.

AIM

To evaluate the diagnostic efficacy of DWI and semiquantitative and quantitative perfusion MRI parameters in SGTs.

METHODS

Diffusion MRI [apparent diffusion coefficient (ADC) value] with a 1.5 T MR machine, semiquantitative perfusion MRI [time intensity curve (TIC) pattern], and quantitative perfusion MRI examinations (K_trans, K_ep and V_e) of 73 tumors in 67 patients with histopathological diagnosis performed from 2017 to 2021 were retrospectively evaluated. In the ADC value and semiquantitative perfusion MRI measurements, cystic components of the tumors were not considered, and the region of interest (ROI) was manually placed through the widest axial section of the tumor. TIC patterns were divided into four groups: Type A = T_peak > 120 s; type B = T_peak ≤ 120 s, washout ratio (WR) ≥ 30%; type C = T_peak ≤ 120 s, WR < 30%; and type D = flat TIC. For the quantitative perfusion MRI analysis, a 3D ROI was placed in the largest solid component of the tumor, and the K_trans, K_ep and V_e values were automatically generated.

RESULTS

The majority of SGTs were located in the parotid glands (86.3%). Of all the SGTs, 68.5% were benign and 31.5% were malignant. Significant differences were found for ADC values among pleomorphic adenomas (PMAs), Warthin's tumors (WTs), and malignant tumors (MTs) (P < 0.001). PMAs had type A and WTs had type B TIC pattern while the vast majority of MTs and other benign tumors (OBTs) (54.5% and 45.5%, respectively) displayed type C TIC pattern. PMAs showed no washout, while the highest mean WR was observed in WTs (59% ± 11%). K_trans values of PMAs, WTs, OBTs, and MTs were not significantly different. K_ep values of PMAs and WTs were significantly different from those of OBTs and MTs. Mean V_e value of WTs was significantly different from those of PMAs, OBTs, and MTs (P < 0.001).

CONCLUSION

The use of quantitative DCE parameters along with diffusion MRI and semiquantitative contrast-enhanced MRI in SGTs could improve the diagnostic accuracy.

Advanced magnetic resonance imaging findings in salivary gland tumors

Salivary gland tumors (SGTs) make up a small portion (approximately 5%) of all head and neck tumors. Most of them are located in the parotid glands, while they are less frequently located in the submandibular glands, minor salivary glands or sublingual gland. The incidence of malignant or benign tumors (BTs) in the salivary glands varies according to the salivary gland from which they originate. While most of those detected in the parotid gland tend to be benign, the incidence of malignancy increases in other glands. The use of magnetic resonance imaging (MRI) in the diagnosis of SGTs is increasing every day. While conventional sequences provide sufficient data on the presence, localization, extent and number of the tumor, they are insufficient for tumor specification. With the widespread use of advanced techniques such as diffusion-weighted imaging, semi-quantitative and quantitative perfusion MRI, studies and data have been published on the differentiation of malignant or BTs and the specificity of their subtypes. With diffusion MRI, differentiation can be made by utilizing the cellularity and microstructural properties of tumors. For example, SGTs such as high cellular Warthin’s tumor (WT) or lymphoma on diffusion MRI have been reported to have significantly lower apparent diffusion values than other tumors. Contrast agent uptake and wash-out levels of tumors can be detected with semi-quantitative perfusion MRI. For example, it is reported that almost all of the pleomorphic adenomas show an increasing enhancement time intensity curve and do not wash-out. On quantitative perfusion MRI studies using perfusion parameters such as Ktrans, Kep, and Ve, it is reported that WTs can show higher Kep and lower Ve values than other tumors. In this study, the contribution of advanced MRI to the diagnosis and differential diagnosis of SGTs will be reviewed.

Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions

The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions (“leave-one-out CV” accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.

Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study

OBJECTIVES

To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors.

METHODS

In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists.

RESULTS

Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under the curve (AUC) for all three steps in the training cohort (0.857, 0.882, and 0.908, respectively), and for the first step in the test cohort (0.826), but produced slightly lower AUCs than SVM in the latter two steps in the test cohort (0.817 vs. 0.833, and 0.789 vs. 0.821, respectively). The total accuracies of XGBoost and SVM in the confusion matrices (70.8% and 59.6%) outperformed those of DT and the radiologist (46.1% and 49.2%).

