Pilot study of ultrasound parotid imaging reporting and data system (PIRADS): Inter-observer agreement

The efficacy of clinicopathological and imaging features in differentiating benign from malignant parotid micro- and minitumors: a retrospective analysis

OBJECTIVE

To compare clinicopathological and imaging features of micro- and minitumors of the parotid gland and provide a reference for preoperative prediction of benign vs malignant status.

STUDY DESIGN

Patients with parotid gland tumors treated surgically were selected. Relevant clinicopathological and imaging data were collected for patients with maximum tumor diameters ≤20 mm on preoperative computed tomography (CT). The lesions were divided into 2 groups, microtumors and minitumors, based on maximum tumor diameter. CT imaging features of benign and malignant tumors were compared through binary logistic regression analysis.

RESULTS

Microtumors and minitumors were categorized by maximum diameters <10 mm (n = 74) and 10-20 mm (n = 611), respectively. Benign and malignant minitumors exhibited significant differences in boundary, tumor density, margin morphology, spiculation margin, and CT values in the plain and arterial phase (P ≤ .027), resembling those found in typical malignant parotid gland tumors. However, no significant differences were observed between benign and malignant microtumors. Logistic regression analysis identified boundary, margin morphology, and spiculation margin as independent predictors of malignancy. The prediction model excelled in identifying benign lesions but was less successful in identifying malignancies.

CONCLUSION

Parotid gland minitumors had imaging features similar to typical larger malignant tumors. Active exclusion of the malignant risk and early surgical treatment is recommended for these tumors.

An ultrasound-based ensemble machine learning model for the preoperative classification of pleomorphic adenoma and Warthin tumor in the parotid gland

OBJECTIVES

The preoperative classification of pleomorphic adenomas (PMA) and Warthin tumors (WT) in the parotid gland plays an essential role in determining therapeutic strategies. This study aims to develop and validate an ultrasound-based ensemble machine learning (USEML) model, employing nonradiative and noninvasive features to differentiate PMA from WT.

METHODS

A total of 203 patients with histologically confirmed PMA or WT who underwent parotidectomy from two centers were enrolled. Clinical factors, ultrasound (US) features, and radiomic features were extracted to develop three types of machine learning model: clinical models, US models, and USEML models. The diagnostic performance of the USEML model, as well as that of physicians based on experience, was evaluated and validated using receiver operating characteristic (ROC) curves in internal and external validation cohorts. DeLong's test was used for comparisons of AUCs. SHAP values were also utilized to explain the classification model.

RESULTS

The USEML model achieved the highest AUC of 0.891 (95% CI, 0.774-0.961), surpassing the AUCs of both the US (0.847; 95% CI, 0.720-0.932) and clinical (0.814; 95% CI, 0.682-0.908) models. The USEML model also outperformed physicians in both internal and external validation datasets (both p < 0.05). The sensitivity, specificity, negative predictive value, and positive predictive value of the USEML model and physician experience were 89.3%/75.0%, 87.5%/54.2%, 87.5%/65.6%, and 89.3%/65.0%, respectively.

CONCLUSIONS

The USEML model, incorporating clinical factors, ultrasound factors, and radiomic features, demonstrated efficient performance in distinguishing PMA from WT in the parotid gland.

CLINICAL RELEVANCE STATEMENT

This study developed a machine learning model for preoperative diagnosis of pleomorphic adenoma and Warthin tumor in the parotid gland based on clinical, ultrasound, and radiomic features. Furthermore, it outperformed physicians in an external validation dataset, indicating its potential for clinical application.

KEY POINTS

• Differentiating pleomorphic adenoma (PMA) and Warthin tumor (WT) affects management decisions and is currently done by invasive biopsy. • Integration of US-radiomic, clinical, and ultrasound findings in a machine learning model results in improved diagnostic accuracy. • The ultrasound-based ensemble machine learning (USEML) model consistently outperforms physicians, suggesting its potential applicability in clinical settings.

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.

