Value of diffusion tensor imaging in differentiating malignant from benign parotid gland tumors

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.

Multiparametric approach with synthetic MR imaging for diagnosing salivary gland lesions

PURPOSE

To determine whether synthetic MR imaging can distinguish between benign and malignant salivary gland lesions.

METHODS

The study population included 44 patients with 33 benign and 11 malignant salivary gland lesions. All MR imaging was obtained using a 3 Tesla system. The QRAPMASTER pulse sequence was used to acquire images with four TI values and two TE values, from which quantitative images of T1 and T2 relaxation times and proton density (PD) were generated. The Mann-Whitney U test was used to compare T1, T2, PD, and ADC values among the subtypes of salivary gland lesions. ROC analysis was used to evaluate diagnostic capability between malignant tumors (MTs) and either pleomorphic adenomas (PAs) or Warthin tumors (WTs). We further calculated diagnostic accuracy for distinguishing malignant from benign lesions when combining these parameters.

RESULTS

PAs demonstrated significantly higher T1, T2, PD, and ADC values than WTs (all p < 0.001). Compared to MTs, PAs had significantly higher T1, T2, and ADC values (all p < 0.001), whereas WTs had significantly lower T1, T2, and PD values (p < 0.001, p = 0.008, and p = 0.003, respectively). T2 and ADC were most effective in differentiating between MTs and PAs (AUC = 0.928 and 0.939, respectively), and T1 and PD values for differentiating between MTs and WTs (AUC = 0.915 and 0.833, respectively). Combining T1 with T2 or ADC achieved accuracy of 86.4% in distinguishing between malignant and benign tumors. Similarly, combining PD with T2 or ADC reached accuracy of 86.4% for differentiating between malignant and benign tumors.

CONCLUSIONS

Utilizing a combination of synthetic MRI parameters may assist in differentiating malignant from benign salivary gland lesions.

Histogram analysis of ultrasonographic images in the differentiation of benign and malignant parotid gland tumors

OBJECTIVE

We evaluated the diagnostic value of histogram analysis (HA) using ultrasonographic (US) images for differentiation among pleomorphic adenoma (PA), adenolymphoma (AL), and malignant tumors (MT) of the parotid gland.

STUDY DESIGN

Preoperative US images of 48 patients with PA, 39 patients with AL, and 17 patients with MT were retrospectively analyzed for gray-scale histograms. Nine first-order texture features derived from histograms of the tumors were compared. Area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of texture features. The Youden index maximum exponent was used to calculate sensitivity and specificity.

RESULTS

Statistically significant differences were discovered in Mean and Skewness HA values between PA and AL (P<0.001), and in Mean values between AL and MT (P<0.001). However, comparison of PA and MT showed no statistically significant differences (P>0.01). Excellent discrimination was detected between PA and AL (AUC=0.802), and between AL and MT (AUC=0.822). The combination of Mean plus Skewness improved discrimination between PA and AL (AUC=0.823) with sensitivity values reaching 1.00. However, Mean plus Skewness applied to differentiate PA from AL and Mean values applied to distinguish AL and MT resulted in low specificity, indicating many false positive interpretations.

CONCLUSIONS

Histogram analysis is useful for differentiating PA from AL and AL from MT but not PA from MT.

Unusual case of skull base adenoid cystic carcinoma presenting as skull base osteomyelitis: case report

Background

Adenoid cystic carcinoma is a rare malignancy. Tumours of palatal region with minor salivary gland origin do not generally present at an early stage as the tumour is submucosal with symptoms prevalent only when there is evidence of perineural spread of the tumour. We report a case of adenoid cystic carcinoma of the palate with rare presentation of left ear discharge and diplopia on left lateral gaze. We discuss the case with emphasis on imaging evaluation mimicking a case of infective etiology with adjacent skull base osteomyelitis on initial presentation. However, on follow-up and further evaluation the patient was diagnosed as adenoid cystic carcinoma of hard palate on left side.

Case presentation

A 25-year-old male patient has presented to Jagadguru Sri Shivarathreeswara Hospital in August 2019 with complaints of left ear discharge and diplopia on left lateral gaze since 1 week. The clinical and imaging findings was suggestive of infective etiology and the patient was treated for the same with IV antibiotics. Repeat magnetic resonance imaging was then done which revealed definitive reduction in the severity of inflammation suggestive of response to therapy. Patient was then discharged and was followed up. Three months later, the patient came with complaints of mass in left nasal cavity. Patient was then referred for contrast enhanced computed tomography neck strongly suggestive of neoplastic etiology. The patient was then operated and histopathological examination of the biopsy revealed adenoid cystic carcinoma.