CONCLUSION

This study demonstrated that machine learning models based on morphological MRI radiomics might be an assistive tool for parotid tumor classification, especially for preliminary screening in absence of more advanced scanning sequences, such as DWI.

KEY POINTS

• Machine learning algorithms combined with morphological MRI radiomics could be useful in the preliminary classification of parotid tumors. • XGBoost algorithm performed better than SVM and DT in subtype differentiation of parotid tumors, while DT seemed to have a poor validation performance. • Using morphological MRI only, the XGBoost and SVM algorithms outperformed radiologists in the four-type classification task for parotid tumors, thus making these models a useful assistant diagnostic tool in clinical practice.

Apparent Diffusion Coefficient Map–Based Radiomics Features for Differential Diagnosis of Pleomorphic Adenomas and Warthin Tumors From Malignant Tumors

Purpose

The magnetic resonance imaging (MRI) findings may overlap due to the complex content of parotid gland tumors and the differentiation level of malignant tumor (MT); consequently, patients may undergo diagnostic lobectomy. This study assessed whether radiomics features could noninvasively stratify parotid gland tumors accurately based on apparent diffusion coefficient (ADC) maps.

Methods

This study examined diffusion-weighted imaging (DWI) obtained with echo planar imaging sequences. Eighty-eight benign tumors (BTs) [54 pleomorphic adenomas (PAs) and 34 Warthin tumors (WTs)] and 42 MTs of the parotid gland were enrolled. Each case was randomly divided into training and testing cohorts at a ratio of 7:3 and then was compared with each other, respectively. ADC maps were digitally transferred to ITK SNAP (www.itksnap.org). The region of interest (ROI) was manually drawn around the whole tumor margin on each slice of ADC maps. After feature extraction, the Synthetic Minority Oversampling TEchnique (SMOTE) was used to remove the unbalance of the training dataset. Then, we applied the normalization process to the feature matrix. To reduce the similarity of each feature pair, we calculated the Pearson correlation coefficient (PCC) value of each feature pair and eliminated one of them if the PCC value was larger than 0.95. Then, recursive feature elimination (RFE) was used to process feature selection. After that, we used linear discriminant analysis (LDA) as the classifier. Receiver operating characteristic (ROC) curve analysis was used to evaluate the diagnostic performance of the ADC.

Results

The LDA model based on 13, 8, 3, and 1 features can get the highest area under the ROC curve (AUC) in differentiating BT from MT, PA from WT, PA from MT, and WT from MT on the validation dataset, respectively. Accordingly, the AUC and the accuracy of the model on the testing set achieve 0.7637 and 73.17%, 0.925 and 92.31%, 0.8077 and 75.86%, and 0.5923 and 65.22%, respectively.

Conclusion

The ADC-based radiomics features may be used to assist clinicians for differential diagnosis of PA and WT from MTs.

Feasibility of Intravoxel Incoherent Motion (IVIM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) in Differentiation of Benign Parotid Gland Tumors

Aim: The aim of this prospective study is to identify quantitative intravoxel incoherent motion and dynamic contrast-enhanced magnetic resonance imaging parameters of the most frequent benign parotid tumors, compare their utility and diagnostic accuracy. Methods: The study group consisted of 52 patients with 64 histopathologically confirmed parotid focal lesions. Parametric maps representing apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (FP) and transfer constant (Ktrans), reflux constant (Kep), extra-vascular extra-cellular volume fraction (Ve), and initial area under curve in 60 s (iAUC) have been obtained from multiparametric MRI. Results: Statistically significant (p < 0.001) inter-group differences were found between pleomorphic adenomas (PA) and Warthin tumors (WT) in all tested parameters but iAUC. Receiver operating characteristic curves were constructed to determine the optimal cut-off levels of the most significant parameters allowing differentiation between WT and PA. The Area Under the Curve (AUC) values and thresholds were for ADC: 0.931 and 1.05, D: 0.896 and 0.9, Kep: 0.964 and 1.1 and Ve: 0.939 and 0.299, respectively. Lesions presenting with a combination of ADC, D, and Ve values superior to the cut-off and Kep values inferior to the cut-off are classified as pleomorphic adenomas. Lesions presenting with combination of ADC, D, and Ve values inferior to the cut-off and Kep values superior to the cut-off are classified as Warthin tumors. Conclusions: DWI, IVIM and quantitative analysis of DCE-MRI derived parameters demonstrated distinctive features of PAs and WT and as such they seem feasible in differentiation of benign parotid gland tumors.