Polar Vessel: A New Ultrasound Sign for Complementary Diagnosis of Major Salivary Gland Adenoid Cystic Carcinoma

OBJECTIVES

To investigate the characteristic ultrasonographic findings of adenoid cystic carcinoma (ACC) in major salivary glands and identify the value of polar vessel in color Doppler flow imaging (CDFI) for the diagnosis of ACC.

METHODS

From January 2017 to December 2021, 76 patients with parotid and submandibular gland tumors, including 14 patients with ACC, as confirmed by surgery and histopathology, were enrolled. Their clinicopathologic information and ultrasound (US) features were recorded and analyzed. The performance of polar vessel in CDFI for differentiating ACC from non-ACC (benign tumors and mucoepidermoid carcinoma [MEC]) was analyzed.

RESULTS

ACC in the major salivary gland was more likely to be associated with pain symptoms (P = .027) and unclear borders and rough edges in grayscale US (P = .002, .015, respectively) than benign tumors. Compared to MEC, ACC tended to feature a homogeneous internal echo (P = .008). ACC of the major salivary gland had a significantly higher incidence of polar vessel sign than that of non-ACC (benign tumors and MEC) (P < .0001, .0001, respectively). The polar vessel sign showed good performance in distinguishing between ACC and non-ACC, with an area under the receiver operating characteristic curve of 0.857, a sensitivity of 71.4%, a specificity of 100%, and an accuracy of 94.7%. Positive predictive value and negative predictive value were calculated at 100% and 93.9%, respectively.

CONCLUSIONS

The US sign of polar vessel has high diagnostic efficiency, and it may have important potential for use as a new complementary sign for the diagnosis of ACC in major salivary glands.

Diagnostic grading of parotid lesions by conventional ultrasound: a pilot study

OBJECTIVES

To provide a graded diagnosis of benign and malignant lesions in the parotid gland by conventional ultrasound, and thus to predict the probability of malignancy of the lesions.

METHODS

Retrospective analysis of conventional ultrasound images of 150 patients with parotid lesions by two observers. Parotid lesions were classified into seven patterns and then categorized into eight grades: Grade 0, unsatisfied illustration on ultrasound; Grade 1, normal parotid gland; Grade 2, definitively benign; Grade 3, probably benign; Grade 4, indeterminate; Grade 5, probably malignant; Grade 6, highly suggestive malignant and Grade 7, already had malignant diagnosis. Combined with the pathological results, the conventional ultrasound diagnostic grade of parotid lesions was evaluated for predicting the probability of malignancy.

RESULTS

There was excellent interobserver agreement of both readers for patterns and grades (K = 0.89 and 0.90, p < 0.01). The proportions of the malignancies in conventional ultrasound Grade 2, 3, 4, 5 and 6 according to the two readers 0 and 0, 0 and 0, 8.7% and 8.8%, 54.2 and 50%, 100 and 100%, respectively. The sensitivity, specificity and area under ROC curve(AUC) were 64.0%, 91.2%, 0.809 and 64.0%, 89.6%, 0.802, respectively, using Grade 5 of the two readers as the best grade for diagnosing benign and malignant parotid lesions.

CONCLUSION

The conventional ultrasound diagnostic grade of parotid lesions can be used to evaluate the risk of malignancy and will be helpful to improve the imaging diagnosis and clinical treatment.

A nomogram based on clinical information, conventional ultrasound and radiomics improves prediction of malignant parotid gland lesions