Conclusions

Tumours of palatal region with minor salivary gland origin do not generally present at an early stage as the tumour is submucosal with symptoms prevalent only when there is evidence of perineural spread of the tumour. In our case patient presented with lateral rectus palsy, involvement of meckel’s cave, trigeminal nerve involvement and cavernous sinus involvement which are strong indicators of the perineural and locoregional spread of the tumour. Hence, it is important for the radiologist and clinician to strongly suspect and evaluate for a primary lesion of the head and neck when such a radiological presentation has been demonstrated.

Frequency and imaging features of the adjacent osseous changes of salivary gland carcinomas in the head and neck region

PURPOSE

The association between salivary gland carcinomas and adjacent osseous changes in the head and neck region is not clear. We evaluated the frequency and imaging features of such changes and investigated the specific characteristics of salivary gland carcinomas associated with them.

METHODS

A total of 118 patients with histologically proven salivary gland carcinomas were retrospectively reviewed. The imaging characteristics of osseous changes were sorted into three categories based on computed tomography images: sclerotic change, erosive change, and lytic change. The frequency of all these osseous changes and any one of them was compared between different pathologies using Fisher's exact test. Odds ratios were calculated to evaluate the association between these changes and perineural spread.

RESULTS

Osseous changes were found in 21 (18%) of 118 cases. Among these, seven (6%) cases were with sclerotic, nine (8%) with erosive, and nine (8%) with lytic changes (four with mixed change). Adenoid cystic carcinoma showed a significantly higher frequency of sclerotic and erosive changes, and either osseous change, than the other salivary gland carcinomas (p < 0.001 for each). Sclerotic changes were only present in the adenoid cystic carcinomas. Perineural spread was a significant factor in showing higher osseous change frequencies (odds ratio = 3.98, p = 0.006).

CONCLUSION

Among salivary gland carcinomas in the head and neck region, adenoid cystic carcinomas had a significantly higher frequency of adjacent osseous changes, especially sclerotic changes, than other salivary gland carcinomas.

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.

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging

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.

Diffusion Tensor Imaging in Characterization of Mediastinal Lymphadenopathy

OBJECTIVE

To determine the efficacy of diffusion-weighted MRI (DWI) and diffusion tensor imaging (DTI) in the characterization of mediastinal lymphadenopathy and the differentiation between malignant and benign lymph nodes (LNs).

METHODS

a retrospective evaluation of 58 patients with mediastinal lymphadenopathy that underwent DWI and DTI with calculation of apparent diffusion coefficient (ADC), fractional anisotropy (FA), and mean diffusivity (MD) values of LNs. Final diagnosis was made by the histopathology and proved metastatic (n = 21), lymphomatous (n = 14), granulomatous (n = 11) and reactive (n = 12) LNs.

RESULTS

Malignant mediastinal LNs had remarkably lower ADC and MD; (p = 0.001) and higher FA; (p = 0.001) than in benign LNs. The threshold of ADC, MD, and FA at (1.48, 1.32 × 10^-3 mm²/s), (1.31, 1.33 × 10^-3 mm²/s), (0.62, 0.52) to differentiate malignant from benign LNs has AUC of (0.89, 0.94), (0.96, 0.95), (0.72, 0.82), accuracy of (87%, 86%), (89%, 86%), (70%, 72%) by both observers respectively. The threshold of ADC, MD, and FA at (1.47, 1.32 × 10^-3 mm²/s), (1.31, 1.3 × 10^-3 mm²/s), (0.62, 0.67) used to differentiate metastatic from reactive LNs revealed AUC of (0.90, 0.94), (0.96, 0.96), (0.73, 0.77), accuracy of (87%, 81%), (87%, 81%), (72%, 66%) by both observers respectively. The mean ADC and MD values of metastatic LNs were statistically significant (p = 0.001) and (p = 0.002, 0.02) respectively when compared with that of lymphoma. The threshold of ADC, and MD (0.94, 0.97 × 10^-3 mm²/s) and (0.87, 0.91 × 10^-3 mm²/s) used to differentiates metastatic from lymphomatous nodes revealed AUC of (0.90, 0.91), (0.81, 0.74), an accuracy of (85%, 91%), (71%, 71%), by both observers respectively. The inter-class correlation between two observers for all nodes for ADC, MD and FA was r= 0.931, 0.956 and 0.885 respectively.

CONCLUSION

Using ADC, MD, and FA can help in the characterization of mediastinal lymphadenopathy noninvasively.

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.