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.

Value of dynamic contrast enhanced MRI in differential diagnostics of Warthin tumors and parotid malignancies

To define an algorithm for differential diagnostics of parotid malignancies and Warthin tumors (WTs) based on dynamic contrast enhanced MRI (DCE-MRI). 55 patients with parotid tumors treated surgically were analyzed. Of which, 19 had parotid malignancy and 36 had WTs confirmed with postoperative histopathological examination. Accuracy of DCE-MRI parameters (T_peak and WR) was compared with the histopathological diagnosis. ROC analysis was performed to determine sensitivity and specificity of DCE-MRI with various T_peak and WR cut-off values. WT showed significantly lower median T_peak and higher median WR than malignant lesions. The cut-off values for T_peak and WR providing maximum sensitivity (84.2%) and specificity (86.1%) for malignant tumors were T_peak > 60 s and WR ≤ 30%. Different diagnostic algorithm, i.e., lower cut-off value for T_peak (T_peak = 60 s), increases sensitivity of DCE-MRI in differentiating parotid malignancies from WTs. However, WR > 30% seems to be a key diagnostic criterion for benign lesions. Precise and reliable preoperative diagnostics of parotid tumors aids in careful surgical planning, thereby assisting in achieving sufficient surgical resection margins and facial nerve preservation.

Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors

PURPOSE

To evaluate the ability of quantitative dynamic contrast-enhanced (DCE)-MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE-DWI) in differentiating parotid tumors (PTs) with different histological types.

METHODS

In this retrospective study, 123 patients with 145 histologically proven PTs who underwent both RESOLVE-DWI and DCE-MRI were enrolled including 51 pleomorphic adenomas (PAs), 52 Warthin's tumors (WTs), 27 other benign neoplasms (OBNs), and 15 malignant tumors (MTs). Quantitative parameters of DCE-MRI (K^trans, K_ep, and V_e) and the apparent diffusion coefficient (ADC) of lesions were calculated and analyzed. Kruskal-Wallis tests with Dunn-Bonferroni correction, logistic regression analyses, and receiver operating characteristic curve were used for statistical analyses.

RESULTS

PAs exhibited a lowest K^trans among these four PTs. WTs demonstrated the highest K_ep and lowest V_e values. WTs and MTs showed lower ADC_min values than PAs and OBNs. The combination of K_ep and V_e provided 98.1% sensitivity, 85% specificity, and 98.7% accuracy for differentiating WTs from the other three PTs. The ADC_min cutoff value of ≤ 0.826 yielded 80.0% sensitivity, 92.3% specificity, and 90.3% accuracy for the differentiation of MTs from PAs and OBNs. K^trans with a cutoff value of ≤ 0.185 achieved a sensitivity, specificity, and accuracy of 84.3, 70.4, and 79.5%, respectively, for discriminating PAs from OBNs.

CONCLUSION

The combination of quantitative DCE-MRI and RESOLVE-DWI is beneficial for characterizing four histological types of PTs.

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

Objective

To investigate the value of the signal intensity on T2-weighted magnetic resonance (MR) imaging using quantitative analysis in the differentiation of parotid tumors.

Materials and Methods

MR data of 80 pleomorphic adenomas (PAs), 68 Warthin tumors (WTs), and 34 malignant tumors (MTs) confirmed by surgery and histology were retrospectively analyzed. The signal intensities of tumor, normal parotid gland, spinal cord, and buccal subcutaneous fat were measured, and the signal intensity ratios (SIRs) between the tumor and the three references were calculated. Receiver operating characteristic curve was used to determine the optimal threshold and diagnostic efficiency of SIR for differentiating PAs, WTs, and MTs.