Although conventional ultrasound (CUS) allows for clear detection of parotid gland lesions (PGLs), it fails to accurately provide benign-malignant differentiation due to overlapping morphological features. Radiomics is capable of processing large-quantity volume of data hidden in CUS image undiscovered by naked eyes. The aim was to explore the potential of CUS-based radiomics score (Rad-score) in distinguishing benign (BPGLs) and malignant PGLs (MPGLs). A consecutive of 281 PGLs (197 in training set and 84 in test set) with definite pathological confirmation was retrospectively enrolled. 1465 radiomics features were extracted from CUS images and Rad-score was constructed by using Least Absolute Shrinkage and Selection Operator (LASSO) algorithm. Different nomogram models, including clinic-radiomics (Clin + Rad-score), CUS-clinic (CUS + Clin) and combined CUS-clinic-radiomics (CUS + Clin + Rad-score), were built using logistic regression. The diagnostic performance of different models were calculated and compared by area under receiver operating curve (AUC) and corresponding sensitivity and specificity. Finally, 26 radiomics features were independent signatures for predicting MPGLs, with MPGLs having higher Rad-scores in both cohorts (both P < 0.05). In the test population, CUS + Clin + Rad-score obtained an excellent diagnostic result, with significantly higher AUC value (AUC = 0.91) when compared to that of CUS + Clin (AUC = 0.84) and Clin + Rad-score (AUC = 0.74), respectively (both P < 0.05). In addition, the sensitivity of this combined model was higher than that of single Rad-score model (100.00% vs. 71.43%, P = 0.031) without compromising the specificity value (82.86% vs. 88.57%, P = 0.334). The calibration curve and decision curve analysis also indicated the clinical effectiveness of the proposed combined nomogram. The combined CUS-clinic-radiomics model may help improve the discrimination of BPGLs from MPGLs.

Artificial Intelligence and Deep Learning of Head and Neck Cancer

Artificial intelligence (AI) algorithms, particularly deep learning, have developed to the point that they can be applied in image recognition tasks. The use of AI in medical imaging can guide radiologists to more accurate image interpretation and diagnosis in radiology. The software will provide data that we cannot extract from the images. The rapid development in computational capabilities supports the wide applications of AI in a range of cancers. Among those are its widespread applications in head and neck cancer.

Prostate Imaging Reporting and Data System (PI-RADS): What the radiologists need to know?

We aim to review the new modifications in MR imaging technique, image interpretation, lexicon, and scoring system of the last version of Prostate Imaging Reporting and Data System version 2.1 (PI-RADS v2.1) in a simple and practical way. This last version of PI-RADS v2.1 describes the new technical modifications in the protocol of Multiparametric MRI (MpMRI) including T2, diffusion-weighted imaging (DWI), and dynamic contrast enhancement (DCE) parameters. It includes also; new guidelines in the image interpretation specifications in new locations (lesions located in the central zone and anterior fibromuscular stroma), clarification of T2 scoring of lesions of the transition zone, the distinction between DWI score 2 and 3 lesions in the transition zone and peripheral zone, as well as between positive and negative enhancement in DCE. Biparametric MRI (BpMRI) along with simplified PI-RADS is gaining more acceptances in the assessment of clinically significant prostatic cancer.

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.

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.

Differentiation malignant from benign parotid tumors in children with diffusion-weighted MR imaging

PURPOSE

To differentiate pediatric solid malignant from the benign parotid tumors with diffusion-weighted MR imaging (DWI).

MATERIALS AND METHODS

A retrospective study comprising 38 children with parotid tumors (21 boys and 17 girls aged from 2 months to 17 years) was conducted using (DWI) of the parotid gland. Apparent diffusion coefficient (ADC) maps were generated. The ADC value of the parotid tumors was calculated.

RESULTS

The mean ADC value of malignant parotid tumors (1.08 ± 0.1, 1.04 ± 0.1 × 10^-3mm²/s) was significantly lower [P = 0.001] than that of benign lesions (1.69 ± 0.2, 1.72 ± 0.3 × 10^-3mm²/s). A threshold of ADC of 1.40, 1.33 × 10^-3mm²/s was used for differentiating malignant parotid tumors from benign lesions and led to the best results of the area under the curve of 0.940, 0.929, accuracy of 86, 89%, sensitivity of 94, 94%, specificity of 80, 85%, negative predictive value of 94.1, 94.4%, and positive predictive value of 81, 85%. There was insignificant difference in ADC values of malignant lesions (P = 0.23, 0.30) as well as within benign lesions (P = 0.25, 0.08).

CONCLUSION

DWI is an innovative anticipating imaging technique that can be used in the differentiation of pediatric solid malignant parotid tumors from benign lesions.

