Diffusion-weighted echo-planar MR imaging of primary parotid gland tumors: is a prediction of different histologic subtypes possible?

Update on the Treatment of Salivary Gland Carcinomas

Salivary gland carcinoma is a rare form of head and neck carcinoma, but it comprises a variety of subsites and histologic subtypes that each present with unique clinical courses and management challenges. Preoperative work-up generally consists of fine-needle aspiration cytology and MRI. However, because of the large variety of subtypes, there are often challenges obtaining a histologic diagnosis before surgery. Upfront surgery at the primary site leads to the greatest improvement in survival. Posttreatment surveillance of these patients is important. This article discusses some of the current controversies in the management of salivary gland carcinomas.

Correlation between MRI (DWI and DCE) and cellularity of parotid gland pleomorphic adenomas

PURPOSE

Parotid pleomorphic adenomas present a risk of recurrence, higher when the tumour is a hypocellular subtype. The aim of the study was to determine whether it is possible to characterize this histological subtype with diffusion and perfusion sequences of the preoperative MRI.

METHODS

This retrospective study included 97 patients operated between 2010 and 2020. Histologic slides review was performed to classify tumours into three histologic subtypes: hypocellular, classical and hypercellular. Univariate and multivariate analyses studied the correlation between histology and diffusion and perfusion MRI parameters obtained with OleaSphere® software.

RESULTS

The hypocellular subtype had higher apparent diffusion coefficient values than the other two subtypes: 2.13 ± 0.23, 1.83 ± 0.42, and 1.61 ± 0.4 × 10-3 mm2/s for hypocellular, classical and hypercellular subtype respectively (p < 0.0001). Multivariate analysis showed that an ADCmean > 1.88 × 10-3 mm2/s was suggestive of a hypocellular pleomorphic adenoma in 79% of the cases, with a specificity and PPV of 94 and 96% (p < 0.001), respectively.

CONCLUSION

The histological subtype of a pleomorphic adenoma can be predicted preoperatively with ADC values. A prospective and multicentric study on a larger cohort is needed to confirm our results.

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.

Deep Learning Reconstruction for DWIs by EPI and FASE Sequences for Head and Neck Tumors

BACKGROUND

Diffusion-weighted images (DWI) obtained by echo-planar imaging (EPI) are frequently degraded by susceptibility artifacts. It has been suggested that DWI obtained by fast advanced spin-echo (FASE) or reconstructed with deep learning reconstruction (DLR) could be useful for image quality improvements. The purpose of this investigation using in vitro and in vivo studies was to determine the influence of sequence difference and of DLR for DWI on image quality, apparent diffusion coefficient (ADC) evaluation, and differentiation of malignant from benign head and neck tumors.

METHODS

For the in vitro study, a DWI phantom was scanned by FASE and EPI sequences and reconstructed with and without DLR. Each ADC within the phantom for each DWI was then assessed and correlated for each measured ADC and standard value by Spearman's rank correlation analysis. For the in vivo study, DWIs obtained by EPI and FASE sequences were also obtained for head and neck tumor patients. Signal-to-noise ratio (SNR) and ADC were then determined based on ROI measurements, while SNR of tumors and ADC were compared between all DWI data sets by means of Tukey's Honest Significant Difference test.

RESULTS

For the in vitro study, all correlations between measured ADC and standard reference were significant and excellent (0.92 ≤ ρ ≤ 0.99, p < 0.0001). For the in vivo study, the SNR of FASE with DLR was significantly higher than that of FASE without DLR (p = 0.02), while ADC values for benign and malignant tumors showed significant differences between each sequence with and without DLR (p < 0.05).

CONCLUSION

In comparison with EPI sequence, FASE sequence and DLR can improve image quality and distortion of DWIs without significantly influencing ADC measurements or differentiation capability of malignant from benign head and neck tumors.

Differentiation of submandibular sialadenitis based on apparent diffusion coefficient

OBJECTIVES

This study aimed to quantify the submandibular gland in suppurative sialadenitis, primary Sjögren's syndrome (pSS) and radiation-induced sialadenitis using the apparent diffusion coefficient (ADC) for differential diagnosis.

SUBJECTS AND METHODS

This retrospective study included 16 patients with suppurative sialadenitis (n = 9), pSS (n = 3) and radiation-induced sialadenitis (n = 4) who underwent magnetic resonance imaging between June 2006 and May 2022. The ADC of the submandibular glands in each state was calculated, and the differences were analysed using a one-way analysis of variance and Tukey's post hoc test. Receiver operating characteristic curves were used to assess the ability of the ADC to distinguish each condition. Statistical significance was set at p < 0.05.

RESULTS

The mean ADC value (×10-3 mm2/s) ± standard deviation in the control (non-affected side of the suppurative sialadenitis group), suppurative sialadenitis, pSS and radiation-induced groups were 0.94 ± 0.16, 1.24 ± 0.16, 1.33 ± 0.13 and 1.5 ± 0.12, respectively (p < 0.001). The diagnostic value for distinguishing each group was ≥0.75.

CONCLUSION

ADC values are useful for quantitatively assessing and distinguishing submandibular glands in suppurative sialadenitis, primary Sjögren's syndrome and radiation-induced sialadenitis.

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.

Characterization of parotid gland tumors: Whole-tumor histogram analysis of diffusion weighted imaging, diffusion kurtosis imaging, and intravoxel incoherent motion - A pilot study

PURPOSE

To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors.

METHOD

From December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman's correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index.

RESULTS

For diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05).