MR Imaging of Salivary Gland Tumors

Neoplasms of the salivary glands are characterized by their marked histologic diversity giving them nonspecific imaging findings. MR imaging is the best imaging modality to evaluate salivary gland tumors. Multiparametric MR imaging combines conventional imaging features, diffusion-weighted imaging, and perfusion imaging to help distinguish benign and low-grade neoplasms from malignant tumors; however, a biopsy is often needed to establish a definitive histopathologic diagnosis. An awareness of potential imaging pitfalls is important to prevent mistakes in salivary neoplasm imaging.

Usefulness of readout-segmented EPI-based diffusion tensor imaging of lacrimal gland for detection and disease staging in thyroid-associated ophthalmopathy

Background

Dysfunction of lacrimal gland (LG) gains increasing attention in patients with thyroid-associated ophthalmopathy (TAO), while the underlying pathological change is still not fully established. This study aimed to evaluate the utility of readout-segmented echo-planar imaging (rs-EPI)-based diffusion tensor imaging (DTI) in non-invasively detecting microstructural alterations of LG in patients with TAO, as well as in discriminating disease activity.

Methods

Thirty TAO patients and 15 age- and sex- matched healthy controls, who underwent rs-EPI-based DTI, were retrospectively enrolled. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) of LG, and clinical-endocrinological variables were collected and compared. The correlations between FA and ADC values of LG and serum thyroid biochemical markers were also assessed.

Results

TAO group showed significantly lower FA (P < 0.001) and higher ADC (P = 0.014) of LG than healthy group. Active subgroup had significantly lower FA (P < 0.001) and higher ADC (P < 0.001) than inactive subgroup. In TAO group, FA of LG was significantly and negatively correlated with TRAb (r=-0.475, P = 0.008), while ADC of LG showed no significant correlation (P > 0.05). The area under receiver operating characteristic curve of FA was significantly greater than that under curve of ADC for discriminating disease activity (0.832 vs. 0.570, P = 0.009).

Conclusions

rs-EPI-based DTI is a useful tool to characterize the microstructural change of LG in patients with TAO. The derived metrics, particularly FA, can help to reveal disease activity.

MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland

OBJECTIVES

Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT.

METHODS

A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets.

RESULTS

Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness.

CONCLUSIONS

The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process.

KEY POINTS

• Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.

Diffusion-weighted imaging with histogram analysis of the apparent diffusion coefficient maps in the diagnosis of parotid tumours

The aim of this study was to investigate the role of diffusion-weighted imaging (DWI) with histogram analysis of apparent diffusion coefficient (ADC) maps in the characterization of parotid tumours. This prospective study included 39 patients with parotid tumours. All patients underwent magnetic resonance imaging with DWI, and ADC maps were generated. The whole lesion was selected to obtain histogram-related parameters, including the mean (ADC_mean), minimum (ADC_min), maximum (ADC_max), skewness, and kurtosis of the ADC. The final diagnosis included pleomorphic adenoma (PA; n=18), Warthin tumour (WT; n=12), and salivary gland malignancy (SGM; n=9). ADC_mean (×10^-3mm²/s) was 1.93±0.34 for PA, 1.01±0.11 for WT, and 1.26±0.54 for SGM. There was a significant difference in whole lesion ADC_mean among the three study groups. Skewness had the best diagnostic performance in differentiating PA from WT (P=0.001; best detected cut-off 0.41, area under the curve (AUC) 0.990) and in discriminating WT from SGM (P=0.03; best detected cut-off 0.74, AUC 0.806). The whole lesion ADC_mean value had best diagnostic performance in differentiating PA from SGM (P=0.007; best detected cut-off 1.16×10^-3mm²/s, AUC 0.948). In conclusion, histogram analysis of ADC maps may offer added value in the differentiation of parotid tumours.

Conventional, diffusion, and dynamic contrast-enhanced MRI findings for differentiating metaplastic Warthin's tumor of the parotid gland