Results

The area under the curve (AUC) of tumor to parotid gland SIR (SIR_P), tumor to spinal cord SIR (SIR_C), and tumor to buccal subcutaneous fat SIR (SIR_F) for differentiating PAs and WTs was 0.922, 0.918, and 0.934, respectively. The sensitivity and specificity at an optimal SIR threshold were 86.3% and 91.2%, 80.0% and 97.1%, and 85.0% and 94.1%, respectively. The AUC of SIR_P, SIR_C, and SIR_F for distinguishing PAs from MTs was 0.793, 0.802, and 0.774, respectively. The sensitivity and specificity at an optimal SIR threshold was 86.3% and 61.8%, 80.0% and 73.5%, and 82.5% and 73.5%, respectively. The AUC of SIR_P, SIR_C, and SIR_F for distinguishing WTs from MTs was 0.716, 0.709, and 0.759, respectively. The sensitivity and specificity at an optimal SIR threshold were 61.8% and 82.4%, 55.9% and 82.4%, and 64.7% and 86.8%, respectively.

Conclusion

SIR_P, SIR_C, and SIR_F on T2-weighted MR images had high diagnostic efficiency for differentiating between PAs and WTs, while SIR_P and SIR_C for differentiating between PAs and MTs, and SIR_F for differentiating between WTs and MTs had relatively high diagnostic efficiency.

Deep Learning for Differentiating Benign From Malignant Parotid Lesions on MR Images

Purpose/Objectives(s)

Salivary gland tumors are a rare, histologically heterogeneous group of tumors. The distinction between malignant and benign tumors of the parotid gland is clinically important. This study aims to develop and evaluate a deep-learning network for diagnosing parotid gland tumors via the deep learning of MR images.

Materials/Methods

Two hundred thirty-three patients with parotid gland tumors were enrolled in this study. Histology results were available for all tumors. All patients underwent MRI scans, including T1-weighted, CE-T1-weighted and T2-weighted imaging series. The parotid glands and tumors were segmented on all three MR image series by a radiologist with 10 years of clinical experience. A total of 3791 parotid gland region images were cropped from the MR images. A label (pleomorphic adenoma and Warthin tumor, malignant tumor or free of tumor), which was based on histology results, was assigned to each image. To train the deep-learning model, these data were randomly divided into a training dataset (90%, comprising 3035 MR images from 212 patients: 714 pleomorphic adenoma images, 558 Warthin tumor images, 861 malignant tumor images, and 902 images free of tumor) and a validation dataset (10%, comprising 275 images from 21 patients: 57 pleomorphic adenoma images, 36 Warthin tumor images, 93 malignant tumor images, and 89 images free of tumor). A modified ResNet model was developed to classify these images. The input images were resized to 224x224 pixels, including four channels (T1-weighted tumor images only, T2-weighted tumor images only, CE-T1-weighted tumor images only and parotid gland images). Random image flipping and contrast adjustment were used for data enhancement. The model was trained for 1200 epochs with a learning rate of 1e-6, and the Adam optimizer was implemented. It took approximately 2 hours to complete the whole training procedure. The whole program was developed with PyTorch (version 1.2).

Results

The model accuracy with the training dataset was 92.94% (95% CI [0.91, 0.93]). The micro-AUC was 0.98. The experimental results showed that the accuracy of the final algorithm in the diagnosis and staging of parotid cancer was 82.18% (95% CI [0.77, 0.86]). The micro-AUC was 0.93.

Conclusion

The proposed model may be used to assist clinicians in the diagnosis of parotid tumors. However, future larger-scale multicenter studies are required for full validation.

Differentiation of Benign and Malignant Parotid Gland Tumors with MRI and Diffusion Weighted Imaging

Objective

This study investigated the effectivity of Magnetic Resonance Imaging (MRI) findings and Apparent Diffusion Coefficient (ADC) value in evaluating parotid gland tumors (PGTs), and aimed to reduce the biopsy procedure before surgery.

