Multiparametric Magnetic Resonance Imaging for the Diagnosis and Differential Diagnosis of Parotid Gland Tumors

Swin MoCo: Improving parotid gland MRI segmentation using contrastive learning

BACKGROUND

Segmentation of the parotid glands and tumors by MR images is essential for treating parotid gland tumors. However, segmentation of the parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures.

PURPOSE

The lack of large and well-annotated datasets limits the development of deep learning in medical images. As an unsupervised learning method, contrastive learning has seen rapid development in recent years. It can better use unlabeled images and is hopeful to improve parotid gland segmentation.

METHODS

We propose Swin MoCo, a momentum contrastive learning network with Swin Transformer as its backbone. The ImageNet supervised model is used as the initial weights of Swin MoCo, thus improving the training effects on small medical image datasets.

RESULTS

Swin MoCo trained with transfer learning improves parotid gland segmentation to 89.78% DSC, 85.18% mIoU, 3.60 HD, and 90.08% mAcc. On the Synapse multi-organ computed tomography (CT) dataset, using Swin MoCo as the pre-trained model of Swin-Unet yields 79.66% DSC and 12.73 HD, which outperforms the best result of Swin-Unet on the Synapse dataset.

CONCLUSIONS

The above improvements require only 4 h of training on a single NVIDIA Tesla V100, which is computationally cheap. Swin MoCo provides new approaches to improve the performance of tasks on small datasets. The code is publicly available at https://github.com/Zian-Xu/Swin-MoCo.

Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review

Purpose

A systematic review and meta-analysis were conducted to evaluate the diagnostic precision of radiomics in the differential diagnosis of parotid tumors, considering the increasing utilization of radiomics in tumor diagnosis. Although some researchers have attempted to apply radiomics in this context, there is ongoing debate regarding its accuracy.

Methods

Databases of PubMed, Cochrane, EMBASE, and Web of Science up to May 29, 2024 were systematically searched. The quality of included primary studies was assessed using the Radiomics Quality Score (RQS) checklist. The meta-analysis was performed utilizing a bivariate mixed-effects model.

Results

A total of 39 primary studies were incorporated. The machine learning model relying on MRI radiomics for diagnosis malignant tumors of the parotid gland, demonstrated a sensitivity of 0.80 [95% CI: 0.74, 0.86], SROC of 0.89 [95% CI: 0.27-0.99] in the validation set. The machine learning model based on MRI radiomics for diagnosis malignant tumors of the parotid gland, exhibited a sensitivity of 0.83[95% CI: 0.76, 0.88], SROC of 0.89 [95% CI: 0.17-1.00] in the validation set. The models also demonstrated high predictive accuracy for benign lesions.

Conclusion

There is great potential for radiomics-based models to improve the accuracy of diagnosing benign and malignant tumors of the parotid gland. To further enhance this potential, future studies should consider implementing standardized radiomics-based features, adopting more robust feature selection methods, and utilizing advanced model development tools. These measures can significantly improve the diagnostic accuracy of artificial intelligence algorithms in distinguishing between benign and malignant tumors of the parotid gland.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023434931.

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.

Evaluation of Quantitative Dual-Energy Computed Tomography Parameters for Differentiation of Parotid Gland Tumors

RATIONALE AND OBJECTIVES

To assess the diagnostic performance of quantitative parameters from dual-energy CT (DECT) in differentiating parotid gland tumors (PGTs).

MATERIALS AND METHODS

101 patients with 108 pathologically proved PGTs were enrolled and classified into four groups: pleomorphic adenomas (PAs), warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). Conventional CT attenuation and DECT quantitative parameters, including iodine concentration (IC), normalized iodine concentration (NIC), effective atomic number (Zeff), electron density (Rho), double energy index (DEI), and the slope of the spectral Hounsfield unit curve (λHU), were obtained and compared between benign tumors (BTs) and MTs, and further compared among the four subgroups. Logistic regression analysis was used to assess the independent parameters and the receiver operating characteristic (ROC) curves were used to analyze the diagnostic performance.

RESULTS

Attenuation, Zeff, DEI, IC, NIC, and λHU in the arterial phase (AP) and venous phase (VP) were higher in MTs than in BTs (p < 0.001-0.047). λHU in VP and Zeff in AP were independent predictors with an area under the curve (AUC) of 0.84 after the combination. Furthermore, attenuation, Zeff, DEI, IC, NIC, and λHU in the AP and VP of MTs were higher than those of PAs (p < 0.001-0.047). Zeff and NIC in AP and λHU in VP were independent predictors with an AUC of 0.93 after the combination. Attenuation and Rho in the precontrast phase; attenuation, Rho, Zeff, DEI, IC, NIC, and λHU in AP; and the Rho in the VP of PAs were lower than those of WTs (p < 0.001-0.03). Rho in the precontrast phase and attenuation in AP were independent predictors with an AUC of 0.89 after the combination. MTs demonstrated higher Zeff, DEI, IC, NIC, and λHU in VP and lower Rho in the precontrast phase compared with WTs (p < 0.001-0.04); but no independent predictors were found.