Interobserver Agreement of Magnetic Resonance Imaging of Liver Imaging Reporting and Data System Version 2018

AIM

This study aimed to assess the interobserver agreement of magnetic resonance (MR) imaging of Liver Imaging Reporting and Data System version 2018 (LI-RADS v2018).

SUBJECTS AND METHODS

Retrospective analysis was done for 119 consecutive patients (77 male and 42 female) at risk of hepatocellular carcinoma who underwent dynamic contrast MR imaging. Image analysis was done by 2 independent and blinded readers for arterial phase hyperenhancement, washout appearance, enhancing capsule appearance, and size. Hepatic lesions were classified into 7 groups according to LI-RADS v2018.

RESULTS

There was excellent interobserver agreement of both reviewers for LR version 4 (κ = 0.887, P = 0.001) with 90.76% agreement. There was excellent interobserver agreement for nonrim arterial phase hyperenhancement (κ = 0.948; 95% confidence interval [CI], 0.89-0.99; P = 0.001), washout appearance (κ = 0.949; 95% CI, 0.89-1.0; P = 0.001); and enhancing capsule (κ = 0.848; 95% CI, 0.73-0.97; P = 0.001) and excellent reliability of size (interclass correlation, 0.99; P = 0.001). There was excellent interobserver agreement for LR-1 (κ = 1.00, P = 0.001), LR-2 (κ = 0.94, P = 0.001), LR-5 (κ = 0.839, P = 0.001), LR-M (κ = 1.00, P = 0.001), and LR-TIV (κ = 1.00; 95% CI, 1.0-1.0; P = 0.001), and good agreement for LR-3 (κ = 0.61, P = 0.001) and LR-4 (κ = 0.61, P = 0.001).

CONCLUSION

MR imaging of LI-RADS v2018 is a reliable imaging modality and reporting system that may be used for standard interpretation of hepatic focal lesions.

Diagnostic Accuracy of Multiparametric Magnetic Resonance Imaging for Differentiation Between Parotid Neoplasms

PURPOSE

This study was designed to evaluate the role of multiparametric magnetic resonance imaging (MRI) for differentiation of parotid gland neoplasms.

METHODS

Prospective study was conducted upon 52 consecutive patients (30 men, 22 women; aged 24-78 years; mean, 51 years) with parotid tumours that underwent multiparametric MRI using combined static MRI, dynamic contrast enhanced (DCE) MRI, and diffusion-weighted imaging (DWI). The static MRI parameter, time signal intensity curves (TIC) derived from DCE-MRI, and apparent diffusion coefficient (ADC) values of parotid tumours were correlated with histopathological findings.

RESULTS

Static MRI revealed a significant difference between both benign and malignant lesions in regards to margin definition (P < .001) and T2 hypointensity (P < .013), with a diagnostic accuracy 95% and 78.33% respectively. Study of the TIC type on DCE-MRI revealed statistically significant difference between benign and malignant lesions (P < .001) and diagnostic accuracy 96.55%. There was no statistically significant difference (P = .181) between the ADC values of benign and malignant lesions. ROC curve analysis revealed that by using ADC cut-off value of 1 × 10^-3 mm²/s had accuracy of 84.62% respectively for differentiating Warthin from malignant tumours that could be modified to higher value (94.28%) by excluding lymphoma from malignant lesions. By using cutoff value of 1.3 × 10^-3 mm²/s to differentiate pleomorphic adenoma from malignancy, ROC curve analysis had high accuracy of 97.06%.

CONCLUSION

Multiparametric MRI can be used for differentiation of malignant from benign parotid tumours and characterization of some benign parotid tumours.

Characterization of salivary gland tumours with diffusion tensor imaging

OBJECTIVES

To characterize salivary glands tumours with diffusion tensor imaging.

METHODS

This study was conducted upon 53 patients (aged 18-81 years: mean 37 years) with salivary gland tumours that underwent diffusion tensor imaging was obtained using a single-shot echoplanar imaging sequence with parallel imaging at 1.5 T scanner. 48 slices were obtained, with a thickness of 2.5 mm, with no gap and the total scan duration was 7-8 min. The fractional anisotropy (FA) and the mean diffusivity (MD) value of the salivary gland tumours was calculated and correlated with pathological findings. Image analysis was performed by one radiologist. The receiver operating characteristic curve was drawn to detect the cut-off point of FA and MD used to characterize salivary gland tumours.