CONCLUSIONS

Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Usefulness of apparent diffusion coefficient values for distinguishing between squamous cell carcinoma and malignant salivary gland tumor of the head and neck

PURPOSE

The aim of the study was to assess the usefulness of apparent diffusion coefficient (ADC) values for differentiating between SCC and malignant salivary gland tumors of the head and neck.

MATERIALS AND METHODS

This retrospective cross-sectional study was conducted on 29 patients with SCCs and 10 patients with malignant salivary gland tumors who underwent pretreatment MRI of the head and neck. The minimum and average ADC values of the tumors were measured, with normalized tumor to spinal cord ADC ratios generated. Comparisons of ADC values and normalized ADC ratios between the two tumor types were performed using the unpaired t-test.

RESULTS

The minimum and average ADC values and normalized average ADC ratios of SCCs (753.17 ± 214.47 × 10-6 mm2/s, 848.79 ± 250.13 × 10-6 mm2/s, and 0.92 ± 0.25, respectively) were significantly lower than those of malignant salivary gland tumors (1084.90 ± 242.60 × 10-6 mm2/s, 1305.90 ± 270.99 × 10-6 mm2/s, and 1.58 ± 0.31, respectively; all p < 0.001). A cutoff value of 1.31 for the normalized average ADC ratio was used to distinguish between SCCs and malignant salivary gland tumors and achieved an area under the curve of 0.93, sensitivity of 96.6%, specificity of 90%, and accuracy of 94.6%.

CONCLUSION

ADC value measurement could help differentiate between SCCs and malignant salivary gland tumors.

Adding automated decision-tree models to multiparametric MRI for parotid tumours improves clinical performance

PURPOSE

Therapeutic management of parotid gland tumours depends on their histological type. To aid its characterisation, we sought to develop automated decision-tree models based on multiparametric magnetic resonance imaging (MRI) parameters and to evaluate their added diagnostic value compared with morphological sequences.

METHODS

206 MRIs from 206 patients with histologically proven parotid gland tumours were included from January 2009 to January 2018. Multiparametric MRI findings (including parameters derived from diffusion-weighted imaging [DWI] and dynamic contrast-enhanced [DCE]) were used to build predictive classification and regression tree (CART) models for each histological type. All MRIs were read twice: first, based on morphological sequence findings only, and second, with the addition of multiparametric sequences and CART findings. The diagnostic performance between these two readings was compared using ROC curves.

RESULTS

Compared to morphological sequences alone, the addition of multiparametric analysis significantly increased the diagnostic performance for all histological types (p < 0.001 to p = 0.011), except for lymphomas, where the increase was not significant (AUC 1.00 vs. 0.99, p = 0.066). ADCmean was the best parameter to identify pleomorphic adenomas, carcinomas and lymphomas with respective cut-offs of 1.292 × 10-3 mm2/s, 1.181 × 10-3 mm2/s and 0.611 × 10-3 mm2/s, respectively. × 10-3 mm2/s. The mean extracellular-extravascular space coefficient was the best parameter to Warthin tumours from the others, with a cut-off of 0.07.

CONCLUSIONS

The addition of decision tree prediction models based on multiparametric sequences improves the non-invasive diagnostic performance of parotid gland tumours. ADC and extracellular-extravascular space coefficient are the two best parameters for decision making.

Imaging of Facial Nerve With 3D-DESS-WE-MRI Before Parotidectomy: Impact on Surgical Outcomes

OBJECTIVE

The intra-parotid facial nerve (FN) can be visualized using three-dimensional double-echo steady-state water-excitation sequence magnetic resonance imaging (3D-DESS-WE-MRI). However, the clinical impact of FN imaging using 3D-DESS-WE-MRI before parotidectomy has not yet been explored. We compared the clinical outcomes of parotidectomy in patients with and without preoperative 3D-DESS-WE-MRI.

MATERIALS AND METHODS

This prospective, non-randomized, single-institution study included 296 adult patients who underwent parotidectomy for parotid tumors, excluding superficial and mobile tumors. Preoperative evaluation with 3D-DESS-WE-MRI was performed in 122 patients, and not performed in 174 patients. FN visibility and tumor location relative to FN on 3D-DESS-WE-MRI were evaluated in 120 patients. Rates of FN palsy (FNP) and operation times were compared between patients with and without 3D-DESS-WE-MRI; propensity score matching (PSM) and inverse probability of treatment weighting (IPTW) were used to adjust for surgical and tumor factors.

RESULTS

The main trunk, temporofacial branch, and cervicofacial branch of the intra-parotid FN were identified using 3D-DESS-WE-MRI in approximately 97.5% (117/120), 44.2% (53/120), and 25.0% (30/120) of cases, respectively. The tumor location relative to FN, as assessed on magnetic resonance imaging, concurred with surgical findings in 90.8% (109/120) of cases. Rates of temporary and permanent FNP did not vary between patients with and without 3D-DESS-WE-MRI according to PSM (odds ratio, 2.29 [95% confidence interval {CI} 0.64-8.25] and 2.02 [95% CI: 0.32-12.90], respectively) and IPTW (odds ratio, 1.76 [95% CI: 0.19-16.75] and 1.94 [95% CI: 0.20-18.49], respectively). Conversely, operation time for surgical identification of FN was significantly shorter with 3D-DESS-WE-MRI (median, 25 vs. 35 min for PSM and 25 vs. 30 min for IPTW, P < 0.001).

CONCLUSION

Preoperative FN imaging with 3D-DESS-WE-MRI facilitated anatomical identification of FN and its relationship to the tumor during parotidectomy. This modality reduced operation time for FN identification, but did not significantly affect postoperative FNP rates.

