Sonoelastography for differential diagnosis between malignant and benign parotid lesions: a meta-analysis

The Importance of Elastography in the Early Diagnosis of Highly Differentiated Parotid Tumors: a Case-Report

It is indisputable that high-resolution ultrasound (US) is the diagnostic gold standard for the evaluation of superficial parotid gland diseases. It is a dynamic, quick, simple, easily available, cost-effective, noninvasive procedure, with absence of ionizing radiation examination that can be performed safely and in special categories of patients such as pregnant women and children. It is widely accepted that on US, benign tumors have clear, smooth and well-defined borders, homogeneous hypoechoic parenchyma and a defined distribution of vessels. On the other hand, malignant lesions usually have unclear borders; also, they are inhomogeneous and sometimes can have areas of necrosis, increased hypoechogenicity and diffuse vascularization. However, many times these findings are not decisive due to the overlap that often occurs in parotid tumors. Shear wave elastography (SWE) represents a new imaging technique that provides additional information about tissue elasticity and stiffness in a selected region of interest. Since malignant tissues show greater stiffness than benign ones, sonoelastography is used to assist differential diagnosis between malignant and benign lesions. In many other organs, such as breast, thyroid, prostate and liver, it has been already successfully used for the differential diagnosis between malignant and benign lesions. The present article highlights the role of elastography in the diagnosis of a small malignant tumor in the left parotid gland of a 73-year-old female patient.

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

Purpose

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

Methods

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

Results

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

Conclusion

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

Systematic review registration

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

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.

Sonographic and Cytological Evaluation of Salivary Gland Tumors

INTRODUCTION

Salivary gland tumours are relatively uncommon, but they have a multifaceted clinical presentation and varied morphological configuration. The investigations required for these tumours remain debatable. We conducted a study to determine the accuracy of various modalities used in salivary gland tumours.

METHODS

We enrolled 72 subjects, consisting of 44 females and 28 males, with a mean age of 40.93 ± 16.51 years (range: 15 to 79 years), suffering from various salivary gland tumours. The tumour distribution included 42 parotid gland tumours (58.33%), followed by 21 submandibular gland tumours (29.16%), three sublingual gland tumours (4.16%), and six minor salivary gland tumours (8.33%). These individuals were subjected to clinical examination, sonography, and fine needle aspiration cytology as per indications. The results of each modality were compared to surgical pathology to find sensitivity and accuracy.

RESULTS

The clinical examination was found to be least sensitive (83.8%) as compared to FNAC (97.6%), and ultrasound (100%). Ultrasound had the highest diagnostic accuracy (86.2%) as compared to clinical examination (80.6%) and FNAC (82.6%).

CONCLUSION

Although sonography was found to have the highest sensitivity and accuracy as compared to fine needle aspiration cytology and clinical examination, the difference was subtle, as both sonography and fine needle aspiration cytology had a statistically significant correlation with histopathology.

A nomogram incorporating clinical, conventional ultrasound and shear wave elastography findings for distinguishing pleomorphic adenoma from Warthin's tumor of the major salivary glands

OBJECTIVE

Pre-operative differentiation between pleomorphic adenoma (PA) and Warthin's tumor (WT) of the major salivary glands is crucial for treatment decisions. The purpose of this study was to develop and validate a nomogram incorporating clinical, conventional ultrasound (CUS) and shear wave elastography (SWE) features to differentiate PA from WT.

METHODS

A total of 113 patients with histological diagnosis of PA or WT of the major salivary glands treated at Fujian Medical University Union Hospital were enrolled in training cohort (n = 75; PA = 41, WT = 34) and validation cohort (n = 38; PA = 22, WT = 16). The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening the most optimal clinical, CUS, and SWE features. Different models, including the nomogram model, clinic-CUS (Clin+CUS) and SWE model, were built using logistic regression. The performance levels of the models were evaluated and validated on the training and validation cohorts, and then compared among the three models.

RESULTS

The nomogram incorporating the clinical, CUS and SWE features showed favorable predictive value for differentiating PA from WT, with the area under the curves (AUCs) of 0.947 and 0.903 for the training cohort and validation cohort, respectively. Decision curve analysis showed that the nomogram model outperformed the Clin+CUS model and SWE model in terms of clinical usefulness.

CONCLUSIONS

The nomogram had good performance in distinguishing major salivary PA from WT and held potential for optimizing the clinical decision-making process.