RESULTS

The mean FA and MD of malignant salivary gland tumours (n = 17) (0.41 ± 0.07 and 0.89 ± 0.15 × 10-3 mm2 s-1) was significantly different (p = 0.001) than that of benign tumours (n = 36) (0.19 ± 0.07 and 1.28 ± 0.42 × 10-3 mm2 s-1), respectively. Combined FA and MD used to differentiate malignant from benign tumours has an area under the curve (AUC) of 0.974, and an accuracy of 86%. There was a significant difference in FA between Warthin tumours and malignant tumours (p = 0.001). Selection FA of 0.35 to differentiate malignant tumours from Warthin tumours revealed AUC of 0.878 and an accuracy of 80%. There was a significant difference in FA and MD of malignant tumours and pleomorphic adenomas (p = 0.001). Combined FA and MD used to differentiate malignant tumours from pleomorphic adenomas revealed AUC of 0.993, and an accuracy of 93%. There was a significant difference in FA and MD of Warthin tumours and pleomorphic adenomas (p = 0.001). Combined FA and MD used to differentiate Warthin tumours from pleomorphic adenomas revealed AUC of 0.978, and an accuracy of 86%.

CONCLUSIONS

Diffusion-weighed imaging is a promising non-invasive method and it may be useful for the characterization and differentiation of benign and malignant salivary gland tumours.

Inter-observer agreement of the Coronary Artery Disease Reporting and Data System (CAD-RADSTM) in patients with stable chest pain

Purpose

To assess inter-observer variability of the Coronary Artery Disease - Reporting and Data System (CAD-RADS) for classifying the degree of coronary artery stenosis in patients with stable chest pain.

Material and methods

A prospective study was conducted upon 96 patients with coronary artery disease, who underwent coronary computed tomography angiography (CTA). The images were classified using the CAD-RAD system according to the degree of stenosis, the presence of a modifier: graft (G), stent (S), vulnerable plaque (V), or non-diagnostic (n) and the associated coronary anomalies, and non-coronary cardiac and extra-cardiac findings. Image analysis was performed by two reviewers. Inter-observer agreement was assessed.

Results

There was excellent inter-observer agreement for CAD-RADS (k = 0.862), at 88.5%. There was excellent agreement for CAD-RADS 0 (k = 1.0), CAD-RADS 1 (k = 0.92), CAD-RADS 3 (k = 0.808), CAD-RADS 4 (k = 0.826), and CAD-RADS 5 (k = 0.833) and good agreement for CAD-RADS 2 (k = 0.76). There was excellent agreement for modifier G (k = 1.0) and modifier S (k = 1.0), good agreement for modifier N (k = 0.79), and moderate agreement for modifier V (k = 0.59). There was excellent agreement for associated coronary artery anomalies (k = 0.845), non-coronary cardiac findings (k = 0.857), and extra-cardiac findings (k = 0.81).

Conclusions

There is inter-observer agreement of CAD-RADS in categorising the degree of coronary arteries stenosis, and the modifier of the system and associated cardiac and extra-cardiac findings.

Multimodal ultrasonographic algorithm in the differentiation of submandibular masses

CONCLUSION

The multimodal diagnostic algorithm is a simple diagnostic tool in the pre-operative assessment of submandibular masses that enables one-stage surgical concepts.

BACKGROUND

The pre-operative assessment of submandibular masses is of major clinical impact. This study assesses the diagnostic utility of a newly introduced diagnostic algorithm in the differentiation of submandibular masses based on epidemiological and B-mode ultrasonographic data.

METHODS

One hundred and eighty-eight patients with submandibular triangle masses were included. Epidemiological and ultrasonographic data of 128 consecutively included patients were assessed and structured in a multimodal algorithm. The diagnostic algorithm was prospectively validated in a further 60 patients.