Imaging of Major Salivary Gland Lesions and Disease

Infectious and inflammatory disorders are the commonest pathologies to affect the major salivary glands however frequently overlap in clinical presentation. Imaging plays an important role in diagnosis, usually initially performed by CT or ultrasound. MRI, with its superior soft-tissue characterization compared with CT, provides a better evaluation of tumors and tumor-like conditions. Imaging features may suggest that a mass is more likely to be benign versus malignant, however, biopsy is often needed to establish a definitive histopathologic diagnosis. Imaging plays a key role in the staging of neoplastic disease.

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.

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.

Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors

PURPOSE

Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors.

METHODS

One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast-enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad-score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad-score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model.

RESULTS

The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80-0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77-1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96-1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88-1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88-0.99) in the training cohort (p < 0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model.

CONCLUSIONS

The CT-based radiomics nomogram combining Rad-score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.

Discriminating between benign and malignant salivary gland tumors using diffusion-weighted imaging and intravoxel incoherent motion at 3 Tesla

PURPOSE

The purpose of this study was to retrospectively evaluate the diagnostic performances of diffusion-weighted imaging (DWI) and intravoxel incoherent motion (IVIM) for discriminating between benign and malignant salivary gland tumors (SGTs).

MATERIALS AND METHODS

Sixty-seven patients with 71 SGTs who underwent MRI examination at 3 Tesla were included. There were 34 men and 37 women with a mean age of 57 ± 17 (SD) years (age range: 20-90 years). SGTs included 21 malignant tumors (MTs) and 50 benign SGTs (33 pleomorphic adenomas [PAs] and 17 Warthin's tumors [WTs]). For each SGT, DWI and IVIM parameters, mean, skewness, and kurtosis of apparent diffusion coefficient (ADC), pure diffusion coefficient (D), pseudo-diffusion coefficient (D*) and perfusion volume fraction (f) were calculated and further compared between SGTs using univariable analysis. Areas under the curves (AUC) of receiver operating characteristic of significant parameters were compared using the Delong test.

RESULTS

Significant differences in ADC_mean, D_mean and D*_mean were found between SGTs (P < 0.001). The highest AUC values were obtained for ADC_mean (0.949) for identifying PAs and D*_mean (0.985) for identifying WTs and skewness and kurtosis did not outperform mean. To discriminate benign from malignant SGTs with thresholds set to maximize Youden index, IVIM and DWI produced accuracies of 85.9% (61/71; 95% CI: 75.6-93.0) and 77.5% (55/71; 95% CI: 66.0-86.5) but misdiagnosed MTs as benign in 28.6% (6/21) and 61.9% (13/21) of SGTs, respectively. After maximizing specificity to 100% for benign SGTs, the accuracies of IVIM and DWI decreased to 76.1% (54/71; 95% CI: 64.5-85.4) and 64.8% (46/71; 95% CI: 52.5-75.8) but no MTs were misdiagnosed as benign. IVIM and DWI correctly diagnosed 66.0% (33/50) and 50.0% (25/50) of benign SGTs and 46.5% (33/71) and 35.2% (25/71) of all SGTs, respectively.

CONCLUSION

IVIM is more accurate than DWI for discriminating between benign and malignant SGTs because of its advantage in detecting WTs. Thresholds set by maximizing specificity for benign SGTs may be advantageous in a clinical setting.

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.

Adenoid cystic carcinoma of the parotid gland

Adenoid cystic carcinoma (ACC) is a slowly progressing malignant tumor of the salivary glands that mostly affects minor salivary glands. ACC of parotid gland is exceptionally rare. In this article, we aimed to provide a case report of parotid gland ACC in a 55-year-old female that was misdiagnosed as benign mixed tumor. The patient was successfully treated by surgery without any complications. We recommend that ACC of parotid gland should be considered in differential diagnosis of benign mixed tumor since there still existed overlapped imaging characteristics.

Advanced Techniques in Head and Neck Cancer Imaging: Guide to Precision Cancer Management

Precision treatment requires precision imaging. With the advent of various advanced techniques in head and neck cancer treatment, imaging has become an integral part of the multidisciplinary approach to head and neck cancer care from diagnosis to staging and also plays a vital role in response evaluation in various tumors. Conventional anatomic imaging (CT scan, MRI, ultrasound) remains basic and focuses on defining the anatomical extent of the disease and its spread. Accurate assessment of the biological behavior of tumors, including tumor cellularity, growth, and response evaluation, is evolving with recent advances in molecular, functional, and hybrid/multiplex imaging. Integration of these various advanced diagnostic imaging and nonimaging methods aids understanding of cancer pathophysiology and provides a more comprehensive evaluation in this era of precision treatment. Here we discuss the current status of various advanced imaging techniques and their applications in head and neck cancer imaging.

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.

Differentiation of lacrimal gland tumors using the multi-model MRI: classification and regression tree (CART)-based analysis

BACKGROUND

Little is known about the value of dynamic contrast-enhanced (DCE) in combination with diffusion-weighted imaging (DWI) for the differentiation of lacrimal gland tumors.

PURPOSE

To evaluate the ability of DCE and DWI in differentiating lacrimal gland tumors.

MATERIAL AND METHODS

DCE and DWI were performed in 72 patients with lacrimal gland tumors. Time-intensity curve (TIC) patterns were categorized as type A, type B, type C, and type D. Apparent diffusion coefficient (ADC) was measured on DWI. Then, the diagnostic effectiveness of TIC in conjunction with ADC was assessed using classification and regression tree (CART) analysis.

