JOURNAL CLUB: The Warthin Tumor Score: A Simple and Reliable Method to Distinguish Warthin Tumors From Pleomorphic Adenomas and Carcinomas

Using Machine Learning on MRI Radiomics to Diagnose Parotid Tumours Before Comparing Performance with Radiologists: A Pilot Study

The parotid glands are the largest of the major salivary glands. They can harbour both benign and malignant tumours. Preoperative work-up relies on MR images and fine needle aspiration biopsy, but these diagnostic tools have low sensitivity and specificity, often leading to surgery for diagnostic purposes. The aim of this paper is (1) to develop a machine learning algorithm based on MR images characteristics to automatically classify parotid gland tumours and (2) compare its results with the diagnoses of junior and senior radiologists in order to evaluate its utility in routine practice. While automatic algorithms applied to parotid tumours classification have been developed in the past, we believe that our study is one of the first to leverage four different MRI sequences and propose a comparison with clinicians. In this study, we leverage data coming from a cohort of 134 patients treated for benign or malignant parotid tumours. Using radiomics extracted from the MR images of the gland, we train a random forest and a logistic regression to predict the corresponding histopathological subtypes. On the test set, the best results are given by the random forest: we obtain a 0.720 accuracy, a 0.860 specificity, and a 0.720 sensitivity over all histopathological subtypes, with an average AUC of 0.838. When considering the discrimination between benign and malignant tumours, the algorithm results in a 0.760 accuracy and a 0.769 AUC, both on test set. Moreover, the clinical experiment shows that our model helps to improve diagnostic abilities of junior radiologists as their sensitivity and accuracy raised by 6 % when using our proposed method. This algorithm may be useful for training of physicians. Radiomics with a machine learning algorithm may help improve discrimination between benign and malignant parotid tumours, decreasing the need for diagnostic surgery. Further studies are warranted to validate our algorithm for routine use.

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.

Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study

PURPOSE

To evaluate the effectiveness of MRI-based radiomics models in distinguishing between Warthin tumors (WT) and misdiagnosed or ambiguous pleomorphic adenoma (PA).

METHODS

Data of patients with PA and WT from two centers were collected. MR images were used to extract radiomic features. The optimal radiomics model was found by running nine machine learning algorithms after feature reduction and selection. To create a clinical model, univariate logistic regression (LR) analysis and multivariate LR were used. The independent clinical predictors and radiomics were combined to create a nomogram. Two integrated models were constructed by the ensemble and stacking algorithms respectively based on the clinical model and the optimal radiomics model. The models' performance was evaluated using the area under the curve (AUC).

RESULTS

There were 149 patients included in all. Gender, age, and smoking of patients were independent clinical predictors. With the greatest average AUC (0.896) and accuracy (0.839) in validation groups, the LR model was the optimal radiomics model. In the average validation group, the radiomics model based on LR did not have a higher AUC (0.795) than the clinical model (AUC = 0.909). The nomogram (AUC = 0.953) outperformed the radiomics model in terms of discrimination performance. The nomogram in the average validation group had a highest AUC than the stacking model (0.914) or ensemble model (0.798).

CONCLUSION

Misdiagnosed or ambiguous PA and WT can be non-invasively distinguished using MRI-based radiomics models. The nomogram exhibited excellent and stable diagnostic performance. In daily work, it is necessary to combine with clinical parameters for distinguishing between PA and WT.

Differential diagnosis of parotid gland tumors using apparent diffusion coefficient, texture features, and their combination

OBJECTIVES

Warthin's tumors (WT) and pleomorphic adenomas (PA) are the commonest parotid gland tumors; however, their differentiation remains difficult. This study aimed to investigate the utility of the apparent diffusion coefficient (ADC) value, texture features, and their combination for the differential diagnosis of parotid gland tumors.

METHODS

Patients who underwent magnetic resonance imaging (MRI) between April 2008 and March 2021 for parotid gland tumors were included and divided into two groups according to the tumor type: WT and PA. The tumor types were used as predictor variables, while the ADC value, texture features, and their combination were the outcome variables. Texture features were measured on short tau inversion recovery (STIR) images and selected using the Fisher's coefficient method and probability of error, and average correlation coefficients. The Mann-Whitney U-test was used to analyze bivariate statistics. Receiver operating characteristic curve analysis was used to assess the ability of the ADC value, texture features, and their combination to distinguishing between the two tumor types.

