Diagnostic value of capsule-like rim enhancement on magnetic resonance imaging for distinguishing malignant from benign parotid tumours

Newly Discovered Parotid Lesion: What Next?

When a parotid lesion is discovered incidentally, it can be challenging for the radiologist to provide specific recommendations for the next steps. This article describes how the radiologist can play an active role in managing incidentally discovered parotid lesions. First, we explore the significance of parotid lesions. Next, the pertinent anatomy of the parotid space is presented to develop an appropriate differential diagnosis. Lastly, we discuss critical clinical and imaging characteristics the radiologist can use to provide specific recommendations.

Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI

BACKGROUND

Parotid tumours (PTs) have a variety of pathological types, and the surgical procedures differ depending on the tumour type. However, accurate diagnosis of PTs from the current preoperative examinations is unsatisfactory.

METHODS

This retrospective study was approved by the Ethics Committee of our hospital, and the requirement for informed consent was waived. A total of 73 patients with PTs, including 55 benign and 18 malignant tumours confirmed by surgical pathology, were enrolled. All patients underwent diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), susceptibility-weighted imaging (SWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). The signal uniformity and capsule on T2WI, apparent diffusion coefficient (ADC) derived from DWI, semi-quantitative parameter time-intensity curve (TIC) pattern, and quantitative parameters including transfer constant (K_trans), extravascular extracellular volume fraction (V_e), wash-out constant (K_ep) calculated from DCE-MRI, and intratumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared between benign and malignant PTs. Logistic regression analysis was used to select the predictive parameters for the classification of benign and malignant parotid gland tumours, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic performance.

RESULTS

Malignant PTs tended to exhibit a type C TIC pattern, whereas benign tumours tended to be type A and B (p < 0.001). Benign PTs had less ITSS than malignant tumours (p < 0.001). Multivariate analyses showed that ADC, V_e, and ITSS were predictors of tumour classification. ROC analysis showed that the area under the curve (AUC) of ADC, V_e, ITSS, and ADC combined with V_e were 0.623, 0.615, 0.826, and 0.782, respectively, in differentiating between malignant and benign PTs. When ITSS was added, the AUCs of ADC, V_e, and ADC combined with V_e increased to 0.882, 0.848, and 0.930, respectively.

CONCLUSION

SWI offers incremental diagnostic value to DWI and DCE-MRI in the characterisation of parotid gland tumours.

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.

Magnetic resonance image biomarkers improve differentiation of benign and malignant parotid tumors through diagnostic model analysis

OBJECTIVES

To explore the effectiveness of magnetic resonance image (MRI)-based biomarkers for identifying benign and malignant parotid tumors via diagnostic model analysis.

METHODS

This retrospective study included 109 patients (development cohort and validation cohort) who underwent MRI preoperatively, including T1- and T2-weighted images. Parameters based on 2D or 3D texture analysis were extracted from tumor lesions by MaZda software, fisher discriminant and bootstrap method were used to perform parameter reduction, diagnostic models with the selected biomarkers were established along with clinical data, model performance (discrimination and calibration) was furtherly evaluated by internal and external validation, decision curve analysis was applied to measure the improvement of clinical benefits.

RESULTS

S(5,5) Entrop, S(0,1) ASM, WavEnHH (s-4), S(1,1,0) Entropy and Perc.10% were significantly associated with the pathological diagnosis of parotid tumor (benign versus malignancy), when adding these biomarkers to the regression analysis, model performance significantly improved in the development cohort (likelihood-ratio-test; p < 0.05, with an increase of AUC from 0.72 (reference model) to 0.85), and these results were maintained in a small external validation cohort. Decision curve analysis indicated that clinical benefit was greater with the application of MRI-based biomarkers.

CONCLUSIONS

MRI-based texture analysis is proven to be an effective tool in differentiating benign and malignant parotid tumors, preoperative diagnosis was improved with the selected biomarkers compared to the reference model.

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.

Controversies in the Workup and Surgical Management of Parotid Neoplasms

OBJECTIVE

Parotid neoplasms are a rare heterogeneous group of tumors with varied clinical presentation and behavior. Here we provide an evidence-based review of the contemporary approach to evaluation and surgical management of parotid tumors.

DATA SOURCE

PubMed and Web of Science Databases.