RESULTS

Type A tumors were all epithelial; they could be further separated into pleomorphic adenoma sand carcinomas. Type B tumors were all non-epithelial tumors, which could be further separated into benign inflammatory infiltrates (BIIs) and lymphomas. Type C tumors contained both carcinomas and non-epithelial tumors, which could be diagnosed into carcinomas, BIIs and lymphomas. Type D tumors were all PAs. The mean ADC of epithelial tumors was significantly higher than that of non-epithelial tumors, and the mean ADC values were significantly different between PAs and carcinomas. Besides, the mean ADC value of BIIs was higher than that of lymphomas. Therefore, the CART decision tree made by ADC and TIC had a predictive accuracy of 86.1%, differentiating lacrimal gland tumors effectively.

CONCLUSION

Combined DCE and DWI-MRI can efficiently differentiate lacrimal gland tumors which can be of help to ophthalmologists in the diagnosis and treatment of these tumors.

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.

MRI findings of epithelial-myoepithelial carcinoma of the parotid gland with radiologic-pathologic correlation

PURPOSE

This study aimed to describe the MRI findings of epithelial-myoepithelial carcinoma (EMC) of the parotid gland.

MATERIALS AND METHODS

Seven patients (four males and three females) aged 40-86 years (mean age, 64 years) with histologically proven EMC of the parotid gland who underwent surgical resection after preoperative MRI were enrolled. MRI images were retrospectively reviewed and contrasted with pathological findings.

RESULTS

Five patients (71%) had predominantly solid lesions, and two (29%) had predominantly cystic lesions. All seven lesions had well-demarcated margins and capsules without the invasion of adjacent structures. The capsules were incomplete in five lesions (71%) and complete in two (29%). Four lesions (57%) exhibited a multinodular structure with internal septa. Cystic components were observed in three lesions (43%). On T1-weighted images, the solid components were frequently homogeneous (5/7, 71%), and demonstrated isointensity in five lesions (71%) and hypointensity in two (29%) relative to the spinal cord. On T2-weighted images, the solid components were usually heterogeneous (6/7, 86%), and demonstrated hyperintensity in five lesions (71%) and isointensity in two (29%) relative to the spinal cord. The mean apparent diffusion coefficient value of the solid components was 0.967 × 10-3 mm2/s.

CONCLUSION

Parotid gland EMCs usually appeared as predominantly solid lesions with well-demarcated margins and capsules. A multinodular structure with internal septa was characteristics of EMCs.

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.

Unusual presentation of parotid gland adenoid cystic carcinoma : A case presentation and literature review

Adenoid cystic carcinoma (ACC) represents approximately 10% of all epithelial salivary neoplasms and most commonly involves the parotid gland. We report CT and MRI finding of a 38-year-old young man presented to our ENT department with 02 years history of an external auditory canal stenosis. Physical examination revealed bilateral parotid gland swelling with a complete stenosis of the left external auditory canal. Temporal bone contrast enhanced CT-SCAN revealed is an isodense enhancing mass measuring 4 cm involving posterior and inferior external auditory canal wall, and invading the superficial lobe of the homolateral parotid gland. No bone erosion was noted. MRI investigation has shown a tumor process highly suspicious of malignancy centered on the left EAC involving the superficial lobe of the homolateral parotid gland. Adenoid cystic carcinoma (ACC) of the parotid gland was the final diagnosis, after surgical biopsy and histopathological examination. The Pre-operative check-up demonstrated multiple round shape lung lesions suggestive of multiple metastases. Considering the metastatic stage of the tumor, a collegial decision to adopt a palliative treatment approach based on chemotherapy was taken by the multidisciplinary oncology board.

Multifrequency magnetic resonance elastography-based tomoelastography of the parotid glands-feasibility and reference values

OBJECTIVES

Accurate radiological differentiation of parotid tumors remains challenging despite recent technical advances in quantitative medical imaging. Multifrequency magnetic resonance elastography (MRE) could provide additional information on viscoelastic properties of normal and abnormal biological tissues. This study investigates the feasibility of MRE of the parotid glands in healthy participants and provides first reference values.

METHODS

20 healthy participants underwent multifrequency MRE of both parotid glands at 3 Tesla. Shear waves at frequencies of 25, 30, 40, and 50 Hz were introduced into the participants' heads through the occiput using pressurized-air actuators. Shear wave speed (SWS) and loss angle of the shear modulus (φ) were reconstructed by tomoelastography post-processing as surrogate parameters for tissue stiffness and viscosity or fluidity. 10 participants underwent repeated MRE to determine test-retest reliability based on intraclass correlation coefficients.

RESULTS

All MRE datasets acquired could be included in the analysis. Mean SWS was 0.97 ± 0.13 m/s, and mean φ was 0.59 ± 0.05 rad, each for both sides combined and without notable lateral difference (p = 0.88/0.87). Test-retest reliability was good for SWS (ICC = 0.84 for both sides/ICC = 0.77 for the right side/ICC = 0.79 for the left side) and good to excellent for φ(ICC = 0.94/0.86/0.90).

CONCLUSIONS

Multifrequency MRE of the parotid glands is feasible and reliable. This technique, therefore, is a promising method for investigating the viscoelastic properties of salivary gland tumors in future studies.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

MR Imaging of Salivary Gland Tumors

Neoplasms of the salivary glands are characterized by their marked histologic diversity giving them nonspecific imaging findings. MR imaging is the best imaging modality to evaluate salivary gland tumors. Multiparametric MR imaging combines conventional imaging features, diffusion-weighted imaging, and perfusion imaging to help distinguish benign and low-grade neoplasms from malignant tumors; however, a biopsy is often needed to establish a definitive histopathologic diagnosis. An awareness of potential imaging pitfalls is important to prevent mistakes in salivary neoplasm imaging.