CONCLUSION

DECT quantitative parameters can help to differentiate PGTs.

Differentiation of salivary gland tumours using diffusion-weighted image-based virtual MR elastography: a pilot study

OBJECTIVES

Differentiation among benign salivary gland tumours, Warthin tumours (WTs), and malignant salivary gland tumours is crucial to treatment planning and predicting patient prognosis. However, differentiation of those tumours using imaging findings remains difficult. This study evaluated the usefulness of elasticity determined from diffusion-weighted image (DWI)-based virtual MR elastography (MRE) compared with conventional magnetic resonance imaging (MRI) findings in differentiating the tumours.

METHODS

This study included 17 benign salivary gland tumours, 6 WTs, and 11 malignant salivary gland tumours scanned on neck MRI. The long and short diameters, T1 and T2 signal intensities, tumour margins, apparent diffusion coefficient (ADC) values, and elasticity from DWI-based virtual MRE of the tumours were evaluated. The interobserver agreement in measuring tumour elasticity and the receiver operating characteristic (ROC) curves were also assessed.

RESULTS

The long and short diameters and the T1 and T2 signal intensities showed no significant difference among the 3 tumour groups. Tumour margins and the mean ADC values showed significant differences among some tumour groups. The elasticity from virtual MRE showed significant differences among all 3 tumour groups and the interobserver agreement was excellent. The area under the ROC curves of the elasticity were higher than those of tumour margins and mean ADC values.

CONCLUSION

Elasticity values based on DWI-based virtual MRE of benign salivary gland tumours, WTs, and malignant salivary gland tumours were significantly different. The elasticity of WTs was the highest and that of benign tumours was the lowest. The elasticity from DWI-based virtual MRE may aid in the differential diagnosis of salivary gland tumours.

Primary Malignant Melanoma in the Parotid Gland: A Case Report and Literature Review

Primary melanoma of the parotid gland is an extremely rare and challenging tumor with a poor prognosis, and its ultrasonic characteristics have yet to be reported. This article presents a case of a 77-year-old man with a left parotid mass that was confirmed as a melanoma following surgery. The ultrasonic features of melanoma were examined in detail, with a particular focus on their diagnostic value. Furthermore, we summarized the clinical characteristics, treatment options, and outcomes associated with primary melanoma of the parotid gland based on a thorough analysis of the available literature.

Diagnostic imaging of salivary gland cancers: REFCOR recommendations by the formal consensus method

OBJECTIVE

To define the indications for each imaging modality in the screening, characterization, extension and follow-up of salivary gland tumors.

MATERIAL AND METHODS

The French Network of Rare Head and Neck Tumors (REFCOR) formed a steering group who drafted a narrative review of the literature published on Medline and proposed recommendations. The level of adherence to the recommendations was then assessed by a rating group, according to the formal consensus method.

RESULTS

If a swelling of a salivary gland is palpable for 3 weeks, an ultrasound scan is recommended to confirm a tumoral lesion and rule out differential diagnoses. For a salivary gland tumor, MRI is recommended with diffusion-weighted and dynamic contrast-enhanced techniques. In the case of histologically proven malignancy or a highly suspicious lesion, a CT scan of the neck and chest is recommended to assess the tumor, lymph nodes and metastases. FDG-PET is not currently recommended in routine clinical practice for initial diagnosis, assessment of extension, evaluation of response to treatment, staging of recurrence, or follow-up of salivary gland tumors.

CONCLUSION

Assessing salivary tumors is based on MRI. Extension assessment is based on neck and chest CT.

Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model

Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images. A ResNet50 pretrained model is used for feature extraction. The first layer of the adopted stacked generalization model consists of multiple weak learners, and the results of the weak learners are integrated as input data in a meta-classifier in the second layer. The output results of the meta-classifier are the final classification results. The classification accuracy of the stacked generalization model reaches 91%. Comparing the classification results under different classifiers, the stacked generalization model used in this study can identify benign and malignant tumors in the parotid gland effectively, thus relieving physicians of tedious work pressure.

Amide proton transfer-weighted magnetic resonance imaging for the differentiation of parotid gland tumors

Purpose

To assess the usefulness of amide proton transfer-weighted (APTw) imaging in the differentiation of parotid gland tumors.

Materials and methods

Patients with parotid gland tumors who underwent APTw imaging were retrospectively enrolled and divided into groups according to pathology. Two radiologists evaluated the APTw image quality independently, and APTw images with quality score ≥3 were enrolled. The maximum and average values of APTw imaging for tumor lesions (APTmax and APTmean) were measured. The differences in APTmax and APTmean were compared between malignant tumors (MTs) and benign tumors (BTs), as well as between MTs and pleomorphic adenomas (PAs) and between MTs and Warthin tumors (WTs). Independent-samples t-test, Kruskal-Wallis H test, and receiver operating characteristic (ROC) curve analyses were used for statistical analysis.