REVIEW METHODS

Searches of the PubMed and Web of Science databases were performed on subjects related to the diagnosis and surgical management of parotid neoplasms. Particular emphasis was placed on the following areas: evaluation of parotid tumors, including imaging workup and the utility of fine-needle aspiration; extent of surgery of the primary lesion, including the extent of parotidectomy as well as oncologic management of the facial nerve; the extent of surgery of involved and at-risk cervical lymphatics; and parotid bed reconstruction. Articles published from 2014 to the present were prioritized, supplementing with information from prior studies in areas where data are lacking.

CONCLUSION

A summary of the literature in these areas is outlined to provide an evidence-based approach to evaluation and management of parotid neoplasms.

IMPLICATIONS FOR PRACTICE

While data are available to help guide many aspects of workup and management of parotid neoplasms, further research is needed to refine protocols for this heterogeneous group of diseases.

Epithelial salivary gland tumors: Utility of radiomics analysis based on diffusion-weighted imaging for differentiation of benign from malignant tumors

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Value of Diffusion-Weighted Imaging Combined with Susceptibility-Weighted Imaging in Differentiating Benign from Malignant Parotid Gland Lesions

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

Non-enhanced MRI in combination with color Doppler flow imaging for improving diagnostic accuracy of parotid gland lesions

PURPOSE

To determine the value of non-enhanced MRI in combination with color Doppler flow imaging (CDFI) for differentiating malignant parotid tumors from benign ones.

METHODS

This retrospective study analyzed 51 parotid gland lesions (39 benign and 12 malignant) in 51 patients who underwent preoperative CDFI as well as non-enhanced MRI including T₁-weighted, T₂-weighted, and diffusion-weighted imaging (DWI). Degrees of intratumor vascularity were categorized into four grades basing on CDFI findings. The relationships between the lesion and its adjacent external carotid artery and retromandibular vein were inspected on T₁-weighted and T₂-weighted images. Apparent diffusion coefficient (ADC) values were calculated from diffusion-weighted images, and were used to classify the parotid gland lesions with and without reference to the CDFI findings. The classification results were compared using the McNemar test. Sensitivity, specificity, and accuracy percentages were calculated when the non-enhanced MRI/CDFI findings were used to differentiate benign lesions from malignant ones.

RESULTS

The diagnostic accuracy (96.1 vs 82.4%) was significantly improved when ADCs were used together with CDFI findings for classifying parotid gland lesions compared to when ADCs were used alone. Pleomorphic adenomas had the highest ADCs. The ADC thresholds were 1.425 × 10^-3 mm²/s for differentiating pleomorphic adenomas from carcinomas, 0.999 × 10^-3 mm²/s for differentiating pleomorphic adenomas from other benign lesions, and 0.590 × 10^-3 mm²/s for differentiating benign lesions other than pleomorphic adenomas from lymphomas.

CONCLUSION

Combining CDFI with non-enhanced MRI can improve the diagnostic accuracy of MRI for classifying parotid gland lesions.

Meta-analysis of magnetic resonance imaging accuracy for diagnosis of oral cancer

Objective

To establish the diagnostic accuracy of magnetic resonance imaging (MRI) as an auxiliary means for the diagnosis of oral cancer through a systematic review and meta-analysis.

Methods

An exhaustive search of publications from 1986 to 2016 was performed of Medline, Embase and Cochrane (and related databases), including grey literature. Primary diagnostic accuracy studies that assessed oral cancer (target condition) using MRI (index test) were included. Diagnostic threshold, sensitivity and meta-regression analyses were performed. A meta-analysis was performed using Meta-DiSc® v. 1.4 software.

Results

A total of 24 primary studies were assessed, comprising 1,403 oral cancer lesions. Nine studies used diffusion-weighted MRI, with a diagnostic odds ratio (DOR) of 30.7 (95% confidence interval [CI]: 12.7–74.3) and area under the curve (AUC) of 0.917 (95% CI: 0.915–0.918); seven studies used dynamic contrast-enhanced MRI, with a DOR of 48.1 (95%CI: 22.4–103.2) and AUC of 0.936 (95% CI: 0.934–0.937); and 13 studies used traditional MRI, with a DOR of 23.9 (95%CI: 13.2–43.3) and AUC of 0.894 (95% CI: 0.894–0.895). Meta-regression analysis indicated that the magnetic field strength may have influenced the heterogeneity of the results obtained (p = 0.0233) using traditional MRI. Sensitivity analysis revealed a discrete reduction of inconsistency in some subgroups.

Conclusion

The three types of MRI assessed exhibited satisfactory accuracy compared to biopsy. Considering the relevance of early treatment and screening and that better health care results in improved survival rates and quality of life for oral cancer patients, we suggest the use of MRI as a part of the pre-treatment and monitoring protocol at public health services.