Methods

This retrospective study included 54 PGTs of 42 patients' (24 female, 18 male, mean age; 51.4±15.9). All of the patients had an MRI, and histopathologic diagnosis. The signal intensity [T1 and T2 Weighted (W), T1W after intravenous contrast agent injection] and mean ADC values of the PGTs were measured. Also contrast enhancement pattern (homogenous, heterogeneous, peripheral or none), margin features (well or ill-defined), sizes, location (superficial lobe/deeplobe/both), perineural spread, presence of lymphadenopathy, and extension to adjacent structures were noted.

Results

The distribution of PGTs was; 21 pleomorphic adenomas, 18 Warthin tumors, 2 lymph nodes, 2 mucoepidermoid carcinomas, 5 adenoid cystic carcinoma, 1 basal cell carcinoma,2 metastases and 2 lymphomas; (13 malignant and 41 benign lesions). Morphologic parameters; ill-defined margin, perineural spread, lymphadenopathy, and extension to adjacent structures were found to be significantly associated with malign lesions (p<0.01). There was a significant difference between ADC values of malignant and benign PGTs (p<0.05). Also ADC values and T2 signal intensity was significantly lower in Warthin tumors rather than pleomorphic adenomas (p<0.05).

Conclusions

Mean ADC values when considered with morphological features may be accessible methods to distinguish benign and malignant PGTs, also ADC values and T2 signal intensity may be useful for differentiating pleomorphic adenomas from Warthin tumors, thereby reducing the number of biopsies and thus complications.

The current value of quantitative shear wave sonoelastography in parotid gland tumors

Background

The preoperative differentiation between salivary gland tumor entities using computed tomography, magnetic resonance imaging (MRI) and ultrasound (US) is still limited. Biopsies are often regarded as indispensable for properly characterizing these various lesions. The aim of this study was to analyze the value of acoustic radiation force impulse (ARFI) sonoelastography as an US differentiation tool when examining parotid gland (PG) lesions.

Methods

We included 104 patients with PG masses in this study, employing two different US devices using quantitative ARFI-sonoelastography (Siemens Acuson-S3000, n=59; Siemens Acuson-Sequoia, n=45). The ability of sonoelastographic measurements to differentiate between different neoplasms was compared and analyzed for both US machines.

Results

Quantitative shear wave sonoelastography is limited in its ability to reliably differentiate between tumor entities of the PG as a stand-alone parameter. Measurement results were unsystematically distributed and not transferable between the two US devices. A significant differentiation of benign and malignant lesions was not possible with either US machine (S3000: P=0.770, Sequoia: P=0.382). A differentiation between pleomorphic adenomas (PA) and Warthin tumors was only possible with the Acuson S3000 system (P=0.001, Spearman-Rho =0.492, sensitivity 73.9%, specificity 65.0%).

Conclusions

A reliable identification and differentiation of PG tumors as well as clinical treatment decisions cannot be made with the sole use of ARFI-sonoelastography. The results emphasize the device-dependence and high error-proneness of this US technique when examining lesions of the PG.

Diagnostic value of maximum signal intensity on T1-weighted MRI images for differentiating parotid gland tumours along with pathological correlation

AIM

To investigate the efficacy of the maximum signal intensity of tumour on T1-weighted magnetic resonance imaging (MRI) images for differentiating Warthin's tumours (WTs) from pleomorphic adenomas (PAs) and malignant tumours (MTs).

MATERIALS AND METHODS

One hundred and fifty-four histopathologically confirmed parotid tumours, including 76 PAs, 45 WTs, and 33 MTs, were analysed. MRI results were compared with pathological findings. The maximum signal intensity of tumour and the average signal intensity of spinal cord were measured on T1-weighted images, then the tumour-to-spinal cord signal intensity ratio (T1-max-SIR) was calculated. The distribution of T1-max-SIRs among the three groups of tumours was analysed using the Mann-Whitney U-test. Receiver operating characteristic curves were generated to assess the ability of T1-max-SIRs to differentiate parotid tumours. In addition, the interobserver agreement between readers was assessed using interclass correlation coefficient (ICC).

RESULTS

T1-max-SIRs were higher in WTs than in PAs (p<0.001) and MTs (p<0.001), and no significant difference was found between PAs and MTs (p=0.151). The area under the curve (AUC) of T1-max-SIRs for differentiating WTs from PAs was 0.901, with a sensitivity of 91.1% and a specificity of 82.9%. The AUC of T1-max-SIRs for differentiating WTs from MTs was 0.851, with a sensitivity of 88.9% and a specificity of 78.8%. Readers had excellent interobserver agreement on T1-max-SIRs (ICC = 0.989; 95% confidence interval, 0.985-0.992).