Results

Seventy-three patients were included for image quality evaluation. In this study, 32/73 and 29/73 parotid tumors were scored as 4 and 3, respectively. After excluding lesions with quality score ≤2 (12/73), the APTmean and APTmax of MTs were 4.15% ± 1.33% and 7.43% ± 1.61%, higher than those of BTs 2.74% ± 1.04% and 5.25% ± 1.54%, respectively (p < 0.05). The areas under the ROC curve (AUCs) of the APTmean and APTmax for differentiation between MTs and BTs were 0.819 and 0.821, respectively. MTs indicated significantly higher APTmean and APTmax values than those of PAs (p < 0.05) and WTs (p < 0.05). The AUCs of the APTmean and APTmax for differentiation between MTs and PAs were 0.830 and 0.815 and between MTs and WTs were 0.847 and 0.920, respectively.

Conclusion

Most APTw images for parotid tumors had acceptable image quality for APTw value evaluation. Both APTmax and APTmean can be used to differentiate MTs from BTs and to differentiate MTs from subtype parotid gland tumors.

Current status of diffusion-weighted imaging in differentiating parotid tumors

Recently, diffusion-weighted imaging (DWI) is an essential magnetic resonance imaging (MRI) protocol for head and neck imaging in clinical practice as it plays an important role in lesion detection, tumor extension evaluation, differential diagnosis, therapeutic effect prediction, therapy evaluation, and recurrence diagnosis. Especially in the parotid gland, several studies have already attempted to achieve accurate differentiation between benign and malignant tumors using DWI. A conventional single-shot echo-planar-based DWI is widely used for head and neck imaging, whereas advanced DWI sequences, such as intravoxel incoherent motion, diffusion kurtosis imaging, periodically rotated overlapping parallel lines with enhanced reconstruction, and readout-segmented echo-planar imaging (readout segmentation of long variable echo-trains), have been used to characterize parotid tumors. The mean apparent diffusion coefficient values are easily measured and useful for assessing cellularity and histological characteristics, whereas advanced image analyses, such as histogram analysis, texture analysis, and machine and deep learning, have been rapidly developed. Furthermore, a combination of DWI and other MRI protocols has reportedly improved the diagnostic accuracy of parotid tumors. This review article summarizes the current state of DWI in differentiating parotid tumors.

CT-based radiomics with various classifiers for histological differentiation of parotid gland tumors

Objective

This study assessed whether radiomics features could stratify parotid gland tumours accurately based on only noncontrast CT images and validated the best classifier of different radiomics models.

Methods

In this single-centre study, we retrospectively recruited 249 patients with a diagnosis of pleomorphic adenoma (PA), Warthin tumour (WT), basal cell adenoma (BCA) or malignant parotid gland tumours (MPGTs) from June 2020 to August 2022. Each patient was randomly classified into training and testing cohorts at a ratio of 7:3, and then, pairwise comparisons in different parotid tumour groups were performed. CT images were transferred to 3D-Slicer software and the region of interest was manually drawn for feature extraction. Feature selection methods were performed using the intraclass correlation coefficient, t test and least absolute shrinkage and selection operator. Five common classifiers, namely, random forest (RF), support vector machine (SVM), logistic regression (LR), K-nearest neighbours (KNN) and general Bayesian network (Gnb), were selected to build different radiomics models. The receiver operating characteristic curve, area under the curve (AUC), accuracy, sensitivity, specificity and F-1 score were used to assess the prediction performances of these models. The calibration of the model was calculated by the Hosmer–Lemeshow test. DeLong’s test was utilized for comparing the AUCs.

Results

The radiomics model based on the RF, SVM, Gnb, LR, LR and RF classifiers obtained the highest AUC in differentiating PA from MPGTs, WT from MPGTs, BCA from MPGTs, PA from WT, PA from BCA, and WT from BCA, respectively. Accordingly, the AUC and the accuracy of the model for each classifier were 0.834 and 0.71, 0.893 and 0.79, 0.844 and 0.79, 0.902 and 0.88, 0.602 and 0.68, and 0.861 and 0.94, respectively.

Conclusion

Our study demonstrated that noncontrast CT-based radiomics could stratify refined pathological types of parotid tumours well but could not sufficiently differentiate PA from BCA. Different classifiers had the best diagnostic performance for different parotid tumours. Our study findings add to the current knowledge on the differential diagnosis of parotid tumours.

Value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging in differentiating parotid gland tumors

PURPOSE

This study aimed to explore the value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging (RESOLVE-DWI) for the differential diagnosis of parotid gland tumors.