Contrast-Enhanced Ultrasound and Strain Elastography for Differentiating Benign and Malignant Parotid Tumors

OBJECTIVES

Preoperative differentiation between benign parotid tumors (BPT) and malignant parotid tumors (MPT) is crucial for treatment decisions. The purpose of this study was to investigate the benefits of combining contrast-enhanced ultrasound (CEUS) and strain elastography (SE) for preoperative differentiation between BPT and MPT.

METHODS

A total of 115 patients with BPT (n=72) or MPT (n=43) who underwent ultrasound (US), SE, and CEUS were enrolled. US and CEUS features and the elasticity score were evaluated. Receiver operating characteristic curve (ROC) analysis was used to assess the diagnostic performance of SE, CEUS, and SE + CEUS with respect to identifying MPT from BPT.

RESULTS

Solitary presentation, larger diameter, irregular shape, ill-defined margin, heterogeneous echogenicity, and calcification on US and higher elasticity score on SE had a significant association with malignancy. MPT also presented an unclear margin, larger size after enhancement, and "fast-in and fast-out" pattern on CEUS. The combination of SE and CEUS was effective for differentiating MPT from BPT (AUC: 0.88, 0.80-0.95), with a sensitivity of 86.0%, specificity of 88.9%, and accuracy of 87.8%, which were significantly higher than the values for SE (AUC: 0.75, 0.66-0.85) and CEUS (AUC: 0.82, 0.73-0.91) alone.

CONCLUSION

The combination of CEUS and SE is valuable for distinguishing MPT from BPT.

Radiomics for Discriminating Benign and Malignant Salivary Gland Tumors; Which Radiomic Feature Categories and MRI Sequences Should Be Used?

The lack of a consistent MRI radiomic signature, partly due to the multitude of initial feature analyses, limits the widespread clinical application of radiomics for the discrimination of salivary gland tumors (SGTs). This study aimed to identify the optimal radiomics feature category and MRI sequence for characterizing SGTs, which could serve as a step towards obtaining a consensus on a radiomics signature. Preliminary radiomics models were built to discriminate malignant SGTs (n = 34) from benign SGTs (n = 57) on T1-weighted (T1WI), fat-suppressed (FS)-T2WI and contrast-enhanced (CE)-T1WI images using six feature categories. The discrimination performances of these preliminary models were evaluated using 5-fold-cross-validation with 100 repetitions and the area under the receiver operating characteristic curve (AUC). The differences between models’ performances were identified using one-way ANOVA. Results show that the best feature categories were logarithm for T1WI and CE-T1WI and exponential for FS-T2WI, with AUCs of 0.828, 0.754 and 0.819, respectively. These AUCs were higher than the AUCs obtained using all feature categories combined, which were 0.750, 0.707 and 0.774, respectively (p < 0.001). The highest AUC (0.846) was obtained using a combination of T1WI + logarithm and FS-T2WI + exponential features, which reduced the initial features by 94.0% (from 1015 × 3 to 91 × 2). CE-T1WI did not improve performance. Using one feature category rather than all feature categories combined reduced the number of initial features without compromising radiomic performance.

Diagnostic performance of elastosonography in the differential diagnosis of benign and malignant salivary gland tumors: A meta-analysis

Purpose

The clinical practice of elastosonography for the detection of salivary gland tumors is still a controversial issue. The objective of this meta-analysis was to evaluate the effect of elastosonography for the diagnosis of salivary gland tumors and to compare the diagnostic value of elastosonography and conventional ultrasound in the diagnosis of salivary gland tumors.

Methods

A comprehensive literature search through PubMed, EMBASE, and Cochrane Library was carried out from inception to November 2021. Two researchers independently extracted the data from the enrolled papers using a standard data extraction form. The pooled sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), diagnostic odds ratio (DOR), and area under the curve (AUC) were calculated to evaluate the diagnostic performance of elastosonography. The Quality Assessment of Diagnostic Accuracy Studies—2 (QUADAS-2) tool was utilized to evaluate the quality of each included study. Meta-DiSc version 1.4, Review Manager 5.3, and StataSE 15 were used.

