Narrative review of multiparametric ultrasound in parotid gland evaluation

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on ultrasound: a retrospective study

BACKGROUND

To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis.

METHODS

A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases' characteristics were conducted.

RESULTS

The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values.

CONCLUSIONS

The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

Application of ultrasound in distinguishing the incipient microtumor from inherent lymph nodes of the parotid glands

BACKGROUND

Parotid microtumors (size ≤ 1 cm) pose a significant clinical challenge due to their malignant potential and risks associated with surgery. It is imperative to explore the diagnostic workflow that incorporates ultrasound (US) in order to make appropriate clinical decisions with minimal invasiveness.

METHODS

The patients receiving both US and ultrasound-guided fine needle aspiration (USFNA) for the parotid microtumors in a medical center were retrospectively recruited. The ultrasonic features, cytology of USFNA, and final surgical pathology were analyzed to differentiate the tumor origins and their malignant potential.

RESULTS

From August 2009 to March 2016, a total of 92 patients were enrolled in the study. The short axis, long-to-short axis ratio, and presence of an echogenic hilum were found to be significantly useful in distinguishing lymphoid tissue origin from salivary gland origin, which was confirmed by USFNA. An irregular border was predictive for malignant parotid microtumors from both origins. Intra-tumoral heterogeneity was also identified as a significant feature associated with malignant lymph nodes. USFNA was able to confirm all malignant lymph nodes, but it had a false negative rate of 8.5% in parotid microtumors of salivary gland origin. Based on the analysis of US and USFNA results, a diagnostic workflow for parotid microtumors was proposed.

CONCLUSION

US and USFNA can be helpful in classifying the origins of parotid microtumors. US-FNA carries the risk of producing false negative results specifically for microtumors originating from salivary glands, but not lymphoid tissue. The diagnostic workflow, which incorporates both US and USFNA, assists in determining the clinical decision for diagnosing and managing parotid microtumors.

Pediatric primary salivary gland tumors

OBJECTIVES

To characterize the presentation and treatment of children presenting with primary salivary gland neoplasms.

METHODS

A retrospective review of primary salivary tumor patients presenting to Children's Hospital Colorado between January 2000 and August 2020.

RESULTS

Fifty children were identified with primary salivary gland tumors, comprising of 39 (78 %) benign and 11 (22 %) malignant lesions. Pleomorphic adenoma was the most common benign tumor (36/39, 92 %), while acinic cell carcinoma was the most common malignancy (7/11, 64 %). The parotid gland was the most common site, followed by the submandibular gland (66 % vs. 34 %). No tumors were found in the sublingual glands. Benign neoplasms accounted for 70 % of parotid lesions and 94 % of submandibular tumors. No significant differences in age (13.6 years, SD 4 vs. 13.0 years, SD 4.3) were noted between patients with benign and malignant disease, but tumors in females were more frequently malignant (M:F 1:1.3 vs. 1:2.7 for benign and malignant tumors, respectively). Neck dissection and/or facial nerve sacrifice were required in 27 % (3/11) and 9.1 % (1/11) of malignancies, respectively. Local recurrence was observed in 7.7 % (3/39) of benign cases and 9.1 % (1/11) of malignant cases. No salivary malignancies required chemotherapy, though one patient with neurofibromatosis received imatinib prior to resection. Two patients with locoregional malignancy received adjunctive radiation. The average duration of follow up for benign and malignant disease were 12.6 ± 25 and 45.1 ± 32 months, respectively.

CONCLUSIONS

This study presents one of the larger single institutional experiences of pediatric primary salivary neoplasms in the past 20 years, identifying pleomorphic adenoma and acinic cell carcinoma as the most common benign and malignant etiologies, respectively. While this review found most neoplasms presented as a localized mass effectively managed with conservative surgical resection, aggressive tumors required multidisciplinary care.

Maxillofacial inflammations visualized with ultrasonography. Description of the imaging features and literature review based on a characteristic case series

Objectives

Inflammations of the maxillofacial regions are a frequent occurrence. They areusually of odontogenic origin, but maxillofacial swelling could also have non-odontogenic causes. Their clinical presentation is worrisome for the patient, presenting as swellings of the region with rapid and significant expansion to adjacent areas due to the thin and delicate nature of the regional soft tissues.