METHODS

A total of 128 patients with histopathologically confirmed parotid gland tumors [86 benign tumors (BTs) and 42 malignant tumors (MTs)] were retrospectively recruited. BTs were further divided into pleomorphic adenomas (PAs, n = 57) and Warthin's tumors (WTs, n = 15). MRI examinations were performed before and after contrast injection to measure the longitudinal relaxation time (T1) value (T1p and T1e, respectively) and the apparent diffusion coefficient (ADC) value of the parotid gland tumors. The reduction in T1 (T1d) values and the percentage of T1 reduction (T1d%) were calculated.

RESULTS

The T1d and ADC values of the BTs were considerably higher than those of the MTs (all P <.05). The area under the curve (AUC) of the T1d and ADC values for differentiating between BTs and MTs of the parotid was 0.618 and 0.804, respectively (all P <.05). The AUC of the T1p, T1d, T1d%, and ADC values for differentiating between PAs and WTs was 0.926, 0.945, 0.925, and 0.996, respectively (all P >.05). The ADC and T1d% + ADC values performed better in differentiating between PAs and MTs than the T1p, T1d, and T1d% (AUC values: 0.902, 0.909, 0.660, 0.726, and 0.736, respectively). The T1p, T1d, T1d%, and T1d% + T1p values all had high diagnosis efficacy in differentiating WTs from MTs (AUC values: 0.865, 0.890, 0.852, and 0.897, respectively, all P >.05).

CONCLUSION

T1 mapping and RESOLVE-DWI can be used to differentiate parotid gland tumors quantitatively and can be complementary to each other.

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.

A Deep Learning Model for Classification of Parotid Neoplasms Based on Multimodal Magnetic Resonance Image Sequences

OBJECTIVE

To design a deep learning model based on multimodal magnetic resonance image (MRI) sequences for automatic parotid neoplasm classification, and to improve the diagnostic decision-making in clinical settings.

METHODS

First, multimodal MRI sequences were collected from 266 patients with parotid neoplasms, and an artificial intelligence (AI)-based deep learning model was designed from scratch, combining the image classification network of Resnet and the Transformer network of Natural language processing. Second, the effectiveness of the deep learning model was improved through the multi-modality fusion of MRI sequences, and the fusion strategy of various MRI sequences was optimized. In addition, we compared the effectiveness of the model in the parotid neoplasm classification with experienced radiologists.

RESULTS

The deep learning model delivered reliable outcomes in differentiating benign and malignant parotid neoplasms. The model, which was trained by the fusion of T2-weighted, postcontrast T1-weighted, and diffusion-weighted imaging (b = 1000 s/mm² ), produced the best result, with an accuracy score of 0.85, an area under the receiver operator characteristic (ROC) curve of 0.96, a sensitivity score of 0.90, and a specificity score of 0.84. In addition, the multi-modal paradigm exhibited reliable outcomes in diagnosing the pleomorphic adenoma and the Warthin tumor, but not in the identification of the basal cell adenoma.

CONCLUSION

An accurate and efficient AI based classification model was produced to classify parotid neoplasms, resulting from the fusion of multimodal MRI sequences. The effectiveness certainly outperformed the model with single MRI images or single MRI sequences as input, and potentially, experienced radiologists.

LEVEL OF EVIDENCE

3 Laryngoscope, 133:327-335, 2023.

Diagnostic efficacy of diffusion-weighted imaging and semiquantitative and quantitative dynamic contrast-enhanced magnetic resonance imaging in salivary gland tumors

BACKGROUND

Increased use of functional magnetic resonance imaging (MRI) methods such as diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI consisting of sequential contrast series, allows us to obtain more information on the microstructure, cellularity, interstitial distance, and vascularity of tumors, which has increased the discrimination power for benign and malignant salivary gland tumors (SGTs). In the last few years, quantitative DCE MRI data containing T1 perfusion parameters (K_trans, K_ep and V_e), were reported to contribute to the differentiation of benign or malignant subtypes in SGTs.

AIM

To evaluate the diagnostic efficacy of DWI and semiquantitative and quantitative perfusion MRI parameters in SGTs.

METHODS

Diffusion MRI [apparent diffusion coefficient (ADC) value] with a 1.5 T MR machine, semiquantitative perfusion MRI [time intensity curve (TIC) pattern], and quantitative perfusion MRI examinations (K_trans, K_ep and V_e) of 73 tumors in 67 patients with histopathological diagnosis performed from 2017 to 2021 were retrospectively evaluated. In the ADC value and semiquantitative perfusion MRI measurements, cystic components of the tumors were not considered, and the region of interest (ROI) was manually placed through the widest axial section of the tumor. TIC patterns were divided into four groups: Type A = T_peak > 120 s; type B = T_peak ≤ 120 s, washout ratio (WR) ≥ 30%; type C = T_peak ≤ 120 s, WR < 30%; and type D = flat TIC. For the quantitative perfusion MRI analysis, a 3D ROI was placed in the largest solid component of the tumor, and the K_trans, K_ep and V_e values were automatically generated.