Results

Sixteen studies with a total of 1105 patients with 1146 lesions were included in this meta-analysis. The pooled sensitivity, specificity, PLR, NLR, and DOR of elastosonography for the differentiation between benign and malignant salivary gland tumors were 0.73 (95%CI, 0.66–0.78), 0.64 (95%CI, 0.61–0.67), 2.83 (95%CI, 1.97–4.07), 0.45 (95%CI, 0.32–0.62), and 9.86 (95%CI, 4.49–21.62), respectively, with an AUC of 0.82. Four studies provided data regarding the conventional ultrasound for the differentiation between benign and malignant salivary gland tumors. The pooled sensitivity, specificity, and DOR were 0.62 (95%CI, 0.50–0.73), 0.93 (95%CI, 0.90–0.96), and 25.07 (95%CI, 4.28–146.65), respectively. The meta-regression and subgroup analyses found that assessment methods were associated with significant heterogeneity, and quantitative or semiquantitative elastosonography performed better than the qualitative one.

Conclusions

Elastosonography showed a limited value for diagnosing malignant salivary gland tumors; it could be considered as a supplementary diagnostic technology to conventional ultrasound, and quantitative or semiquantitative elastosonography was superior to the qualitative one.

Using deep learning to distinguish malignant from benign parotid tumors on plain computed tomography images

Objectives

Evaluating the diagnostic efficiency of deep-learning models to distinguish malignant from benign parotid tumors on plain computed tomography (CT) images.

Materials and methods

The CT images of 283 patients with parotid tumors were enrolled and analyzed retrospectively. Of them, 150 were benign and 133 were malignant according to pathology results. A total of 917 regions of interest of parotid tumors were cropped (456 benign and 461 malignant). Three deep-learning networks (ResNet50, VGG16_bn, and DenseNet169) were used for diagnosis (approximately 3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve [AUC]) of three networks were calculated and compared based on the 917 images. To simulate the process of human diagnosis, a voting model was developed at the end of the networks and the 283 tumors were classified as benign or malignant. Meanwhile, 917 tumor images were classified by two radiologists (A and B) and original CT images were classified by radiologist B. The diagnostic efficiencies of the three deep-learning network models (after voting) and the two radiologists were calculated.

Results

For the 917 CT images, ResNet50 presented high accuracy and sensitivity for diagnosing malignant parotid tumors; the accuracy, sensitivity, specificity, and AUC were 90.8%, 91.3%, 90.4%, and 0.96, respectively. For the 283 tumors, the accuracy, sensitivity, and specificity of ResNet50 (after voting) were 92.3%, 93.5% and 91.2%, respectively.

Conclusion

ResNet50 presented high sensitivity in distinguishing malignant from benign parotid tumors on plain CT images; this made it a promising auxiliary diagnostic method to screen malignant parotid tumors.

Small Study Effects in Diagnostic Imaging Accuracy: A Meta-Analysis

IMPORTANCE

Small study effects are the phenomena that studies with smaller sample sizes tend to report larger and more favorable effect estimates than studies with larger sample sizes.

OBJECTIVE

To evaluate the presence and extent of small study effects in diagnostic imaging accuracy meta-analyses.

DATA SOURCES

A search was conducted in the PubMed database for diagnostic imaging accuracy meta-analyses published between 2010 and 2019.

STUDY SELECTION

Meta-analyses with 10 or more studies of medical imaging diagnostic accuracy, assessing a single imaging modality, and providing 2 × 2 contingency data were included. Studies that did not assess diagnostic accuracy of medical imaging techniques, compared 2 or more imaging modalities or different methods of 1 imaging modality, were cost analyses, used predictive or prognostic tests, did not provide individual patient data, or were network meta-analyses were excluded.

DATA EXTRACTION AND SYNTHESIS

Data extraction was performed in accordance with the PRISMA guidelines.

MAIN OUTCOMES AND MEASURES

The diagnostic odds ratio (DOR) was calculated for each primary study using 2 × 2 contingency data. Regression analysis was used to examine the association between effect size estimate and precision across meta-analyses.

RESULTS

A total of 31 meta-analyses involving 668 primary studies and 80 206 patients were included. Fixed effects analysis produced a regression coefficient for the natural log of DOR against the SE of the natural log of DOR of 2.19 (95% CI, 1.49-2.90; P < .001), with computed tomography as the reference modality. Interaction test for modality and SE of the natural log of DOR did not depend on modality (Wald statistic P = .50). Taken together, this analysis found an inverse association between effect size estimate and precision that was independent of imaging modality. Of 26 meta-analyses that formally assessed for publication bias using funnel plots and statistical tests for funnel plot asymmetry, 21 found no evidence for such bias.