RESULTS

A total of 22 patients were included, 11 in each group. The ADC value, 10 texture features, and their combination were significantly different between the two groups (p < .001). Moreover, all three variables had high area under the curve values of 0.93-0.96.

CONCLUSION

The ADC value, texture features, and their combination demonstrated good diagnostic ability to distinguish between WTs and PAs. This method may be used to aid the differential diagnosis of parotid gland tumors, thereby promoting timely and adequate treatment.

Value of T2-weighted-based radiomics model in distinguishing Warthin tumor from pleomorphic adenoma of the parotid

OBJECTIVES

The differentiation of Warthin tumor and pleomorphic adenoma before treatment is crucial for clinical strategies. The aim of this study was to develop and test a T2-weighted-based radiomics model for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

METHODS

A total of 117 patients, including 61 cases of Warthin tumor and 56 cases of pleomorphic adenoma, were retrospectively enrolled from two centers between January 2010 and June 2022. The training set included 82 cases, and the validation set included 35 cases. From T2-weighted images, 971 radiomics features were extracted. Seven radiomics features remained after a two-step selection process. We used the seven radiomics features and clinical factors through multivariable logistic regression to build radiomics and clinical models, respectively. A radiomics-clinical model was also built that combined the independent clinical predictors with the radiomics features. Through ROC curves, the three models were evaluated and compared.

RESULTS

In the radiomics model, AUCs were 0.826 and 0.796 in training and validation sets, respectively. In the clinical model, the AUCs were 0.923 and 0.926 in the training and validation sets, respectively. Decision curve analysis revealed that the radiomics-clinical model had the best diagnostic performance for distinguishing Warthin tumor from pleomorphic adenoma of the parotid gland (AUC = 0.962 and 0.934 for the training and validation sets, respectively).

CONCLUSION

The radiomics-clinical model performed well in differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

KEY POINTS

• The clinical model outperformed the radiomics model in distinguishing pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics features extracted from T2-weighted images could help differentiate pleomorphic adenoma from Warthin tumor of the parotid gland. • The radiomics-clinical model was superior to the radiomics and the clinical models for differentiating pleomorphic adenoma from Warthin tumor of the parotid gland.

Predicting Lumbar Vertebral Osteopenia Using LvOPI Scores and Logistic Regression Models in an Exploratory Study of Premenopausal Taiwanese Women

Purpose

To propose hybrid predicting models integrating clinical and magnetic resonance imaging (MRI) features to diagnose lumbar vertebral osteopenia (LvOPI) in premenopausal women.

Methods

This prospective study enrolled 101 Taiwanese women, including 53 before and 48 women after menopause. Clinical information, including age, body height, body weight and body mass index (BMI), were recorded. Bone mineral density (BMD) was measured by the dual-energy X-ray absorptiometry. Lumbar vertebral fat fraction (LvFF) was measured by MRI. LvOPI scores (LvOPISs) comprising different clinical features and LvFF were constructed to diagnose LvOPI. Statistical analyses included normality tests, linear regression analyses, logistic regression analyses, group comparisons, and diagnostic performance. A P value less than 0.05 was considered as statistically significant.

Results

The post-menopausal women had higher age, body weight, BMI, LvFF and lower BMD than the pre-menopausal women (all P < 0.05). The lumbar vertebral osteoporosis group had significantly higher age, longer MMI, and higher LvFF than the LvOPI group (all P < 0.05) and normal group (all P < 0.005). LvOPISs (AUC, 0.843 to 0.864) outperformed body weight (0.747; P = 0.0566), BMI (0.737; P < 0.05), age (0.649; P < 0.05), and body height (0.5; P < 0.05) in diagnosing LvOPI in the premenopausal women. Hybrid predicting models using logistic regression analysis (0.894 to 0.9) further outperformed all single predictors in diagnosing LvOPI in the premenopausal women (P < 0.05).

Conclusion

The diagnostic accuracy of the LvOPI can be improved by using our proposed hybrid predicting models in Taiwanese premenopausal women.

Differentiation of Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Objectives

We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs).

Materials and Methods

This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models.

Results

The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967).

Conclusions

Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

Purpose

Differentiating Warthin tumor (WT) from pleomorphic adenoma (PA) is of primary importance due to differences in patient management, treatment and outcome. We sought to evaluate the performance of MRI-based radiomic features in discriminating PA from WT in the preoperative setting.