CONCLUSIONS

T1-max-SIRs can be useful for differentiating WTs from PAs and MTs with high diagnostic efficiency.

Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

BACKGROUND AND PURPOSE

Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY).

METHODS

Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient.

RESULTS

The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively.

CONCLUSIONS

Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.

Cross-sectional imaging and cytologic investigations in the preoperative diagnosis of parotid gland tumors - An updated literature review

An accurate preoperative diagnosis of parotid tumors is essential for the selection and planning of surgical treatment. Various modern cross-sectional imaging and cytologic investigations can support the differential diagnosis of parotid tumors. The aim of this study was to achieve a comprehensive and updated review of modern imaging and cytologic investigations used in parotid tumor diagnosis, based on the latest literature data. This literature review could serve as a guide for clinicians in selecting different types of investigations for the preoperative differential diagnosis of parotid tumors. Magnetic resonance imaging (MRI) with its dynamic and advanced sequences is the first-line imaging investigation used in differentiating parotid tumors. Computed tomography (CT) and positron emission tomography (PET)-CT provide limited indications in differentiating parotid tumors. Fine needle aspiration biopsy and core needle biopsy can contribute with satisfactory results to the cytological diagnosis of parotid tumors. Dynamic MRI with its dynamic contrast-enhanced and diffusion-weighted sequences provides the best accuracy for the preoperative differential diagnosis of parotid tumors. CT allows the best evaluation of bone invasion, being useful when MRI cannot be performed, and PET-CT has value in the follow-up of cancer patients. The dual cytological and imaging approach is the safest method for an accurate differential diagnosis of parotid tumors.

RETRACTED ARTICLE: The diagnostic value of combined dynamic contrast-enhanced MRI (DCE-MRI) and diffusion-weighted imaging (DWI) in characterization of parotid gland tumors

Background

MRI is considered to be the imaging modality of choice in preoperative diagnosis of parotid gland tumors and differentiating benign from malignant ones. Recently, functional MR imaging sequences including dynamic contrast-enhanced magnetic resonance imaging (DCE- MRI) and diffusion-weighted imaging (DWI) have significantly contributed to the diagnosis of head and neck masses. The purpose of this study was to evaluate the diagnostic value of combined DCE-MRI and DWI in characterization of parotid gland tumors.

Results

There was significant difference between benign and malignant parotid gland tumors as regard the type of time intensity curve (TIC) (P < 0.001). There was significant difference between pleomorphic adenoma (PMA) and malignant parotid gland tumors (MT) as regard mean ADC value (P = 0.046) and TTP (P = 0.002). There was no significant difference between Warthin’s tumor (WT) and malignant parotid gland tumors as regard the ADC value and TTP (P > 0.5); on the other hand, WT usually have high WR when compared with MT (P = 0.004). Combined use of DCE-MRI and DWI had 100% sensitivity, 90.5% specificity, and 93.3% accuracy in differentiating benign from malignant parotid gland tumors.

Conclusion

Combined use of DCE-MRI and DWI could result in high sensitivity, specificity, and diagnostic accuracy in characterization of parotid gland tumors.

Performance of diffusion-weighted imaging for the diagnosis of parotid gland malignancies: A meta-analysis

PURPOSE

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

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Multiparametric magnetic resonance imaging of parotid tumors: A systematic review

PURPOSE

The purpose of this systematic review was to provide an overview of the contribution of multiparametric magnetic resonance imaging (MRI) in the diagnosis of parotid tumors (PT) and recommendations based on current evidences.

MATERIAL AND METHODS

We performed a retrospective systematic search of PubMed, EMBASE, and Cochrane Library databases from inception to January 2020, using the keywords "magnetic resonance imaging" and "salivary gland neoplasms".