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.

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

Application of Diffusion Kurtosis Imaging and Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Differentiating Benign and Malignant Head and Neck Lesions

BACKGROUND

Preoperative differentiation of head and neck lesions is important for treatment plan selection.

PURPOSE

To evaluate the diagnostic value of diffusion kurtosis imaging (DKI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in differentiating benign from malignant head and neck lesions and subgroups, including lymphoma subgroup (LS), Warthin's tumor subgroup (WS), malignant tumor subgroup (excluding lymphoma) (MTS), and benign tumor subgroup (excluding Warthin's tumor) (BTS).

STUDY TYPE

Retrospective.

POPULATION

Seventy-four patients with 79 head and neck lesions (44 benign, 35 malignant), divided into four subgroups: LS (14), WS (12), MTS (21), and BTS (32).

FIELD STRENGTH/SEQUENCES

A 3.0 T, single-shot echo-planar sequence with 5 b-values for DKI and enhanced T1 high-resolution isotropic volume excitation (eTHRIVE) sequence for DCE-MRI.

ASSESSMENT

The mean diffusivity (MD) and mean kurtosis (MK) derived from DKI and the time-signal intensity curve (TIC), peak time (T_peak ), and washout ratio (WR) based on DCE-MRI were measured. The diagnostic efficiencies of DKI and DCE-MRI, alone and in combination, were calculated and compared. The parameters mentioned above were compared between the four subgroups.

STATISTICAL TEST

Mann-Whitney U test, chi-square test, receiver operating characteristic curve, Delong test, one-way analysis of variance test, and Kruskal-Wallis H test. A P value < 0.05 was considered statistically significant.

RESULTS

The combination of TIC and parameters of DKI and DCE-MRI for differentiating benign and malignant lesions with 94.94% accuracy is superior to DKI or DCE-MRI alone with approximately 75% accuracy. MD, MK, T_peak , and WR showed significant differences among the four subgroups. The accuracy of MD and MK was 91.14% and 92.41% for differentiating BTS from the other three subgroups. WR achieved 100% accuracy for discriminating WS from LS or MTS. MD and MK both differentiated LS from MTS with 97.14% accuracy.

DATA CONCLUSION

A combination of DKI and DCE-MRI can effectively differentiate head and neck lesions with good accuracy.

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

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.

Apparent diffusion coefficient measurement of the parotid gland parenchyma

The measurements of apparent diffusion coefficient (ADC) with diffusion weighted magnetic resonance imaging (DW-MRI) is becoming a popular diagnostic and research tool for examination of parotid glands. However, there is little agreement between the reported ADC values of the parotid gland in published literature. In this review 43 studies on ADC measurement of the parotid glands were included. The analyses indicated several possible culprits of the observed ADC discrepancies. For example, DW-MRI examinations under gustatory stimulation gives higher ADC values compared to the unstimulated parotid gland (P=0.003). The diffusion weighting factors (b-values) can either increase (b-value <200 s/mm²) or decrease ADC values (b-values >1,000 s/mm²). The timing of follow-up DW-MRI after radiotherapy (RT) indicates correlation to the found ADC values (R² =0.39). Interestingly, the choice of regions of interest (ROI) appears not to affect the measurements of ADC (P=0.75). It can be concluded that there is a critical need for standardization of ADC measurement of the parotid glands to allow valid inter-study comparisons and eventually to reach consensus on the use of ADC as biomarker.

Quantitative dynamic contrast-enhanced MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging in differentiating parotid gland tumors

PURPOSE

To evaluate the ability of quantitative dynamic contrast-enhanced (DCE)-MRI and readout segmentation of long variable echo-trains diffusion-weighted imaging (RESOLVE-DWI) in differentiating parotid tumors (PTs) with different histological types.

METHODS

In this retrospective study, 123 patients with 145 histologically proven PTs who underwent both RESOLVE-DWI and DCE-MRI were enrolled including 51 pleomorphic adenomas (PAs), 52 Warthin's tumors (WTs), 27 other benign neoplasms (OBNs), and 15 malignant tumors (MTs). Quantitative parameters of DCE-MRI (K^trans, K_ep, and V_e) and the apparent diffusion coefficient (ADC) of lesions were calculated and analyzed. Kruskal-Wallis tests with Dunn-Bonferroni correction, logistic regression analyses, and receiver operating characteristic curve were used for statistical analyses.

RESULTS

PAs exhibited a lowest K^trans among these four PTs. WTs demonstrated the highest K_ep and lowest V_e values. WTs and MTs showed lower ADC_min values than PAs and OBNs. The combination of K_ep and V_e provided 98.1% sensitivity, 85% specificity, and 98.7% accuracy for differentiating WTs from the other three PTs. The ADC_min cutoff value of ≤ 0.826 yielded 80.0% sensitivity, 92.3% specificity, and 90.3% accuracy for the differentiation of MTs from PAs and OBNs. K^trans with a cutoff value of ≤ 0.185 achieved a sensitivity, specificity, and accuracy of 84.3, 70.4, and 79.5%, respectively, for discriminating PAs from OBNs.

CONCLUSION

The combination of quantitative DCE-MRI and RESOLVE-DWI is beneficial for characterizing four histological types of PTs.

MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland

OBJECTIVES

Preoperative differentiation between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT) is important for treatment decisions. The purpose of this study was to develop and validate an MRI-based radiomics nomogram for the preoperative differentiation of BPGT from MPGT.

METHODS

A total of 115 patients (80 in training set and 35 in external validation set) with BPGT (n = 60) or MPGT (n = 55) were enrolled. Radiomics features were extracted from T1-weighted and fat-saturated T2-weighted images. A radiomics signature model and a radiomics score (Rad-score) were constructed and calculated. A clinical-factors model was built based on demographics and MRI findings. A radiomics nomogram model combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The diagnostic performance of the three models was evaluated and validated using ROC curves on the training and validation datasets.

RESULTS

Seventeen features from MR images were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature had an AUC value of 0.952 in the training set and 0.938 in the validation set. Decision curve analysis showed that the nomogram outperformed the clinical-factors model in terms of clinical usefulness.

CONCLUSIONS

The above-described radiomics nomogram performed well for differentiating BPGT from MPGT, and may help in the clinical decision-making process.

KEY POINTS

• Differential diagnosis between BPGT and MPGT is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, clinical data, and MRI features facilitates differentiation of BPGT from MPGT with improved diagnostic efficacy.

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.

The radiologic (CT/MRI)-pathological correlations of the salivary duct carcinoma (SDC) with hyaline degeneration and peripheral nerve invasion

OBJECTIVES

Salivary duct carcinoma is an aggressive tumour commonly showing local invasion and/or nerve palsy. However, their CT/MRI findings, especially, regarding T2WI, and the diffusion-weighted-image (DWI), were not well known. In this study, we correlated the CT/MRI appearance and the pathological findings containing the nerve invasion cases such as a facial nerve.

METHODS

We reviewed 14 cases of SDC (parotid = 11, submandibular = 2, minor salivary gland = 1) pathologically proven peripheral nerve involvement. Their CT findings of all patient including dynamic contrast-enhancement study､MRI (n = 9) and DWI (n = 6) were also analyzed with histopathological correlation.

RESULTS

On contrast-enhanced CT, the solid component was moderately enhanced. On MRI, T2WI central low signal core (n = 6) with peripheral high intensity rim (n = 5) was frequently observed except heterogeneous low and high (n = 1), diffuse low (n = 1), and high (n = 1) signal cases. The hyaline degenerative area located in the tumour core was poorly enhanced. Eleven tumours had an ill-defined margin, reflecting invasive tumour growth. On DWI, they showed high signal [the central low and peripherally high (n = 4), and diffuse (n = 1), heterogeneously high signal (n = 1)]. The mean ADC value was 1.148 ~ 0.961 x 10-3 mm²/s. With pathological correlation, the central low signal area on T2WI reflected hyaline degeneration. The sites of gross nerve involvement were revealed as tubular or branching structures on CE-CT (n = 3), and MRI (n = 1).

CONCLUSIONS

(1) We frequently observed a central low signal area on T2WI/DWI in SDC. With histopathological correlation, it corresponded to the central hyaline degeneration with the peripheral viable tumour. 2) The gross nerve involvement might be detected as a strongly enhancement structure.

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.

The current value of quantitative shear wave sonoelastography in parotid gland tumors

Background

The preoperative differentiation between salivary gland tumor entities using computed tomography, magnetic resonance imaging (MRI) and ultrasound (US) is still limited. Biopsies are often regarded as indispensable for properly characterizing these various lesions. The aim of this study was to analyze the value of acoustic radiation force impulse (ARFI) sonoelastography as an US differentiation tool when examining parotid gland (PG) lesions.

Methods

We included 104 patients with PG masses in this study, employing two different US devices using quantitative ARFI-sonoelastography (Siemens Acuson-S3000, n=59; Siemens Acuson-Sequoia, n=45). The ability of sonoelastographic measurements to differentiate between different neoplasms was compared and analyzed for both US machines.

Results

Quantitative shear wave sonoelastography is limited in its ability to reliably differentiate between tumor entities of the PG as a stand-alone parameter. Measurement results were unsystematically distributed and not transferable between the two US devices. A significant differentiation of benign and malignant lesions was not possible with either US machine (S3000: P=0.770, Sequoia: P=0.382). A differentiation between pleomorphic adenomas (PA) and Warthin tumors was only possible with the Acuson S3000 system (P=0.001, Spearman-Rho =0.492, sensitivity 73.9%, specificity 65.0%).

Conclusions

A reliable identification and differentiation of PG tumors as well as clinical treatment decisions cannot be made with the sole use of ARFI-sonoelastography. The results emphasize the device-dependence and high error-proneness of this US technique when examining lesions of the PG.

A triple-classification radiomics model for the differentiation of pleomorphic adenoma, Warthin tumour, and malignant salivary gland tumours on the basis of diffusion-weighted imaging

AIM

To develop and validate a triple-classification radiomics model for the preoperative differentiation of pleomorphic adenoma (PA), Warthin tumour (WT), and malignant salivary gland tumour (MSGT) based on diffusion-weighted imaging (DWI).

MATERIALS AND METHODS

Data from 217 patients with histopathologically confirmed salivary gland tumours (100 PAs, 68 WTs, and 49 MSGTs) from January 2015 to March 2019 were analysed retrospectively and divided into a training set (n=173), and a validation set (n=44). A total of 396 radiomic features were extracted from the DWI of all patients. Analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression were used to select radiomic features, which were then constructed using three classification models, namely, logistic regression method (LR), support vector machine (SVM), and K-nearest neighbor (KNN). The diagnostic performance of the radiomics model was quantified by the receiver operating characteristic (ROC) curve and area under the ROC curve (AUC) of the training and validation data sets.