RESULTS

The majority of SGTs were located in the parotid glands (86.3%). Of all the SGTs, 68.5% were benign and 31.5% were malignant. Significant differences were found for ADC values among pleomorphic adenomas (PMAs), Warthin's tumors (WTs), and malignant tumors (MTs) (P < 0.001). PMAs had type A and WTs had type B TIC pattern while the vast majority of MTs and other benign tumors (OBTs) (54.5% and 45.5%, respectively) displayed type C TIC pattern. PMAs showed no washout, while the highest mean WR was observed in WTs (59% ± 11%). K_trans values of PMAs, WTs, OBTs, and MTs were not significantly different. K_ep values of PMAs and WTs were significantly different from those of OBTs and MTs. Mean V_e value of WTs was significantly different from those of PMAs, OBTs, and MTs (P < 0.001).

CONCLUSION

The use of quantitative DCE parameters along with diffusion MRI and semiquantitative contrast-enhanced MRI in SGTs could improve the diagnostic accuracy.

Magnetic resonance imaging of parotid gland tumors: a pictorial essay

Imaging of parotid gland tumors is challenging due to the wide variety of differential diagnoses. Malignant parotid tumors can have very similar features to benign ones, such as slow growth and displacement instead of infiltration of neighboring structures. Malignant and benign tumors may therefore not be clinically distinguishable. Correct characterization of parotid tumors (i.e., benign or malignant) determines preoperative treatment planning and is important in optimizing the individualized surgical plan. Magnetic resonance imaging (MRI) is the imaging modality of choice for evaluation of suspected parotid gland lesions and differentiation between benign and malignant lesions. Certain conventional MRI features can suggest whether a mass is more likely to be a benign or low-grade malignancy or a high-grade malignancy and adding diffusion-weighted imaging or advanced MRI techniques like perfusion can aid in this distinction. Morphological features seen on MRI, such as low signal on T2-w, infiltrative changes or ill-defined margins, change over time and diffusion restriction can point to the malignant nature of the lesion. MRI is useful for detection and localization of the lesion(s), and associated findings like perineural spread of tumor, lymph node involvement and infiltrative changes of the surrounding tissues. In this pictorial essay, we present selected images of a variety of benign and malignant parotid tumors and emphasize the MRI features that may be useful in their characterization.

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.

Parotid metastases from primary lung cancer: Case series and systematic review of the features

Most parotid metastases have been reported to come from the head and neck; however, cases metastasized from the lung are extremely rare. Missed diagnoses and misdiagnoses occurred quite a few times. Thus, accurately identifying the clinical features of parotid metastasis of lung cancer is important. However, current studies about this issue are mostly case reports, and little is known about the detailed and systematic aspects. We reported three cases of parotid metastases from lung cancer and then systematically searched similar cases through “Pub-Med” and “Web of Science”. Finally, twenty-three patients were included in the study. Eighty-three percent of which were males, and 19 patients were over 50 years old. In all cases with smoking history mentioned, 93% were smokers. The predominant pathological type was small cell lung cancer (SCLC, 13 patients, 56%). Seventeen combined with other site metastasis, while more than half of which were brain metastases. The survival time ranged from 3months-17years, and as for SCLCs, it was only 3months-40months. It can be concluded that clinical features, such as sex, age, smoking history, pathological types, and metastasis patterns, could provide valuable evidence for diagnosis. The lung seems to be the most common primary site of parotid metastases except for head and neck tumors. The two circumstances, SCLC coexisting with Warthin’s tumor and parotid small cell carcinoma with lung metastasis, should be differentiated from parotid metastasis of lung cancer with caution For cases presented as SCLC, more aggressive strategies, such as chemotherapy with immunotherapy and maintenance therapy, may be more suitable. Due to the greater tendency of brain metastasis in such diseases, whole-brain radiation therapy, stereotactic radiosurgery or prophylactic cranial irradiation should be applied to corresponding patients in time. Additionally, lung cancer parotid metastases may be a marker of poor prognosis.

Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions

The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions (“leave-one-out CV” accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.

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.

Assessment of hyperbaric oxygenation treatment response in parotid glands by T 2 mapping following radiotherapy for head and neck tumours

Background

The study was designed to evaluate the influence of hyperbaric oxygenation therapy (HBOT) on the parotid gland in patients following radiotherapy for head and neck tumours.

Patients and methods

HBOT response was monitored by 3T magnetic resonance imaging (MRI) using T₂ mapping and subsequent measurement of mean T₂ and T₂ variability as well as by salivary tests (salivary flow, buffer capacity, and pH). Eighteen patients previously treated with irradiation doses between 50 and 80 Gy as well as 18 healthy gender and age matched controls were enrolled. MRI was performed prior to HBOT (40.2 ± 20 months after radiotherapy) and after 20 daily HBOT at 2.5 ATA (absolute atmosphere). Each HBOT consisted of breathing 100% oxygen for 90 minutes.