RESULTS

The initial search returned 2345 references and 90 were deemed relevant for this study. A total of 54 studies (60%) reported the use of diffusion-weighted imaging (DWI) and 28 studies (31%) the use of dynamic contrast-enhanced (DCE) imaging. Specific morphologic signs of frequent benign PT and suggestive signs of malignancy on conventional sequences were reported in 37 studies (41%). DWI showed significant differences in apparent diffusion coefficient (ADC) values between benign and malignant PT, and especially between pleomorphic adenomas and malignant PT, with cut-off ADC values between 1.267×10^-3mm²/s and 1.60×10^-3mm²/s. Perfusion curves obtained with DCE imaging allowed differentiating among pleomorphic adenomas, Warthin's tumors, malignant PT and cystic lesions. The combination of morphological MRI sequences, DCE imaging and DWI helped increase the diagnostic accuracy of MRI.

CONCLUSION

Multiparametric MRI, including morphological MRI sequences, DWI and DCE imaging, is the imaging modality of choice for the characterization of focal PT and provides features that are highly suggestive of a specific diagnosis.

Conventional and Diffusion-Weighted MR Imaging Findings of Parotid Gland Tumors

OBJECTIVE

To investigate diffusion-weighted magnetic resonance imaging (MRI) findings of parotid gland lesions in addition to conventional MRI findings and demographic data.

METHODS

A retrospective evaluation was made of the demographic data, histopathologic data, preoperative conventional and diffusion-weighted MRI of 74 patients who underwent parotidectomy. The patients were categorized according to the histopathology (pleomorphic adenoma [PA], Warthin's Tumor [WT] and malignant Tumor [MT]).

RESULTS

Histologically, 30 patients had PA, 27 patients had WT, and the remaining 17 patients had MT. The mean age of the PA, WT and MT groups were 44±21 (20-72), 55±10 (41-71) and 62±20 (21-76) years, respectively. The WT (81%) and MT (70%) groups were male dominant, while the PA group showed female dominance (55%). The PA group showed statistically significant difference in terms of age (p<0.05) and gender (p=0.009) compared to the other two groups. The median apparent diffusion coefficient (ADC) values for the PA, WT and MT groups were 1.99±0.94 (1.10-2.41) × 10-3 mm2/s, 0.92±0.35 (0.21-1.79) × 10-3 mm2/s and 1.20±0.34 (0.78-1.47) × 10-3 mm2/s, respectively. PA was differentiated from the other two groups (p=0.001). The sensitivity and specificity for distinguishing PAs from WT was 97% and 85%, respectively, when the ADC cutoff value was 1.25; and for distinguishing PAs from MT was 77% and 83%, respectively, when the ADC cutoff value was 1.35.

CONCLUSION

ADC measurements are useful for the differentiation of PA from both WT and MT; and can be used as a complementary tool to predict the histopathology in the preoperative planning of parotid tumors.

Added value of susceptibility-weighted imaging to diffusion-weighted imaging in the characterization of parotid gland tumors

PURPOSE

To assess the added value of susceptibility-weighted imaging (SWI) to diffusion-weighted imaging (DWI) in the characterization of parotid gland tumors.

METHODS

Seventy-eight patients with pathologically confirmed parotid gland tumors, who underwent DWI and SWI for pre-surgery evaluation, were enrolled. Apparent diffusion coefficient (ADC) and degree of intratumoral susceptibility signal intensity (ITSS) were measured and compared between benign and malignant groups, and among pleomorphic adenoma (PA), Warthin tumor (WT) and malignant tumor (MT). Independent sample t test, one-way analysis of variance and receiver operating characteristic curve analysis were used for statistical analyses.

RESULTS

Benign parotid gland tumor showed a significantly higher mean ADC value than malignant tumors (0.836 ± 0.350 vs 0.592 ± 0.163, p = 0.001). Setting an average ADC value of 0.679 as the cut-off value, optimal differentiating performance could be obtained (AUC, 0.700; sensitivity, 62.69%; specificity, 81.82%) for differentiating malignant from benign tumors. PA showed significantly higher mean ADC and less ITSS than WT (ADC, p < 0.001; ITSS, p = 0.033) and MT (ADC, p < 0.001; ITSS, p = 0.024), while the difference between WT and MT was not significant (ADC, p = 0.826; ITSS, p = 0.539). After integration with ITSS, the diagnostic performance of ADC was improved for differentiating PA from WT (AUC 0.921 vs 0.873) and from MT (AUC 0.906 vs 0.882).