The purpose of this study was to explore conventional, diffusion, and dynamic contrast-enhanced MRI (DCE-MRI) characteristics for differentiating metaplastic Warthin's tumor (MWT) from other tumor types of the parotid gland, including non-metaplastic Warthin's tumor (non-MWT), pleomorphic adenoma (PA), and malignant tumor (MT). A total of 178 patients with histologically proven tumors of the parotid gland, including 21 MWTs, 49 non-MWTs, 66 PAs, and 42 MTs, were enrolled in the study. Conventional MRI was performed in all patients. One hundred and fifty patients had preoperative diffusion-weighted MR imaging (DWI), and 62 patients had preoperative DCE-MRI. The differences in the conventional, DCE-MRI, and DWI records between MWTs and the other three tumor types were statistically evaluated. Compared with non-MWTs and PAs, there was a statistically significant difference in circumscription (p < 0.01). The ill-defined circumscription was more common in MWTs than non-MWTs and PAs. Compared with PAs, there was a statistically significant difference in morphology (p < 0.05). The lobulated morphology was more common in PAs than MWTs. Compared with PAs and MTs, there was a statistically significant difference in the T2 signal of the solid component (p < 0.01). The T2 moderate intensity of solid components was more common in MWTs than PAs and MTs. The solid components of PAs mostly showed hyperintense on T2-weighted imaging. Cyst/necrosis was more common in MWTs than PAs and MTs. Hyperintense of cyst/necrosis was more common in MWTs and non-MWTs. With respect to contrast enhancement, 52.4% MWTs exhibited moderate or marked enhancement, and most non-MWTs (81.6%) exhibited mild enhancement. Most PAs (84.8%) exhibited marked enhancement. The mean ADC value of MWTs (0.94 × 10-3 ± 0.11 mm2/s) was significantly lower than that of the PAs (1.60 × 10-3 ± 0.17 mm2/s) (p < 0.001). On DCE-MRI, six of eight MWTs demonstrated TIC of type B. Although MWT is rare, conventional MRI characteristics, DWI and DCE-MRI can provide useful information for differentiating MWT from other parotid mass.

A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging

AIM

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).

MATERIALS AND METHODS

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.

RESULTS

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.

CONCLUSION

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.

Differentiating malignant from benign salivary gland lesions: a multiparametric non-contrast MR imaging approach

The purpose of this study is to determine whether multiparametric non-contrast MR imaging including diffusion-weighted imaging (DWI), arterial spin labeling (ASL), and amide proton transfer (APT) weighted imaging can help differentiate malignant from benign salivary gland lesions. The study population consisted of 42 patients, with 31 benign and 11 malignant salivary gland lesions. All patients were evaluated using DWI, three-dimensional pseudo-continuous ASL, and APT-weighted imaging on 3 T MR imaging before treatment. Apparent diffusion coefficient (ADC), tumor blood flow (TBF), and APT-related signal intensity (APTSI) values within the lesion were compared between the malignant and benign lesions by Mann-Whitney U test. For each parameter, optimal cutoff values were chosen using a threshold criterion that maximized the Youden index for predicting malignant lesions. The performance of ADC, TBF, APTSI, individually and combined, was evaluated in terms of diagnostic ability for malignant lesions. Diagnostic performance was compared by McNemar test. APTSI was significantly higher in malignant lesions (2.18 ± 0.89%) than in benign lesions (1.57 ± 1.09%, p = 0.047). There was no significant difference in ADC or TBF between benign and malignant lesions (p = 0.155 and 0.498, respectively). The accuracy of ADC, TBF, and APTSI for diagnosing malignant lesions was 47.6%, 50.0%, and 66.7%, respectively; whereas the accuracy of the three parameters combined was 85.7%, which was significantly higher than that of each parameter alone (p = 0.001, 0.001, and 0.008, respectively). Therefore, the combination of ADC, TBF, and APTSI can help differentiate malignant from benign salivary gland lesions.

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.

Classification of parotid gland tumors by using multimodal MRI and deep learning

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T₂ -weighted, postcontrast T₁ -weighted, and diffusion-weighted images. In this study, we present a fully automatic system for the diagnosis of parotid gland tumors by using deep learning methods trained on multimodal MRI images. We used a two-dimensional convolution neural network, U-Net, to segment and classify parotid gland tumors. The U-Net model was trained with transfer learning, and a specific design of the batch distribution optimized the model accuracy. We also selected five combinations of MRI contrasts as the input data of the neural network and compared the classification accuracy of parotid gland tumors. The results indicated that the deep learning model with diffusion-related parameters performed better than those with structural MR images. The performance results (n = 85) of the diffusion-based model were as follows: accuracy of 0.81, 0.76, and 0.71, sensitivity of 0.83, 0.63, and 0.33, and specificity of 0.80, 0.84, and 0.87 for Warthin tumors, pleomorphic adenomas, and malignant tumors, respectively. Combining diffusion-weighted and contrast-enhanced T₁ -weighted images did not improve the prediction accuracy. In summary, the proposed deep learning model could classify Warthin tumor and pleomorphic adenoma tumor but not malignant tumor.

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.