Results

Significant differences in mean T₂ prior to HBOT were observed between the ipsilateral irradiated (121 ± 20 ms), contralateral parotids (107 ± 21) and control group (96 ± 12 ms). A positive correlation in patients between T₂ variability and irradiation dose was detected in contralateral parotids before HBOT (R = 0.489, p = 0.0287). In addition, negative correlations were observed between mean T₂ in the ipsilateral as well as the contralateral gland and salivary flow before and after HBOT. Negative correlations between mean T₂, T₂ variability and pH of unstimulated saliva were also observed in the sides of parotid before and after HBOT.

Conclusions

The study confirmed that T₂ mapping had a potential for monitoring the differences between irradiated and normal parotid glands. It could also be useful in the assessment of the glandular tissue response to HBOT.

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.

Diffusion kurtosis imaging and dynamic contrast-enhanced MRI for the differentiation of parotid gland tumors

OBJECTIVE

To assess the usefulness of combined diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced MRI (DCE-MRI) in the differentiation of parotid gland tumors.

METHODS

Seventy patients with 80 parotid gland tumors who underwent DKI and DCE-MRI were retrospectively enrolled and divided into four groups: pleomorphic adenomas (PAs), Warthin tumors (WTs), other benign tumors (OBTs), and malignant tumors (MTs). DCE-MRI and DKI quantitative parameters were measured. The Kruskal-Wallis H test and post hoc test with Bonferroni correction and ROC curve were used for statistical analysis.

RESULTS

WTs demonstrated the highest K_ep value (median 1.89, interquartile range [1.46-2.31] min^-1) but lowest V_e value (0.20, [0.15-0.25]) compared with PAs (K_ep, 0.34 [0.21-0.55] min^-1; V_e, 0.36 [0.24-0.43]), OBTs (K_ep, 1.22 [0.27-1.67] min^-1; V_e, 0.28 [0.25-0.41]), and MTs (K_ep, 0.71 [0.50-1.23] min^-1; V_e, 0.35 [0.26-0.45]) (all p < .05). MTs had the lower D value (1.10, [0.88-1.29] × 10^-3 mm²/s) compared with PAs (1.81, [1.60-2.20] × 10^-3 mm²/s) and OBTs (1.57, [1.32-1.89] × 10^-3 mm²/s) (both p < .05). PAs had the lower K^trans value (0.12, [0.07-0.18] min^-1) compared with OBTs (0.28, [0.11-0.50] min^-1) (p < .05). The cutoff values of combined K_ep and V_e, D, and K^trans to distinguish WTs, MTs, and PAs sequentially were 1.06 min^-1, 0.28, 1.46 × 10^-3 mm²/s, and 0.21 min^-1, respectively (accuracy, 89% [71/80], 91% [73/80], 78% [62/80], respectively).

CONCLUSION

The combined use of DKI and DCE-MRI may help differentiate parotid gland tumors.

KEY POINTS

• The combined use of DKI and DCE-MRI could facilitate the understanding of the pathophysiological characteristics of parotid gland tumors. • A stepwise diagnostic diagram based on the combined use of DCE-MRI parameters and the diffusion coefficient is helpful for accurate preoperative diagnosis in parotid gland tumors and may further facilitate the clinical management of patients.

Evaluation of MR imaging findings differentiating parotid basal cell adenomas from other parotid tumors

PURPOSE

To evaluate the usefulness of magnetic resonance imaging (MRI) to differentiate basal cell adenomas (BCAs) from other parotid tumors.

METHOD

A total of 136 patients with histologically proven parotid gland tumors (13 BCAs, 66 pleomorphic adenomas [PAs], 30 Warthin tumors [WTs], and 27 parotid cancers [PCs]) who underwent a cervical MRI study between December 2011 and March 2019 were retrospectively enrolled. The MRI findings of the tumors were evaluated by two board-certified radiologists.

RESULTS

All 13 of the BCAs showed smooth margins, while 19 of the 27 PCs showed irregular margins (p < 0.0001). Eleven BCAs had some cystic components, and five were cyst-dominant. The BCAs had significantly more cystic components than the PAs (p = 0.0077). The mean apparent diffusion coefficient (ADC) value of the BCAs was 1.21 ± 0.20 × 10^-3 mm²/sec, which was equivalent to that of the PCs (1.12 ± 0.25 × 10^-3 mm²/sec, p = 0.76), significantly lower than that of the PAs (1.61 ± 0.32 × 10^-3 mm²/sec, p < 0.0001), and significantly higher than that of the WTs (0.81 ± 0.19 × 10^-3 mm²/sec, p = 0.0004). The plateau time-intensity curve (TIC) was the most common type for both BCAs and PCs, seen in 8 of 12 BCAs and 21 of 26 PCs, with no significant difference between these groups (p = 0.34).