Materials and methods

The characteristic features are discussed upon the presentation of a case series of the most common types of inflammation seen in the region.

Results

In most hospital emergency departments, ultrasound scanning is readily accessible, and typically constitutes the first-line imaging modality for this entity. Nevertheless, the role of ultrasound imaging is limited in cases with deep extension of the inflammation, where cross-sectional imaging with CT or MRI will be the modality of choice. This manuscript aims to present the characteristic features of various inflammatory conditions of the maxillofacial area seen on ultrasonography.

Conclusions

Even though maxillofacial inflammations are often treated without imaging in their initial phase, ultrasound can provide aninexpensive, easy-to-use, and readily available alternative that best visualizes the characteristics and expansion patterns of the lesions, based on their origin and area of initial presentation.

Histogram analysis of ultrasonographic images in the differentiation of benign and malignant parotid gland tumors

OBJECTIVE

We evaluated the diagnostic value of histogram analysis (HA) using ultrasonographic (US) images for differentiation among pleomorphic adenoma (PA), adenolymphoma (AL), and malignant tumors (MT) of the parotid gland.

STUDY DESIGN

Preoperative US images of 48 patients with PA, 39 patients with AL, and 17 patients with MT were retrospectively analyzed for gray-scale histograms. Nine first-order texture features derived from histograms of the tumors were compared. Area under the receiver operating characteristic curve (AUC) was used to evaluate the diagnostic performance of texture features. The Youden index maximum exponent was used to calculate sensitivity and specificity.

RESULTS

Statistically significant differences were discovered in Mean and Skewness HA values between PA and AL (P<0.001), and in Mean values between AL and MT (P<0.001). However, comparison of PA and MT showed no statistically significant differences (P>0.01). Excellent discrimination was detected between PA and AL (AUC=0.802), and between AL and MT (AUC=0.822). The combination of Mean plus Skewness improved discrimination between PA and AL (AUC=0.823) with sensitivity values reaching 1.00. However, Mean plus Skewness applied to differentiate PA from AL and Mean values applied to distinguish AL and MT resulted in low specificity, indicating many false positive interpretations.

CONCLUSIONS

Histogram analysis is useful for differentiating PA from AL and AL from MT but not PA from MT.

Clinical Diagnostic Application of Contrast-Enhanced Ultrasound in Focal Lesions of the Salivary Glands

OBJECTIVES

To evaluate the clinical diagnostic value of contrast-enhanced ultrasound (CEUS) for focal benign and malignant lesions of the salivary glands, as well as for common benign lesions.

METHODS

A total of 91 patients with focal lesions of the salivary glands were included in this study. In this study, CEUS was used to study the differential diagnosis of focal benign and malignant lesions of the salivary gland and the most common benign tumors, that is, pleomorphic adenoma (PA) and adenolymphoma.

RESULTS

The differences between focal benign and malignant lesions in the salivary glands were statistically significant (P < .05) in terms of qualitative CEUS indicators, enhancement pattern, enhancement homogeneity, enhancement margin, and enhanced lesion size, whereas the differences were not statistically significant (P > .05) in terms of wash-in and wash-out pattern, enhancement degree. Blurred margins and increased size of the lesion after enhancement are two CEUS features independently associated with focal malignant lesions of the salivary gland. The differences between salivary gland PA and adenolymphoma were statistically significant (P < .05) in terms of wash-in pattern, enhancement degree, enhancement homogeneity, and enhancement pattern, but not in terms of wash-out pattern, enhancement margin, and enhanced lesion size (P > .05).

CONCLUSIONS

As an economical, convenient, and safe imaging method, CEUS has important clinical value in distinguishing benign and malignant salivary glands.

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.