CONCLUSIONS AND RELEVANCE

This meta-analysis found evidence of widespread prevalence of small study effects in the diagnostic imaging accuracy literature. One likely contributor to the observed effects is publication bias, which can undermine the results of many meta-analyses. Conventional methods for detecting funnel plot asymmetry conducted by included studies appeared to underestimate the presence of small study effects. Further studies are required to elucidate the various factors that contribute to small study effects.

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.

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

OBJECTIVE：

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

METHODS AND MATERIALS

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

RESULTS

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

CONCLUSIONS

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

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

Objective

To investigate the value of the signal intensity on T2-weighted magnetic resonance (MR) imaging using quantitative analysis in the differentiation of parotid tumors.

Materials and Methods

MR data of 80 pleomorphic adenomas (PAs), 68 Warthin tumors (WTs), and 34 malignant tumors (MTs) confirmed by surgery and histology were retrospectively analyzed. The signal intensities of tumor, normal parotid gland, spinal cord, and buccal subcutaneous fat were measured, and the signal intensity ratios (SIRs) between the tumor and the three references were calculated. Receiver operating characteristic curve was used to determine the optimal threshold and diagnostic efficiency of SIR for differentiating PAs, WTs, and MTs.

Results

The area under the curve (AUC) of tumor to parotid gland SIR (SIR_P), tumor to spinal cord SIR (SIR_C), and tumor to buccal subcutaneous fat SIR (SIR_F) for differentiating PAs and WTs was 0.922, 0.918, and 0.934, respectively. The sensitivity and specificity at an optimal SIR threshold were 86.3% and 91.2%, 80.0% and 97.1%, and 85.0% and 94.1%, respectively. The AUC of SIR_P, SIR_C, and SIR_F for distinguishing PAs from MTs was 0.793, 0.802, and 0.774, respectively. The sensitivity and specificity at an optimal SIR threshold was 86.3% and 61.8%, 80.0% and 73.5%, and 82.5% and 73.5%, respectively. The AUC of SIR_P, SIR_C, and SIR_F for distinguishing WTs from MTs was 0.716, 0.709, and 0.759, respectively. The sensitivity and specificity at an optimal SIR threshold were 61.8% and 82.4%, 55.9% and 82.4%, and 64.7% and 86.8%, respectively.

Conclusion

SIR_P, SIR_C, and SIR_F on T2-weighted MR images had high diagnostic efficiency for differentiating between PAs and WTs, while SIR_P and SIR_C for differentiating between PAs and MTs, and SIR_F for differentiating between WTs and MTs had relatively high diagnostic efficiency.

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.

Can Ultrasound in Combination with Virtual Touch Imaging Quantification Predict the Dignity of a Parotid Tumor?

Pre-operative evaluation of a parotid gland tumor is crucial in guiding treatment. This study evaluates the diagnostic performance of B-mode ultrasound in combination with Virtual Touch imaging quantification (VTIQ) in the assessment of parotid lesions. A prospective study of 268 patients with parotid lesions was conducted. Pre-operative ultrasound findings and VTIQ data were compared against histologic results. Ill-defined margins on ultrasound were associated with a significantly higher risk of malignancy (odds ratio [OR] = 1224.0, 95 % confidence interval [CI]: 151.8-9872.7). Faster mean shear waves on VTIQ (OR = 1.81, 95% CI: 1.47-2.23, per 1 m/s increase) and an area with shear wave velocity >6.0 m/s involving >70 % of the lesion (OR = 19.80, 95 % CI: 6.22-63.07) were associated with higher risk of malignancy. Addition of VTIQ to routine pre-operative B-mode ultrasound can provide supplemental information on the dignity of a parotid tumor, allowing for peri-operative procedural optimization.

A CT-based radiomics nomogram for differentiation of lympho-associated benign and malignant lesions of the parotid gland

OBJECTIVES

Preoperative differentiation between benign lymphoepithelial lesion (BLEL) and mucosa-associated lymphoid tissue lymphoma (MALToma) in the parotid gland is important for treatment decisions. The purpose of this study was to develop and validate a CT-based radiomics nomogram combining radiomics signature and clinical factors for the preoperative differentiation of BLEL from MALToma in the parotid gland.