CONCLUSIONS

BCA should be considered a possibility when a parotid lesion has smooth margins with an entire capsule and includes a cystic component, even if the TIC and diffusion-weighted MR images suggest a malignant pattern.

Influence of post-label delay time on the performance of 3D pseudo-continuous arterial spin labeling magnetic resonance imaging in the characterization of parotid gland tumors

OBJECTIVE

To evaluate the influence of post-label delay times (PLDs) on the performance of 3D pseudo-continuous arterial spin labeling (pCASL) magnetic resonance imaging for characterizing parotid gland tumors and to explore the optimal PLDs for the differential diagnosis.

MATERIALS AND METHOD

Fifty-eight consecutive patients with parotid gland tumors were enrolled, including 33 patients with pleomorphic adenomas (PAs), 16 patients with Warthin's tumors (WTs), and 9 patients with malignant tumors (MTs). 3D pCASL was scanned for each patient five times, with PLDs of 1025 ms, 1525 ms, 2025 ms, 2525 ms, and 3025 ms. Tumor blood flow (TBF) was calculated, and compared among different PLDs and tumor groups. Performance of TBF at different PLDs was evaluated using receiver operating characteristic analysis.

RESULTS

With an increasing PLD, TBF tended to gradually increase in PAs (p < 0.001), while TBF tended to slightly increase and then gradually decrease in WTs (p = 0.001), and PAs showed significantly lower TBF than WTs at all 5 PLDs (p < 0.05). PAs showed significantly lower TBF than MTs at 4 PLDs (p < 0.05), except at 3025 ms (p = 0.062). WTs showed higher TBF than MTs at all 5 PLDs; however, differences did not reach significance (p > 0.05). Setting a TBF of 64.350 mL/100g/min at a PLD of 1525 ms, or a TBF of 23.700 mL/100g/min at a PLD of 1025 ms as the cutoff values, optimal performance could be obtained for differentiating PAs from WTs (AUC = 0.905) or from MTs (AUC = 0.872).

CONCLUSIONS

Short PLDs (1025 ms or 1525 ms) are suggested to be used in 3D pCASL for characterizing parotid gland tumors in clinical practice.

KEY POINTS

• With 5 different PLDs, 3D pCASL can reflect the variation of blood flow in parotid gland tumors. • 3D pCASL is useful for characterizing PAs from WTs or MTs. • Short PLDs (1025 ms or 1525 ms) are suggested to be used in 3D pCASL for characterizing parotid gland tumors in clinical practice.

Differentiation of Benign and Malignant Parotid Gland Tumors with MRI and Diffusion Weighted Imaging

Objective

This study investigated the effectivity of Magnetic Resonance Imaging (MRI) findings and Apparent Diffusion Coefficient (ADC) value in evaluating parotid gland tumors (PGTs), and aimed to reduce the biopsy procedure before surgery.

Methods

This retrospective study included 54 PGTs of 42 patients' (24 female, 18 male, mean age; 51.4±15.9). All of the patients had an MRI, and histopathologic diagnosis. The signal intensity [T1 and T2 Weighted (W), T1W after intravenous contrast agent injection] and mean ADC values of the PGTs were measured. Also contrast enhancement pattern (homogenous, heterogeneous, peripheral or none), margin features (well or ill-defined), sizes, location (superficial lobe/deeplobe/both), perineural spread, presence of lymphadenopathy, and extension to adjacent structures were noted.

Results

The distribution of PGTs was; 21 pleomorphic adenomas, 18 Warthin tumors, 2 lymph nodes, 2 mucoepidermoid carcinomas, 5 adenoid cystic carcinoma, 1 basal cell carcinoma,2 metastases and 2 lymphomas; (13 malignant and 41 benign lesions). Morphologic parameters; ill-defined margin, perineural spread, lymphadenopathy, and extension to adjacent structures were found to be significantly associated with malign lesions (p<0.01). There was a significant difference between ADC values of malignant and benign PGTs (p<0.05). Also ADC values and T2 signal intensity was significantly lower in Warthin tumors rather than pleomorphic adenomas (p<0.05).

Conclusions

Mean ADC values when considered with morphological features may be accessible methods to distinguish benign and malignant PGTs, also ADC values and T2 signal intensity may be useful for differentiating pleomorphic adenomas from Warthin tumors, thereby reducing the number of biopsies and thus complications.

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.

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.

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.

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.

Texture analysis in the characterization of parotid salivary gland lesions: A study on MR diffusion weighted imaging

BACKGROUND AND PURPOSE

Parotid lesions show overlaps of morphological findings, apparent diffusion coefficient (ADC) values and types of time/intensity curve. This research aimed to evaluate the role of diffusion weighted imaging texture analysis in differentiating between benign and malignant parotid lesions and in characterizing pleomorphic adenoma (PA), Warthin tumor (WT), epithelial malignancy (EM), and lymphoma (LY).