RESULTS

Single epidemiological and ultrasonographic data do not reliably predict the lesional entity. The multimodal diagnostic algorithm achieved a diagnostic sensitivity/specificity of 100%/100% in sialolithiasis, 44%/100% in benign submandibular gland diseases (BSD), 94%/96% in nodal lymphoma/unspecific lymphadenitis, and 91%/84% in carcinomas. Reduced sensitivity in BSD or specificity in carcinomas increased after intra-operative fresh frozen section in patients suspected for BSD.

Imaging of sialadenitis

Sialadenitis is an inflammation or infection of the salivary glands that may affect the parotid, submandibular and small salivary glands. Imaging findings vary among unilateral or bilateral salivary gland enlargement, atrophy, abscess, ductal dilation, cysts, stones and calcification. Imaging can detect abscess in acute bacterial suppurative sialadenitis, ductal changes with cysts in chronic adult and juvenile recurrent parotitis. Imaging is sensitive for detection of salivary stones and stricture in obstructive sialadenitis. Immunoglobulin G4-sialadenitis appears as bilateral submandibular gland enlargement. Imaging is helpful in staging and surveillance of patients with Sjögren’s syndrome. Correlation of imaging findings with clinical presentation can aid diagnosis of granulomatous sialadenitis. Post-treatment sialadenitis can occur after radiotherapy, radioactive iodine or surgery.

Imaging of sialadenitis

Sialadenitis is an inflammation or infection of the salivary glands that may affect the parotid, submandibular and small salivary glands. Imaging findings vary among unilateral or bilateral salivary gland enlargement, atrophy, abscess, ductal dilation, cysts, stones and calcification. Imaging can detect abscess in acute bacterial suppurative sialadenitis, ductal changes with cysts in chronic adult and juvenile recurrent parotitis. Imaging is sensitive for detection of salivary stones and stricture in obstructive sialadenitis. Immunoglobulin G4-sialadenitis appears as bilateral submandibular gland enlargement. Imaging is helpful in staging and surveillance of patients with Sjögren's syndrome. Correlation of imaging findings with clinical presentation can aid diagnosis of granulomatous sialadenitis. Post-treatment sialadenitis can occur after radiotherapy, radioactive iodine or surgery.

Imaging of sialadenitis

Sialadenitis is an inflammation or infection of the salivary glands that may affect the parotid, submandibular and small salivary glands. Imaging findings vary among unilateral or bilateral salivary gland enlargement, atrophy, abscess, ductal dilation, cysts, stones and calcification. Imaging can detect abscess in acute bacterial suppurative sialadenitis, ductal changes with cysts in chronic adult and juvenile recurrent parotitis. Imaging is sensitive for detection of salivary stones and stricture in obstructive sialadenitis. Immunoglobulin G4-sialadenitis appears as bilateral submandibular gland enlargement. Imaging is helpful in staging and surveillance of patients with Sjögren’s syndrome. Correlation of imaging findings with clinical presentation can aid diagnosis of granulomatous sialadenitis. Post-treatment sialadenitis can occur after radiotherapy, radioactive iodine or surgery.

Imaging of connective tissue diseases of the head and neck

We review the imaging appearance of connective tissue diseases of the head and neck. Bilateral sialadenitis and dacryoadenitis are seen in Sjögren's syndrome; ankylosis of the temporo-mandibular joint with sclerosis of the crico-arytenoid joint are reported in rheumatoid arthritis and lupus panniculitis with atypical infection are reported in patients with systemic lupus erythematosus. Relapsing polychondritis shows subglottic stenosis, prominent ear and saddle nose; progressive systemic sclerosis shows osteolysis of the mandible, fibrosis of the masseter muscle with calcinosis of the subcutaneous tissue and dermatomyositis/polymyositis shows condylar erosions and autoimmune thyroiditis. Vascular thrombosis is reported in antiphospholipid antibodies syndrome; cervical lymphadenopathy is seen in adult-onset Still's disease, and neuropathy with thyroiditis reported in mixed connective tissue disorder. Imaging is important to detect associated malignancy with connective tissue disorders. Correlation of the imaging findings with demographic data and clinical findings are important for the diagnosis of connective tissue disorders.