METHODS

A total of 101 patients with BLEL (n = 46) or MALToma (n = 55) were divided into a training set (n = 70) and validation set (n = 31). Radiomics features were extracted from non-contrast CT images, a radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. Demographics and CT findings were assessed to build a clinical factor model. A radiomics nomogram combining the Rad-score and independent clinical factors was constructed using multivariate logistic regression analysis. The performance levels of the nomogram, radiomics signature, and clinical model were evaluated and validated on the training and validation datasets, and then compared among the three models.

RESULTS

Seven features were used to build the radiomics signature. The radiomics nomogram incorporating the clinical factors and radiomics signature showed favorable predictive value for differentiating parotid BLEL from MALToma, with AUCs of 0.983 and 0.950 for the training set and validation set, respectively. Decision curve analysis showed that the nomogram outperformed the clinical factor model in terms of clinical usefulness.

CONCLUSIONS

The CT-based radiomics nomogram incorporating the Rad-score and clinical factors showed favorable predictive efficacy for differentiating BLEL from MALToma in the parotid gland, and may help in the clinical decision-making process.

KEY POINTS

• Differential diagnosis between BLEL and MALToma in parotid gland is rather difficult by conventional imaging modalities. • A radiomics nomogram integrated with the radiomics signature, demographics, and CT findings facilitates differentiation of BLEL from MALToma with improved diagnostic efficacy.

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.

Evaluation of benign parotid gland tumors with superb microvascular imaging and shear wave elastography

Aim of the study: This study aimed to examine the role of superb microvascular imaging and shear wave elastography for the pre-surgical evaluation of common parotid tumors. Material and methods: This single-center prospective study included 37 patients with parotid gland lesions. After institutional review board approval, grayscale, shear wave elastography and superb microvascular imaging ultrasound examinations were performed prior to biopsy or operation. The diagnosis of the lesions was based on cytological/pathological evaluation after the ultrasound examinations. Pleomorphic adenomas and Warthin tumors were compared using the Mann–Whitney U test. A receiver operating characteristic curve analysis was performed to obtain a cut-off value. A multivariate regression analysis was carried out. Results: The mean age of the patients (11 female, 26 male) was 48.2 ± 18. The shear wave elastography parameters of the lesions were not significantly different between pleomorphic adenomas and Warthin tumors, while the vascular index obtained by using superb microvascular imaging was significantly different (p = 0.012). The mean vascular index was 2.9 ± 3.1 in pleomorphic adenomas, and 9.5 ± 9.5 in Warthin tumors. A cut-off value of 4.05 for the vascular index discriminated pleomorphic adenoma and Warthin tumors with 68% sensitivity and 72% specificity (the area under the curve was 0.768). Conclusion: Superb microvascular imaging is a novel ultrasound imaging technique which is useful for the discrimination of pleomorphic adenomas and Warthin tumors.

Accuracy of transvaginal sonoelastography for differential diagnosis between malignant and benign cervical lesions: A systematic review and meta-analysis

BACKGROUND

To evaluate the performance of transvaginal sonoelastography (TVSE) for differential diagnosis between malignant and benign cervical lesions using a meta-analysis.

METHODS

An independent literature search was conducted on the English medical database, including PubMed, Embase and Medline, Cochrane Library, Web of Science, and OVID. The diagnostic accuracy of TVSE was compared with that of histopathology, which is the gold reference standard for diagnosis. The accuracy of TVSE was assessed by calculating the pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve (AUC). The imaging mechanisms, assessment methods, and QUADAS scores were assessed with a meta-regression analysis. A Deeks funnel plot was performed for evaluating publication bias.

RESULTS

Six eligible studies reported a total sample of 615 cervical lesions (415 cancers, 200 benign lesions). TVSE showed a pooled diagnostic odds ratio of 21.42 (95% CI 13.65-33.61), sensitivity of 0.87 (95% CI 0.84-0.90), specificity of 0.79 (95% CI 0.72-0.84), and an AUC of 0.892 (Q* = 0.822). The results of the meta-regression analysis showed that the imaging mechanism (P = .253), the assessment method (P = .279), or QUADAS score (P = .205) did not affect the study heterogeneity.

