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

Xu, Zhuangyong; Chen, Meiwei; Zheng, Shaoyan; Chen, Shaoxian; Xiao, Jianning; Hu, Zehuan; Lu, Liejing; Yang, Zehong; Lin, Daiying

doi:10.1016/j.ejrad.2021.110094

Cited by 8 publications

(6 citation statements)

References 41 publications

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“…Taking that into consideration, we agree with Yabuuchi et al that further analysis of Kep and Ve parameters may prove them feasible for precise evaluation of the contrast agent movement between extravascular extracellular space and blood pool [28]. Furthermore, in the light of recent research by Xu et al, Ve seems to have a discriminative potential between benign and malignant lesions [39].…”

Section: Discussionsupporting

confidence: 89%

“…Interpreted in conjunction with conventional morphological sequences, dynamic contrast-enhanced MRI and diffusionweighted MRI have high potential for the characterization of parotid tumors [37], according to research comparable to that of FNAC [7,38]. Nowadays, novel advanced DWI analysis techniques are under ongoing research with the aim to identify characteristic features of different histopathological types of parotid tumors in order to further increase diagnostic accuracy [39]. In our study DWI, IVIM and quantitative analysis of DCE-MRI derived parameters demonstrated distinctive features of pleomorphic adenomas and Warthin tumors as confirmed with a Kruskal-Wallis test for independent samples.…”

Section: Discussionmentioning

confidence: 99%

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

Markiet

Glińska

Nowicki

et al. 2022

Biology

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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.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

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

Markiet

Glińska

Nowicki

et al. 2022

Biology

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“…In the evaluation of parotid gland tumors with MR imaging, poorly defined margins, T2-signal hypointensity, and invasion of surrounding structures are suggestive of malignancy but not definitive (4,7,8). Malignant tumors demonstrate ill-defined borders in fifty-nine percent of cases, which is almost three times more than benign…”

Section: Discussionmentioning

confidence: 99%

A preliminary diagnostic accuracy study of quantitative MRI biomarkers for differentiating parotid tumor types

Alavanja¹,

Hooper²,

Hasse³

et al. 2023

Gland Surg

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Background: Differentiating among the different types of parotid tumors on imaging is useful for guiding clinical disposition, which ultimately may lead to surgical management. The goal of this study was to determine whether quantitative T2 signal characteristics and morphologic features on magnetic resonance imaging (MRI) can serve as predictive biomarkers for distinguishing between tumor types.Methods: A retrospective review of T2-weighted MRIs in patients with pathology-proven parotid tumors was performed. Quantitative T2 maps and surface regularity measurements of the tumors were obtained via semi-automated regions of interest (ROI). Linear Discriminant Analysis was used to populate the receiver operating characteristics (ROCs) curves for these variables. A P value of <0.05 was considered to be significant.Results: A total of 35 tumors (21 benign and 14 malignant neoplasms) were included in this analysis.For differentiating the benign versus malignant classes of parotid tumors, T2 signal and surface regularity combined yielded an area under the curve of 0.62 (P value: 0.2) through the ROC analysis. However, for the pleomorphic adenomas versus other types of parotid tumors, using both T2 signal and surface regularity yielded an area under the curve of 0.81 (P value: 0.007) through the ROC analysis.Conclusions: T2 signal and surface regularity combined can significantly differentiate pleomorphic adenomas from other types of parotid tumors and can potentially be used as a predictive imaging biomarker.

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“…The evaluation included the following radiological features (1): size (maximum tumor diameter in axial position) (2); tumor location (superficial or deep lobe was determined according to the main part of the tumor; the superficial and deep lobes are demarcated by a virtual line drawn from the lateral border of the posterior belly of the digastric muscle and retromandibular vein to the lateral edge of the mandible (15) (3); scope (localized or diffused; tumor involving local or whole parotid gland) (4); number (single or multiple) (5); shape (regular or irregular) (6); tumor margin (well-defined or ill-defined) (7); cystic or necrotic areas (CNA; absent or present) (8); infiltration of surrounding tissue (IST, absent or present; tumors involve surrounding muscles, bone, skin, or subcutaneous tissue (3) (9); lymphatic metastasis (LM; absent or present; obvious density change in cervical lymph nodes or short axis diameter >10 mm) (10); CT value at each phase (the solid section of the tumor was measured three times, and the average was calculated) (11); enhancement degree in P-A phase (difference in CT values between arterial and plain phases); and (12) enhancement degree in A-V phase (difference in CT values between venous and arterial phases).…”

Section: Clinical-radiological Feature Evaluationmentioning

confidence: 99%

“…CT and MRI are widely applied in preoperative localization, tumor invasion diagnoses, and differential diagnoses of parotid tumors, but conventional image evaluation largely depends on semantic features, and a large amount of information on tumor heterogeneity cannot be quantitatively elucidated ( 8 ). Although the application of multiparametric MRI in parotid tumors has increased, such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced MRI, its value in the differential diagnosis of benign and malignant parotid tumors is still controversial ( 9 , 10 ).…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2022

Front. Oncol.

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ObjectiveThis 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).MethodsThis 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.ResultsAmong 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).ConclusionMultiphasic 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.

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

Cited by 8 publications

References 41 publications

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

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

A preliminary diagnostic accuracy study of quantitative MRI biomarkers for differentiating parotid tumor types

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

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