Methods

We retrospectively evaluated 81 parotid gland lesions (48 PA and 33 WT) on T2-weighted (T2w) images and 52 of them on post-contrast fat-suppressed T1-weighted (pcfsT1w) images. All MRI examinations were carried out on a 1.5-Tesla MRI scanner, and images were segmented manually using the software ITK-SNAP (www.itk-snap.org).

Results

The most discriminative feature on pcfsT1w images was GLCM_InverseVariance, yielding area under the curve (AUC), sensitivity and specificity of 0.9, 86 % and 87 %, respectively. Skewness was the feature extracted from T2w images with the highest specificity (88 %) in discriminating WT from PA.

Conclusion

Radiomic analysis could be an important tool to improve diagnostic accuracy in differentiating PA from WT.

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.

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.

Parotid Warthin's tumor: novel MR imaging score as diagnostic indicator

OBJECTIVES

Despite known characteristic radiologic and clinical features, differentiation between Warthin's tumor (WT) and other parotid tumors remains challenging. The purpose of this study was to more precisely assess the MR imaging features of WT and to develop a scoring system combining the most specific characteristics.

METHODS

A total of 208 patients with parotid gland tumors and presurgical MRI were included. Tumors were divided into 5 histological subtypes, and different MRI features were compared between groups. An MRI scoring test was developed including MR parameters that contributed significantly in distinguishing WT from other tumors.

RESULTS

The best MRI features for differentiating between WTs from other tumors included bilaterality (P = 0.002), multifocality (P < 0.001), ADC values <905.1 (P < 0.001), and high signal intensity on T1-W images (P < 0.001). Six or more points on the 14-point scoring MRI scale was associated with an area under the curve of 0.99 (Accuracy of 98%), while a cut-off value of 7 indicated 100% specificity and 100% positive predictive value.

CONCLUSIONS

Ill-defined margins, low T1-W signal, and location in the upper 2/3 of the parotid gland excluded WTs in 100% of cases. The proposed scoring method allows WTs to be distinguished from other tumors with high accuracy.

KEY POINTS

Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland

OBJECTIVE：

Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA.

METHODS AND MATERIALS

A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (n = 75; WT = 34, PMA = 41) and external test set (n = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model.

RESULTS

The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively.

CONCLUSIONS

The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.

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.

Cytopathology and diagnostics of Warthin's tumour

Warthin's tumour (WT) is a benign epithelial salivary tumour, one type of salivary adenoma. Histologically, WT is structured of two components, epithelial tissue that often lines cystic formations and lymphoid tissue in the tumour stroma. FNA is a reliable diagnostic approach in the diagnosis of salivary gland lesions allowing a highly accurate categorization of benign tumour-like lesions, benign tumours and malignant tumours. In the proposed Milan reporting system of salivary gland lesions, WT is categorized in the IVA group of benign neoplasms. Accurate cytological diagnosis is straightforward when three characteristic components are present: oncocytes, either isolated or associated in clusters, lymphocytes and lymphoid cells and often an inflammatory/necrotic-like substance. Also, specific features of scintigraphy and radiological imaging contribute to the diagnosis of WT. WT is categorized according to Seifert G. et al in 4 types, depending on the proportions of the epithelial component and lymphoid stroma. Differential cytopathological and pathohistological diagnosis include other salivary gland lesions with lymphoid, oncocytic epithelial and cystic components. In some cases, such as the metaplastic WT variant, there are additional cytopathological and histological diagnostic difficulties. Moreover, bilateral, multicentric or multiple and infrequently seen extra-salivary localizations of WT are associated with further cytopathological diagnostic difficulties. Also, a rare possibility of malignant transformation of the epithelial or lymphoid component of WT as well as possible association with other primary tumours remains a challenge in accurate cytopathological and histological diagnosis of WT.

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

Background

The differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.

Materials and Methods

A sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin’s tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin’s lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm.

Results

Models for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort.

Conclusions

Radiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.

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.

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.

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.

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.

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.