CONCLUSION

TVSE has a relatively high and satisfactory value for differential diagnosis between malignant and benign cervical lesions. The diagnostic performance of strain elastography and shear wave elastography were similar and good. However, to accommodate heterogeneity and publication bias, high-quality studies are required to further comparative effectiveness analyses to verify the efficacy of ultrasound detection.

Influence of epidemiological characteristics (age, parity and other factors) in the assessment of healthy uterine cervical stiffness evaluated through shear wave elastography as a prior step to its use in uterine cervical pathology

PURPOSE

The purpose of this study was to evaluate stiffness changes occurring in the healthy uterine cervix according to age, parity, phase of the menstrual cycle and other factors by shear wave elastography (SWE).

METHODS

Evaluations of cervical speed and stiffness measurements were performed in 50 non-pregnant patients without gynaecological pathology using SWE transvaginal ultrasound. We performed the evaluation in the midsagittal plane of the uterine cervix with measurements at 0.5, 1 and 1.5 cm from external cervical os, at both anterior and posterior cervical lips.

RESULTS

We evaluated 44 patients by SWE and obtained a total average velocity of 3.48 ± 1.08 m/s and stiffness of 42.39 ± 25.33 kPa. We found differences in speed and stiffness according to the cervical lip and depth evaluated; thus, we observed a velocity of 2.70 m/s at 0.5 cm of depth in the anterior lip and 3.53 m/s at 1.5 cm of depth in the posterior lip (p < 0.05). We observed differences according to parity, obtaining a wave transmission speed of 2.67 m/s and 4.41 m/s at the cervical canal of nulliparous and multiparous patients, respectively (p < 0 0.002). We observed differences according to patient age (from a speed of 2.75 m/s at the cervical canal in the age group of 20-35 years to 5.05 m/s in the age group > 50 years) (p < 0.008). We did not observe differences in speed or stiffness according to the phase of the menstrual cycle, BMI, smoking status or the presence or absence of non-HPV infections.

CONCLUSIONS

The wave transmission speed and stiffness of the uterine cervix evaluated by SWE varies according to the cervical lip and depth of the evaluation as well as according to the parity and age of the patient.

Characterization of parotid gland tumors: added value of permeability MR imaging to DWI and DCE-MRI

OBJECTIVES

To determine added value of permeability MRI in parotid tumor characterization to T2-weighted imaging (T2WI), semi-quantitative analysis of time-intensity curve (TIC), and intra-voxel incoherent motion diffusion-weighted imaging (IVIM-DWI).

METHODS

This retrospective study was approved by the institutional review board, and the informed consent was waived. Sixty-one parotid tumors in 61 patients were examined using T2WI, IVIM-DWI, and permeability MRI. TIC patterns were categorized as persistent, washout, or plateau. Signal intensity ratio of lesion-to-muscle on T2WI, apparent diffusion coefficients (ADCs), D and f values from IVIM-DWI, and K_trans, k_ep, V_e, and V_p values from permeability MRI were measured. Multiple comparisons were applied to determine whether any differences among 4 histopathologic types (pleomorphic adenomas, Warthin's tumors, other benign tumors, and malignant tumors) existed. Diagnostic accuracy was compared before and after modification diagnosis referring to permeability MRI. In a validation study, 60 parotid tumors in 60 patients were examined.

RESULTS

ADC and D values of malignant tumors were significantly lower than those of benign tumors other than Warthin's tumors, but higher than those of Warthin's tumors. k_ep and V_p values of Warthin's tumors were significantly higher than those of malignant tumors. Multivariate analyses showed that TIC pattern, D, and k_ep values were suitable parameters. McNemar's test showed a significant increase of sensitivity (11/12, 92%) and specificity (46/49, 94%) with adding k_ep. The validation study yielded high sensitivity (14/16, 88%) and specificity (41/44, 93%).

CONCLUSION

Permeability MRI offers added value to IVIM-MRI and semi-quantitative TIC analysis of DCE-MRI in characterization of parotid tumors KEY POINTS: • Permeability MR imaging offers added value in the characterization of parotid gland tumors in combination with semi-quantitative TIC analysis and IVIM analyses with D parameter. • The combination of TIC pattern, D, and k_ep might facilitate accurate characterization of parotid gland tumor, thereby avoiding unnecessary surgery for benign tumors or delayed treatment for malignant tumors. • A combination of permeability and diffusion MR imaging can be used to guide the selection of an appropriate biopsy site.