RESULTS

The 20 most valuable features were investigated based on the LASSO regression. LR and SVM methods exhibited better diagnostic ability than KNN for multiclass classification. LR and SVM had the best performance and yielded the AUC values of 0.857 and 0.824, respectively, in the training data set and the AUC values of 0.932 and 0.912, respectively, in the validation data set of MSGT diagnosis.

CONCLUSION

DWI-based triple-classification radiomics model has predictive value in distinguishing PA, WT, and MSGT, which can be used for preoperative auxiliary diagnosis in clinical practice.

Imaging features of parotid gland oncocytoma: a case series study

Background

Reports regarding the imaging findings of parotid gland oncocytoma are limited and prospective definitive diagnosis is difficult. This case series is aimed at furthering the knowledge of imaging for parotid gland oncocytoma.

Methods

Seven patients with pathologically confirmed parotid gland oncocytoma were identified between January, 2016 and August, 2020. They were subjected to computed tomography, magnetic resonance imaging or ultrasound. Imaging features of lesions were retrospectively analyzed and documented by a senior radiologist.

Results

Among the 7 recruited parotid gland oncocytoma patients, 4 were males and 3 were females. They were aged between 47 and 87 years with a median age of 63 years. The range of maximum diameter of the mass was about 1.6-4.5 cm, with the median size being 3.5 cm. All cases were unilateral single masses. Three patients had their parotid gland oncocytoma located in the deep lobe of the parotid gland and 4 patients had their parotid gland oncocytoma located in the superficial lobe. Most lesions exhibited a clear boundary and irregular shapes or lobulated, and contain cystic components. Local pain was the most common clinical manifestation. Most of the lesions exhibited a hypoechoic mass on ultrasound, soft tissue density on computed tomography, iso to hypointense signal on T1 weighted imaging, slightly hypersignal on T2 weighted imaging, while contrast-enhanced computed tomography, magnetic resonance imaging, and ultrasound showed avid enhancement.

Conclusions

Parotid gland oncocytomas usually exhibit a clear boundary to surrounding parotid tissue, lobulated or irregular shape and avid enhancement, sometimes accompanied with cystic components. Diagnosis of parotid gland oncocytoma should be considered when these features are encountered in clinical practice.

Differentiating malignant from benign salivary gland lesions: a multiparametric non-contrast MR imaging approach

The purpose of this study is to determine whether multiparametric non-contrast MR imaging including diffusion-weighted imaging (DWI), arterial spin labeling (ASL), and amide proton transfer (APT) weighted imaging can help differentiate malignant from benign salivary gland lesions. The study population consisted of 42 patients, with 31 benign and 11 malignant salivary gland lesions. All patients were evaluated using DWI, three-dimensional pseudo-continuous ASL, and APT-weighted imaging on 3 T MR imaging before treatment. Apparent diffusion coefficient (ADC), tumor blood flow (TBF), and APT-related signal intensity (APTSI) values within the lesion were compared between the malignant and benign lesions by Mann-Whitney U test. For each parameter, optimal cutoff values were chosen using a threshold criterion that maximized the Youden index for predicting malignant lesions. The performance of ADC, TBF, APTSI, individually and combined, was evaluated in terms of diagnostic ability for malignant lesions. Diagnostic performance was compared by McNemar test. APTSI was significantly higher in malignant lesions (2.18 ± 0.89%) than in benign lesions (1.57 ± 1.09%, p = 0.047). There was no significant difference in ADC or TBF between benign and malignant lesions (p = 0.155 and 0.498, respectively). The accuracy of ADC, TBF, and APTSI for diagnosing malignant lesions was 47.6%, 50.0%, and 66.7%, respectively; whereas the accuracy of the three parameters combined was 85.7%, which was significantly higher than that of each parameter alone (p = 0.001, 0.001, and 0.008, respectively). Therefore, the combination of ADC, TBF, and APTSI can help differentiate malignant from benign salivary gland lesions.

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.

Efficacy of computed tomography features in the differentiation of basal cell adenoma and Warthin tumor in the parotid gland

OBJECTIVE

The aim of this study was to identify computed tomography (CT) features that differentiate basal cell adenoma (BCA) from Warthin tumor (WT).

MATERIALS AND METHODS

Histopathologically confirmed parotid gland tumors (57 BCAs and 83 WTs) were retrospectively reviewed. CT images were evaluated to determine location, distribution, cyst formation, size, the new vessel facing sign (VFS), and enhancement behavior including the CT attenuation of solid portions of the tumor (AST), the vessel near the tumor (AVT), and maxillary artery (AMA) on early stage 2-phase contrast CT. Tumor CT attenuation ratios (AST/AVT and AST/AMA) were calculated. Chi-square tests, independent t-tests, and receiver operating characteristic curve analysis were conducted.

RESULTS

Male:female ratio, patient age, posteroinferior location, tumor size, and presence of VFS were significantly lower for BCA than WT. The average AST/AVT was significantly higher for BCA than WT. The threshold value for AST/AVT on early stage 2-phase contrast CT was 0.72 between BCA and WT, and sensitivity and specificity were 94.7% and 98.8%, respectively, as calculated by receiver operating characteristic analysis.

CONCLUSION

Gender ratio, age, location, size, presence of VFS, and AST/AVT value may help to differentiate BCA from WT in parotid glands on CT examination.

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.