Differentiation of salivary gland tumor using diffusion-weighted imaging with a fractional order calculus model

OBJECTIVE

To evaluate the feasibility of using imaging parameters (D, β and μ) obtained from fractional order calculus (FROC) diffusion model to differentiate salivary gland tumors.

METHODS

15 b-value (0-2000 s/mm²) diffusion-weighted imaging (DWI) was scanned in 62 patients with salivary gland tumors (47 benign and 15 malignant). Diffusion coefficient D, fractional order parameter β (which correlates with tissue heterogeneity) and a microstructural quantity μ of the solid portion within the tumor were calculated, and compared between benign and malignant groups, or among pleomorphic adenoma (PA), Warthin's tumor (WT), and malignant tumor (MT) groups. Performance of FROC parameters for differentiation was assessed using receiver operating characteristic analysis.

RESULTS

None of the FROC parameters exhibited significant differences between benign and malignant group (D, p = 0.150; β, p = 0.967; μ, p = 0.693). WT showed significantly lower D (p < 0.001) and β (p < 0.001), while higher μ (p = 0.001) than PA. Combination of D, β and μ showed optimal diagnostic performance (area under the curve, AUC, 0.998). MT showed significantly lower D (p = 0.001) and β (p = 0.025) than PA, while no significant difference was found on μ (p = 0.064). Combination of D and β showed optimal diagnostic performance (AUC, 0.933). Significant difference was found on β (p = 0.027) between MT and WT, while not on D (p = 0.806) and μ (p = 0.789). Setting a βof 0.615 as the cut-off value, optimal diagnostic performance could be obtained (AUC = 0.806).

CONCLUSION

A non-Gaussian FROC diffusion model can serve as a noninvasive and quantitative imaging technique for differentiating salivary gland tumors.

ADVANCES IN KNOWLEDGE

(1) PA showed higher D and β and lower μ than WT. (2) PA had higher D and β than MT. (3) WT demonstrated lower β than MT. (4) β, as a new FROC parameter, could offer an added value to the differentiation.

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.

Routine and Advanced Diffusion Imaging Modules of the Salivary Glands

This article reviews the role of routine and advanced diffusion imaging modules of the salivary glands. Routine and advanced diffusion imaging modules have a role in differentiation of malignant from benign salivary gland tumors, characterization of some benign salivary gland tumors, and staging of salivary gland cancer. The role of advanced diffusion modules in patients with salivary gland cancer after surgery, radiation therapy, or radioiodine therapy is discussed. Advanced diffusion imaging modules can help in diagnosis and staging of Sjögren syndrome.

Characterization of salivary gland tumours with diffusion tensor imaging

OBJECTIVES

To characterize salivary glands tumours with diffusion tensor imaging.

METHODS

This study was conducted upon 53 patients (aged 18-81 years: mean 37 years) with salivary gland tumours that underwent diffusion tensor imaging was obtained using a single-shot echoplanar imaging sequence with parallel imaging at 1.5 T scanner. 48 slices were obtained, with a thickness of 2.5 mm, with no gap and the total scan duration was 7-8 min. The fractional anisotropy (FA) and the mean diffusivity (MD) value of the salivary gland tumours was calculated and correlated with pathological findings. Image analysis was performed by one radiologist. The receiver operating characteristic curve was drawn to detect the cut-off point of FA and MD used to characterize salivary gland tumours.

RESULTS

The mean FA and MD of malignant salivary gland tumours (n = 17) (0.41 ± 0.07 and 0.89 ± 0.15 × 10-3 mm2 s-1) was significantly different (p = 0.001) than that of benign tumours (n = 36) (0.19 ± 0.07 and 1.28 ± 0.42 × 10-3 mm2 s-1), respectively. Combined FA and MD used to differentiate malignant from benign tumours has an area under the curve (AUC) of 0.974, and an accuracy of 86%. There was a significant difference in FA between Warthin tumours and malignant tumours (p = 0.001). Selection FA of 0.35 to differentiate malignant tumours from Warthin tumours revealed AUC of 0.878 and an accuracy of 80%. There was a significant difference in FA and MD of malignant tumours and pleomorphic adenomas (p = 0.001). Combined FA and MD used to differentiate malignant tumours from pleomorphic adenomas revealed AUC of 0.993, and an accuracy of 93%. There was a significant difference in FA and MD of Warthin tumours and pleomorphic adenomas (p = 0.001). Combined FA and MD used to differentiate Warthin tumours from pleomorphic adenomas revealed AUC of 0.978, and an accuracy of 86%.

CONCLUSIONS

Diffusion-weighed imaging is a promising non-invasive method and it may be useful for the characterization and differentiation of benign and malignant salivary gland tumours.