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

MR-Neurography of the facial nerve in parotid tumors: intra-parotid nerve visualization and surgical correlation

PURPOSE

One of the most severe complications in surgery of parotid tumors is facial palsy. Imaging of the intra-parotid facial nerve is challenging due to small dimensions. Our aim was to assess, in patients with parotid tumors, the ability of high-resolution 3D double-echo steady-state sequence with water excitation (DE3D-WE) (1) to visualize the extracranial facial nerve and its tracts, (2) to evaluate their relationship to the parotid lesion and (3) to compare MRI and surgical findings.

METHODS

A retrospective study was conducted including all patients with parotid tumors, who underwent MRI from April 2022 to December 2023. Two radiologists independently reviewed DE3D-WE images, assessing quality of visualization of the facial nerve bilaterally and localizing the nerve's divisions in relation to the tumor. MRI data were compared with surgical findings.

RESULTS

Forty consecutive patients were included (M:F = 22:18; mean age 56.3 ± 17.4 years). DE3D-WE could excellently visualize the nerve main trunk and the temporofacial division in all cases. The cervicofacial branch was visible in 99% of cases and visibility was good. Distal divisions were displayed in 34% of cases with a higher visibility on the tumor side (p < 0.05). Interrater agreement was high (weighted kappa 0.94 ± 0.01 [95% CI 0.92-0.97]). Compared to surgery accuracy of MRI in localizing the nerve was 100% for the main trunk, 96% for the temporofacial and 89% for the cervicofacial branches.

CONCLUSIONS

Facial nerve MR-neurography represents a reliable tool. DE3D-WE can play an important role in surgical planning of patients with parotid tumors, reducing the risk of nerve injury.

MTFN: multi-temporal feature fusing network with co-attention for DCE-MRI synthesis

BACKGROUND

Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) plays an important role in the diagnosis and treatment of breast cancer. However, obtaining complete eight temporal images of DCE-MRI requires a long scanning time, which causes patients' discomfort in the scanning process. Therefore, to reduce the time, the multi temporal feature fusing neural network with Co-attention (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables the acquisition of DCE-MRI images without scanning. In order to reduce the time, multi-temporal feature fusion cooperative attention mechanism neural network (MTFN) is proposed to generate the eighth temporal images of DCE-MRI, which enables DCE-MRI image acquisition without scanning.

METHODS

In this paper, we propose multi temporal feature fusing neural network with Co-attention (MTFN) for DCE-MRI Synthesis, in which the Co-attention module can fully fuse the features of the first and third temporal image to obtain the hybrid features. The Co-attention explore long-range dependencies, not just relationships between pixels. Therefore, the hybrid features are more helpful to generate the eighth temporal images.

RESULTS

We conduct experiments on the private breast DCE-MRI dataset from hospitals and the multi modal Brain Tumor Segmentation Challenge2018 dataset (BraTs2018). Compared with existing methods, the experimental results of our method show the improvement and our method can generate more realistic images. In the meanwhile, we also use synthetic images to classify the molecular typing of breast cancer that the accuracy on the original eighth time-series images and the generated images are 89.53% and 92.46%, which have been improved by about 3%, and the classification results verify the practicability of the synthetic images.

CONCLUSIONS

The results of subjective evaluation and objective image quality evaluation indicators show the effectiveness of our method, which can obtain comprehensive and useful information. The improvement of classification accuracy proves that the images generated by our method are practical.

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.

Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland

OBJECTIVE

To evaluate the diagnostic performance of computed tomography (CT) radiomics analysis for differentiating pleomorphic adenoma (PA), Warthin tumor (WT), and basal cell adenoma (BCA).

METHODS

A total of 189 patients with PA (n = 112), WT (n = 53) and BCA (n = 24) were divided into a training set (n = 133) and a test set (n = 56). The radiomics features were extracted from plain CT and contrast-enhanced CT images. After dimensionality reduction, plain CT, multiphase-enhanced CT, integrated radiomics signature models and radiomics score (Rad-score) were established and calculated. The receiver operating characteristic (ROC) curve analysis was taken for the assessment of the model performance, and then comparison was conducted among these models. Decision curve analysis (DCA) was adopted to assess the clinical benefits of the models. Diagnostic performances including sensitivity, specificity, and accuracy of the radiologists were evaluated.

RESULTS

Seven, nine, fourteen, and fourteen optimal features were used to constructed plain scan, arterial phase, venous phase, and integrated radiomics signature models, respectively. ROC analysis showed these four models were able to differentiate PA from BCA and WT, with the area under the ROC curve (AUC) values of 0.79, 0.90, 0.87, and 0.94 in the training set, and 0.79, 0.89, 0.86, and 0.94 in the test set, respectively. The integrated model had better diagnostic performance than single-phase radiomics model, but it had similar diagnostic performance to that of the radiomics model based on the arterial phase (p > 0.05). The sensitivity, specificity, and accuracy in the diagnosis of PA were 0.86, 0.46, and 0.70 for the non-subspecialized radiologist and 0.88, 0.77, and 0.84 for the subspecialized radiologist, respectively. Six venous phase parameters were finally selected in differentiating WT from BCA. The predictive effect of the model was favorable, with AUC value of 0.95, sensitivity of 0.96, specificity of 0.83, and accuracy of 0.92. The sensitivity, specificity, and accuracy in the diagnosis between WT and BCA were 0.26, 0.87, and 0.45 for the non-subspecialized radiologist and 0.85, 0.58, and 0.77 for the subspecialized radiologist, respectively.

CONCLUSION

The CT-based radiomics analysis showed favorable predictive performance for differentiating PA, WT, and BCA, thus may be helpful in the clinical decision-making.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors

Objective

This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs).

Methods

This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves.

Results

Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888).

Conclusion

Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.

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.