METHODS

Texture analysis of 54 parotid lesions (19 PA, 14 WT, 14 EM, and 7 LY) was performed on ADC map images. An ANOVA test was used to estimate both the difference between benign and malignant lesions and the texture feature differences among PA, WT, EM, and LY. A P-value≤0.01 was considered to be statistically significant. A cut-off value defined by ROC curve analysis was found for each statistically significant texture parameter. The diagnostic accuracy was obtained for each texture parameter with AUC ≥ 0.5. The agreement between each texture parameter and histology was calculated using the Cohen's kappa coefficient.

RESULTS

The mean kappa values were 0.61, 0.34, 0.26, 0.17, and 0.48 for LY, EM, WT, PA, and benign vs. malignant lesions respectively. Long zone emphasis cut-off values >1.870 indicated EM with an accuracy of 81 % and values >2.630 revealed LY with an accuracy of 93 %. Long run emphasis values >1.050 and >1.070 indicated EM and LY with a diagnostic accuracy of 79% and 93% respectively.

CONCLUSIONS

Long zone emphasis and long run emphasis texture parameters allowed the identification of LY and the differentiation between benign and malignant lesions. WT and PA were not accurately recognized.

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.

Intravoxel Incoherent Motion Magnetic Resonance Imaging for Assessing Parotid Gland Tumors: Correlation and Comparison with Arterial Spin Labeling Imaging

Objective

To compare and correlate the findings of intravoxel incoherent motion (IVIM) magnetic resonance (MR) imaging and arterial spin labeling (ASL) imaging in characterizing parotid gland tumors.

Materials and Methods

We retrospectively reviewed 56 patients with parotid gland tumors evaluated by MR imaging. The true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and fraction of perfusion (f) values of IVIM imaging and tumor-to-parotid gland signal intensity ratio (SIR) on ASL imaging were calculated. Spearman rank correlation coefficient, chi-squared, Mann-Whitney U, and Kruskal-Wallis tests with the post-hoc Dunn-Bonferroni method and receiver operating characteristic curve assessments were used for statistical analysis.

Results

Malignant parotid gland tumors showed significantly lower D than benign tumors (p = 0.019). Within subgroup analyses, pleomorphic adenomas (PAs) showed significantly higher D than malignant tumors (MTs) and Warthin's tumors (WTs) (p < 0.001). The D* of WTs was significantly higher than that of PAs (p = 0.031). The f and SIR on ASL imaging of WTs were significantly higher than those of MTs and PAs (p < 0.05). Significantly positive correlation was found between SIR on ASL imaging and f (r = 0.446, p = 0.001). In comparison with f, SIR on ASL imaging showed a higher area under curve (0.853 vs. 0.891) in discriminating MTs from WTs, although the difference was not significant (p = 0.720).

Conclusion

IVIM and ASL imaging could help differentiate parotid gland tumors. SIR on ASL imaging showed a significantly positive correlation with f. ASL imaging might hold potential to improve the ability to discriminate MTs from WTs.

Added value of susceptibility-weighted imaging to diffusion-weighted imaging in the characterization of parotid gland tumors

PURPOSE

To assess the added value of susceptibility-weighted imaging (SWI) to diffusion-weighted imaging (DWI) in the characterization of parotid gland tumors.

METHODS

Seventy-eight patients with pathologically confirmed parotid gland tumors, who underwent DWI and SWI for pre-surgery evaluation, were enrolled. Apparent diffusion coefficient (ADC) and degree of intratumoral susceptibility signal intensity (ITSS) were measured and compared between benign and malignant groups, and among pleomorphic adenoma (PA), Warthin tumor (WT) and malignant tumor (MT). Independent sample t test, one-way analysis of variance and receiver operating characteristic curve analysis were used for statistical analyses.

RESULTS

Benign parotid gland tumor showed a significantly higher mean ADC value than malignant tumors (0.836 ± 0.350 vs 0.592 ± 0.163, p = 0.001). Setting an average ADC value of 0.679 as the cut-off value, optimal differentiating performance could be obtained (AUC, 0.700; sensitivity, 62.69%; specificity, 81.82%) for differentiating malignant from benign tumors. PA showed significantly higher mean ADC and less ITSS than WT (ADC, p < 0.001; ITSS, p = 0.033) and MT (ADC, p < 0.001; ITSS, p = 0.024), while the difference between WT and MT was not significant (ADC, p = 0.826; ITSS, p = 0.539). After integration with ITSS, the diagnostic performance of ADC was improved for differentiating PA from WT (AUC 0.921 vs 0.873) and from MT (AUC 0.906 vs 0.882).

CONCLUSION

SWI could provide added information to DWI and serve as a supplementary imaging marker for the characterization of parotid gland tumors.