CONCLUSION

SWI could provide added information to DWI and serve as a supplementary imaging marker for the characterization of parotid gland tumors.

Shear wave elastography as a new method to identify parotid lymphoma in primary Sjögren Syndrome patients: an observational study

Parotid non-Hodgkin lymphoma (NHL) in primary Sjögren syndrome (pSS) has no specific biomarker for diagnosis. Salivary glands ultrasound (US) is largely used, but its contribution in detecting parotid NHL has not been established. The aim of our study was to determine the added value of bidimensional shear wave elastography (2D-SWE) in pSS diagnosis and to determine its accuracy in identifying parotid NHL. Grey-scale US (GSUS) and 2D-SWE of salivary glands were performed in 35 patients with pSS and 35 healthy controls. The GSUS scores were calculated and the mean of three SWE consecutive measurements was used to appreciate the gland stiffness. SWE increase the diagnostic rate at a cut-off of 6.45 kPa (from 88.6 to 94.2%, p < 0.001) only if applied in patients with insufficient GSUS criteria for pSS diagnosis. The parotid glands with NHL (8 patients, all mucosa-associated lymphoid tissue type) had hyperechoic bands in more than half of the glandular parenchyma (in 68.75% of the glands), large hypoechoic area > 20 mm (all glands), traced gland area over 5 cm² (all glands), parotid US score greater than 13 (in 68.75% of the glands), and high stiffness (elasticity modulus 13.9 ± 4.08 vs 6.32 ± 2.24) (all p < 0.001). These findings give high sensitivity (92.3%), specificity (100%), and positive (100%) and negative predictive values (98.3%) for NHL identification. The rest of GSUS findings did not correlate with the classic risk factors for lymphoma development (all p > 0.05). 2D-SWE had added value for pSS diagnosis in cases where GSUS aspect is normal or nonspecific. The higher stiffness of parotid NHL can be used for early diagnosis, biopsy guidance, and, possible, for treatment monitoring.

The histogram analysis of apparent diffusion coefficient in differential diagnosis of parotid tumor

OBJECTIVES

Use apparent diffusion coefficient (ADC) histogram to investigate whether the parameters of ADC histogram can distinguish between benign and malignant tumors and further differentiate the tumor subgroups.

METHODS AND MATERIALS

This study retrospectively enrolls 161 patients with parotid gland tumors. Histogram parameters including mean, inhomogeneity, skewness, kurtosis and 10th, 25th, 50th, 75th, 90th percentiles are derived from ADC mono-exponential model. Mann-Whitney U test is used to compare the differences between benign and malignant groups. Kruskal-Wallis test with post-hoc Dunn-Bonferroni method is used for subgroup classification, then receiver operating characteristic curve analysis is performed in mean ADC value to obtain the appropriate cutoff values.

RESULTS

Except for kurtosis and 90th percentile, there are significant differences in all other ADC parameters between benign and malignant groups. In subgroup classification of benign tumors, there are significant differences in all ADC parameters between pleomorphic adenoma and Warthin's tumor (area under curve 0.988; sensitivity 93.8%; specificity 94.7%; all ps < 0.05). Pleomorphic adenoma has high value in mean than basal cell adenoma (area under curve 0.819; sensitivity 76.9%; specificity 76.9%; p < 0.05). Basal cell adenoma has high values in mean (area under curve 0.897; sensitivity 92.3%; specificity 78.9%; all ps < 0.05) and 10th, 25th, 50th percentiles than Warthin's tumor. In subgroup classification of malignant tumors, low-risk parotid carcinomas have higher values than hematolymphoid tumors in mean (area under curve 0.912; sensitivity 84.6%; specificity 100%, all ps < 0.05) and 10th, 25th percentiles.

CONCLUSION

ADC histogram parameters, especially mean and 10th, 25th percentiles, can potentially be an effective indicator for identifying and classifying parotid tumors.

Multiparametric Magnetic Resonance Imaging for the Diagnosis and Differential Diagnosis of Parotid Gland Tumors

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.