Parotid gland tumors: comparison of conventional and diffusion-weighted MRI findings with histopathological results

OBJECTIVES

The aim of this study was to investigate the relationship between pathological classification of parotid gland tumors and conventional MRI - diffusion-weighted imaging findings and also contribute the possible effect of apparent diffusion coefficient (ADC) to diagnosis.

METHODS

60 patients with parotid masses diagnosed using histopathology and/or cytology were enrolled in this retrospective study. All patients were evaluated using a 1.5 T MRI. Demographic features, conventional MRI findings, and ADC values (mean, minimum, maximum, and relative) were recorded. MRI findings and ADC values were compared between benign-malignant groups and pleomorphic adenoma vs Warthin's tumor groups.

RESULTS

60 tumors (48 benign, 12 malignant) were evaluated in a total of 60 patients (39 males, 21 females). The mean age was 59 (±14, 18-86) years old; the mean lesion size was 26 (±10, 11-61) mm. On the texture of conventional MRI, T2 dominantly hyperintense/with hypointensity signal was seen in 87% of pleomorphic adenomas and T2 dominantly hypointense/with hyperintesity signal was encountered in 64% of all Warthin's tumors. Seven (28%) Warthin's tumors were misdiagnosed as pleomorphic adenomas and two others (8%) as malignant tumors. The commonly used mean ADC value was 1.6 ± 0.6 × 10^-3 mm² s^-1 for benign tumors, 0.8 ± 0.3 × 10^-3 mm² s^-1 for malign tumors, 1 (0.9-1.8) × 10^-3 mm² s^-1 for Warthin's tumors, and 1.9 ± 0.3 × 10^-3 mm² s^-1 for pleomorphic adenomas. There was a statistically significant difference in ADC values between benign-malignant tumors and pleomorphic adenomas-Warthin's tumors.

CONCLUSIONS

Warthin's tumor may occasionally be misdiagnosed as pleomorphic adenoma and malignant tumor because of variable morphologic features. In addition to benign-malignant differentiation, the added ADC measurement may also be useful for differentiating Warthin's tumors from pleomorphic adenomas.

Performance of diffusion-weighted imaging for the diagnosis of parotid gland malignancies: A meta-analysis

PURPOSE

This study aimed to assess the diagnostic performance of diffusion-weighted imaging (DWI) for parotid gland malignancies.

METHODS

Four databases (PubMed, the Cochrane Library, Embase, and Web of Science) were searched systematically and retrospectively by two researchers until May 18, 2020. The methodological quality of the studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. A bivariate random effects model was used to pool the sensitivity and specificity data for the apparent diffusion coefficient (ADC). Summary receiver operating characteristic curve was constructed, and the area under the curve (AUC) was calculated. The positive (LR+) and negative likelihood ratios (LR-) were also calculated. Subgroup and meta-regression analyses were performed to evaluate heterogeneity within studies.

RESULTS

Sixteen studies involving 1004 patients were included. The pooled sensitivity, specificity, and AUC for the ADC to distinguish malignant from begin parotid lesions were 89 %, 76 %, and 0.91, respectively. The LR + was 3.7 and LR- was 0.15, respectively. Subgroup analyses revealed that the applied cut-off b values and study size were sources of heterogeneity for the ADC. There were publication bias concerns.

CONCLUSIONS

Our meta-analysis suggests that the ADC value provides excellent sensitivity and moderate specificity for the diagnosis of malignant lesions in the parotid gland. However, substantial heterogeneity was found. Therefore, additional larger, prospective studies in combination with standard techniques focusing on parotid tumors should be conducted to determine the true performance of DWI for the differential diagnosis of parotid lesions.

Guidelines of the French Society of Otorhinolaryngology-Head and Neck Surgery (SFORL), part I: Primary treatment of pleomorphic adenoma

INTRODUCTION

The authors present the guidelines of the French Society of Otorhinolaryngology-Head and Neck Surgery (SFORL) for the diagnosis and treatment of pleomorphic adenoma (PA) of the salivary glands.

METHOD

A review of the literature was performed by a multidisciplinary task force. Guidelines were drafted based on the articles retrieved and the workgroup members' individual experience. Guidelines were graded A, B, C or expert opinion by decreasing level of evidence.

RESULTS

In clinically suspected salivary gland PA, MRI should be performed, including head and neck lymph node levels. Fine needle aspiration cytology is particularly recommended for tumours difficult to characterise by MRI. Frozen section biopsy should be performed to confirm diagnosis and adapt the surgical procedure in case of intraoperative findings of malignancy. Complete resection of the parotid PA should be performed en bloc, including margins, when feasible according to tumour location, while respecting the facial nerve. Enucleation (resection only in contact with the tumour) is not recommended. For the accessory salivary and submandibular glands, complete en bloc resection should be performed.

ACR Appropriateness Criteria® Neck Mass-Adenopathy

A palpable neck mass may be the result of neoplastic, congenital, or inflammatory disease. Older age suggests neoplasia, and a congenital etiology is more prevalent in the pediatric population. The imaging approach is based on the patient age, mass location, and clinical pulsatility. Underlying human papillomavirus-related malignancy should be considered in all age groups. Although the imaging appearance of some processes in the head and neck overlap, choosing the appropriate imaging examination may allow a specific diagnosis, or a limited differential diagnosis. Tissue sampling is indicated to confirm suspected malignancy. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision include an extensive analysis of current medical literature from peer reviewed journals and the application of well-established methodologies (RAND/UCLA Appropriateness Method and Grading of Recommendations Assessment, Development, and Evaluation or GRADE) to rate the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where evidence is lacking or equivocal, expert opinion may supplement the available evidence to recommend imaging or treatment.