The Diagnostic Value of Ultrasound-Based Deep Learning in Differentiating Parotid Gland Tumors

Objectives. Evidence suggests that about 80% of all salivary gland tumors involve the parotid glands, with approximately 20% of parotid gland tumors (PGTs) being malignant. Discriminating benign and malignant parotid gland lesions preoperatively is vital for selecting the appropriate treatment strategy. This study explored the diagnostic performance of deep learning system for discriminating benign and malignant PGTs in ultrasonography images and compared it with radiologists. Methods. A total of 251 consecutive patients with surgical resection and proven parotid gland malignant or benign tumors who underwent preoperative ultrasound examinations were enrolled in this study between January 2014 and November 2020. Next, we compared the diagnostic accuracy of deep learning methods (ViT-B\16, EfficientNetB3, DenseNet121, and ResNet50) and radiologists in parotid gland tumor. In addition, the area under the curve (AUC), specificity, sensitivity, positive predictive value, and negative predictive value were calculated. Results. Among the 251 patients, 176/251 were the training set, whereas 75/251 were the validation set. Results showed that 74/251 patients had malignant tumor. Deep learning models achieved good performance in differentiating benign from malignant tumors, with the diagnostic accuracy and AUCs of ViT-B\16, EfficientNetB3, DenseNet121, and ResNet50 model being 81% and 0.81, 80% and 0.82, 77% and 0.81, and 79% and 0.80, respectively. On the other hand, the diagnostic accuracy and AUCs of radiologists were 77%-81% and 0.68-0.75, respectively. It was evident that the diagnostic accuracy of deep learning methods was higher than that of inexperienced radiologists, but there was no significant difference between deep learning methods and experienced radiologists. Conclusions. This study shows that the deep learning system can be used for diagnosing parotid tumors. The findings also suggest that the deep learning system may improve the diagnosis performance of inexperienced radiologists.

Perfusion analysis of benign parotid gland tumors by contrast-enhanced ultrasonography (CEUS)

BACKGROUND

Diagnosis of parotid gland tumors is sometimes challenging due to their diversity and pleomorphic histological appearance. B-scan sonography along with color-coded duplex sonography is the gold standard in the diagnostic workup of these lesions, whereas histopathology is to date the gold standard for the final diagnosis. To date no single imaging technique provides the chance for an art-diagnosis with highly diagnostic accuracy. Contrast enhanced ultrasonography (CEUS) on the other hand provides information of the perfusion down to the capillary level. Currently there are only a few papers published with systematical examination of the perfusion in benign parotid gland tumors and its diagnostic significance.

PATIENTS AND METHODS

One hundred patients with a parotid gland tumor were examined. The examinations included conventional B-scan sonography, color-coded duplexsonography along with contrast enhanced ultrasonography (CEUS). B-scan sonographic parameters, i.e. echogenicity, shape, size, demarcation, and borders of a lesion along with vascularization estimated by color-coded-duplexsonography were analyzed. Analysis of quantitative CEUS parameters was performed using 8 regions of interest (ROI), which were standardized located throughout the entire tumors. The perfusion parameters were analyzed for particular tumor entities. Qualitative CEUS analysis with estimating the perfusion pattern was additionally performed.

RESULTS

Histological examination revealed benign tumors in 92 cases, with pleomorphic adenomas and Warthin´s tumors were the most frequent entities. Malignant conditions were found in 8 cases. CEUS revealed a centripetal perfusion pattern in malignant tumors significantly more frequently than in benign tumors. CEUS showed a significant heterogenic perfusion in all tumors, with a higher perfusion in the medial parts of the tumors and in some cases also in the center. Perfusion patterns of PA and WT were different. WT displayed centrifugal, centripetal, and central diffuse perfusion more often than PA, whereas in PA perfusion often was limited to the capsule or periphery. Oncocytoma had the highest perfusion values. Intraglandular cysts showed no intralesional perfusion.

CONCLUSIONS

CEUS analysis in different parts of benign tumors revealed a significant heterogeneity in tumor perfusion. Some perfusion pattern could be identified which might be characteristic for particular lesions. Based on this, the diagnostic accuracy of CEUS in the differential diagnosis of parotid gland tumors can be increased. In particular, the perfusion analysis within the tumors using ROIs located standardized throughout the entire tumor provides additional information which are important for the art diagnosis and in differentiation of tumor entity.