Imaging quality of PROPELLER diffusion-weighted MR imaging and its diagnostic performance in distinguishing pleomorphic adenomas from Warthin tumors of the parotid gland

The aim of this study was to evaluate the imaging quality and diagnostic performance of fast spin echo diffusion-weighted imaging with periodically rotated overlapping parallel lines with enhanced reconstruction (FSE-PROP-DWI) in distinguishing parotid pleomorphic adenoma (PMA) from Warthin tumor (WT). This retrospective study enrolled 44 parotid gland tumors from 34 patients, including 15 PMAs and 29 WTs with waived written informed consent. All participants underwent 1.5 T diffusion-weighted imaging including FSE-PROP-DWI and single-shot echo-planar diffusion-weighted imaging (SS-EP-DWI). After imaging resizing and registration among T2WI, FSE-PROP-DWI and SS-EP-DWI, imaging distortion was quantitatively analyzed by using the Dice coefficient. Signal-to-noise ratio and contrast-to-noise ratio were qualitatively evaluated. The mean apparent diffusion coefficient (ADC) of parotid gland tumors was calculated. Wilcoxon signed-rank test was used for paired comparison between FSE-PROP-DWI versus SS-EP-DWI. Mann-Whitney U test was used for independent group comparison between PMAs versus WTs. Diagnostic performance was evaluated by receiver operating characteristics curve analysis. P < 0.05 was considered statistically significant. The Dice coefficient was statistically significantly higher on FSE-PROP-DWI than SS-EP-DWI for both tumors (P < 0.005). Mean ADC was statistically significantly higher in PMAs than WTs on both FSE-PROP-DWI and SS-EP-DWI (P < 0.005). FSE-PROP-DWI and SS-EP-DWI successfully distinguished PMAs from WTs with an AUC of 0.880 and 0.945, respectively (P < 0.05). Sensitivity, specificity, positive predictive value, negative predictive value and accuracy in diagnosing PMAs were 100%, 69.0%, 62.5%, 100% and 79.5% for FSE-PROP-DWI, and 100%, 82.8%, 75%, 100% and 88.6% for SS-EP-DWI, respectively. FSE-PROP-DWI is useful to distinguish parotid PMAs from WTs with less distortion of tumors but lower AUC than SS-EP-DWI.

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

Background

Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution.

Methods

The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC).

Results

Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P<0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P<0.001).

Conclusions

Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.

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.

Effects of region of interest sizes on apparent diffusion coefficient measurements of pleomorphic adenoma, Warthin tumor, and normal parotid parenchyma

Background

Tumor apparent diffusion coefficient (ADC) measurements may be influenced by region of interest (ROI) sizes; however, this effect has not been systematically studied in parotid tumors. Our purpose was to determine the effects of ROI size on ADC measurements for the differentiation of pleomorphic adenoma (PA), Warthin tumor (WT), and normal parotid parenchyma.

Methods

Sixty-five patients including 37 with PA (lesions, n=37) and 28 with WT (lesions, n=36) were examined with diffusion-weighted imaging (DWI). Participants with normal contralateral parenchyma of the parotid gland constituted the control group (n=56). The mean ADC values and standard deviations (SDs) of the ADC (ADC_SD) of 12 concentric round ROIs (areas: 9, 28, 34, 50, 60, 82, 93, 98, 115, 130, 136, and 149 mm²) for tumors and normal tissue were measured by using custom-made software. Homogeneity index, which was defined by the ADC_SD/mean ADC, was also calculated. One-way repeated analyses of variance (ANOVAs) were performed on the mean ADCs, ADC_SDs, and homogeneity indices of the 12 ROIs in each group. The three parameters at different ROIs among PA, WT, and normal parotid parenchyma were compared using Kruskal-Wallis tests.

Results

There was excellent agreement for the ADC measurements with the 12 ROIs for PA [intraclass correlation coefficient (ICC), 0.98], WT (ICC, 0.99), and normal parotid parenchyma (ICC, 0.95). No significant differences were observed in the mean ADCs of the 12 ROIs for each of the three groups (P=0.744-0.990). Among the three groups, the mean ADC of normal parotid parenchyma [(0.94±0.003)×10^-3 mm²/s] was significantly lower than that of both PA [(1.72±0.01)×10^-3 mm²/s] and WT [(1.16±0.01)×10^-3 mm²/s] in the 12 ROIs, whereas the PA group had the highest mean ADC values. No significant differences were found in the mean ADC_SDs with each ROI between PA and WT (all P>0.017). PAs had lower homogeneity indices compared with WTs and normal parotid parenchyma (all P<0.01).

Conclusions

The effect of ROI size on ADC measurements could be excluded from the differentiation of PA, WT, and normal parotid parenchyma. Homogeneity index was a useful parameter in discriminating between the three groups.