Parotid gland lesions: separate and combined diagnostic value of conventional MRI, diffusion-weighted imaging and dynamic contrast-enhanced MRI

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.

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

Purpose

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

Materials and methods

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

Results

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

Conclusion

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

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

Objective

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

Methods

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

Results

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

Conclusion

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

External auditory canal and middle ear tumors: characterization by morphology and diffusion features on CT and MRI

PURPOSE

To explore the value of morphology and diffusion features on CT and MRI in the characterization of external auditory canal and middle ear tumors (EAMETs).

METHODS

Forty-seven patients with histologically proved EAMETs (23 benign and 24 malignant) who underwent CT and MRI were retrospectively analyzed in this study. CT and MRI characteristics (including size, shape, signal intensity, border, enhancement degree, and bone changes) and apparent diffusion coefficient (ADC) value were analyzed and compared between benign and malignant EAMETs. Logistic regression, receiver operating characteristic (ROC) curve, and Delong test were performed to assess the diagnostic performance.

RESULTS

Compared with benign tumors, the malignant EAMETs are characterized by irregular shape, ill-defined border, invasive bone destruction, and intense enhancement (all p < 0.05). There were no significant differences on the size and signal intensity between benign and malignant tumors. The ADC value of malignant tumors were (879.96 ± 201.15) × 10^-6 mm²/s, which was significantly lower than benign ones (p < 0.05). Logistic regression demonstrates the presence of ill-defined margin, invasive bone destruction, and low ADC value (≤ 920.33 × 10^-6 mm²/s) have significant relationship with malignant EAMETs. The combination of characterization by morphology and diffusion features on CT and MRI can further improve the diagnostic efficiency when compared with morphology and diffusion features alone (both p < 0.05).

CONCLUSION

Some CT and MRI characteristics are helpful in identifying malignant EAMETs from benign ones (especially ill-defined margin, invasive bone destruction, and low ADC value), and the combination of morphology and diffusion features on CT and MRI has best diagnostic efficiency for discriminating these two entities.

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.

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 Benign From Malignant Parotid Gland Tumors Using Conventional MRI Based on Radiomics Nomogram

Objectives

We aimed to develop and validate radiomic nomograms to allow preoperative differentiation between benign- and malignant parotid gland tumors (BPGT and MPGT, respectively), as well as between pleomorphic adenomas (PAs) and Warthin tumors (WTs).

Materials and Methods

This retrospective study enrolled 183 parotid gland tumors (68 PAs, 62 WTs, and 53 MPGTs) and divided them into training (n = 128) and testing (n = 55) cohorts. In total, 2553 radiomics features were extracted from fat-saturated T2-weighted images, apparent diffusion coefficient maps, and contrast-enhanced T1-weighted images to construct single-, double-, and multi-sequence combined radiomics models, respectively. The radiomics score (Rad-score) was calculated using the best radiomics model and clinical features to develop the radiomics nomogram. The receiver operating characteristic curve and area under the curve (AUC) were used to assess these models, and their performances were compared using DeLong’s test. Calibration curves and decision curve analysis were used to assess the clinical usefulness of these models.

Results

The multi-sequence combined radiomics model exhibited better differentiation performance (BPGT vs. MPGT, AUC=0.863; PA vs. MPGT, AUC=0.929; WT vs. MPGT, AUC=0.825; PA vs. WT, AUC=0.927) than the single- and double sequence radiomics models. The nomogram based on the multi-sequence combined radiomics model and clinical features attained an improved classification performance (BPGT vs. MPGT, AUC=0.907; PA vs. MPGT, AUC=0.961; WT vs. MPGT, AUC=0.879; PA vs. WT, AUC=0.967).

Conclusions

Radiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors

Objective

This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs).

Methods

This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves.

Results

Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888).

Conclusion

Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.

Machine learning-based radiomics for histological classification of parotid tumors using morphological MRI: a comparative study

OBJECTIVES

To evaluate the effectiveness of machine learning models based on morphological magnetic resonance imaging (MRI) radiomics in the classification of parotid tumors.

METHODS

In total, 298 patients with parotid tumors were randomly assigned to a training and test set at a ratio of 7:3. Radiomics features were extracted from the morphological MRI images and screened using the Select K Best and LASSO algorithm. Three-step machine learning models with XGBoost, SVM, and DT algorithms were developed to classify the parotid neoplasms into four subtypes. The ROC curve was used to measure the performance in each step. Diagnostic confusion matrices of these models were calculated for the test cohort and compared with those of the radiologists.

RESULTS

Six, twelve, and eight optimal features were selected in each step of the three-step process, respectively. XGBoost produced the highest area under the curve (AUC) for all three steps in the training cohort (0.857, 0.882, and 0.908, respectively), and for the first step in the test cohort (0.826), but produced slightly lower AUCs than SVM in the latter two steps in the test cohort (0.817 vs. 0.833, and 0.789 vs. 0.821, respectively). The total accuracies of XGBoost and SVM in the confusion matrices (70.8% and 59.6%) outperformed those of DT and the radiologist (46.1% and 49.2%).

CONCLUSION

This study demonstrated that machine learning models based on morphological MRI radiomics might be an assistive tool for parotid tumor classification, especially for preliminary screening in absence of more advanced scanning sequences, such as DWI.

KEY POINTS

• Machine learning algorithms combined with morphological MRI radiomics could be useful in the preliminary classification of parotid tumors. • XGBoost algorithm performed better than SVM and DT in subtype differentiation of parotid tumors, while DT seemed to have a poor validation performance. • Using morphological MRI only, the XGBoost and SVM algorithms outperformed radiologists in the four-type classification task for parotid tumors, thus making these models a useful assistant diagnostic tool in clinical practice.

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.

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.

Machine learning-based multiparametric traditional multislice computed tomography radiomics for improving the discrimination of parotid neoplasms

Characterization of parotid tumors is important for treatment planning and prognosis, and parotid tumor discrimination has recently been developed at the molecular level. The aim of the present study was to establish a machine learning (ML) predictive model based on multiparametric traditional multislice CT (MSCT) radiomic and clinical data analysis to improve the accuracy of differentiation among pleomorphic adenoma (PA), Warthin tumor (WT) and parotid carcinoma (PCa). A total of 345 patients (200 with WT, 91 with PA and 54 with PCa) with pathologically confirmed parotid tumors were retrospectively enrolled from five independent institutions between January 2010 and May 2019. A total of 273 patients recruited from institutions 1, 2 and 3 were randomly assigned to the training model; the independent validation set consisted of 72 patients treated at institutions 1, 4 and 5. Data were investigated using a linear discriminant analysis-based ML classifier. Feature selection and dimension reduction were conducted using reproducibility testing and a wrapper method. The diagnostic accuracy of the predictive model was compared with histopathological findings as reference results. This classifier achieved a satisfactory performance for the discrimination of PA, WT and PCa, with a total accuracy of 82.1% in the training cohort and 80.5% in the validation cohort. In conclusion, ML-based multiparametric traditional MSCT radiomics can improve the accuracy of differentiation among PA, WT and PCa. The findings of the present study should be validated by multicenter prospective studies using completely independent external data.

Diagnostic performance of qualitative and radiomics approach to parotid gland tumors: which is the added benefit of texture analysis?

OBJECTIVE

To investigate whether MRI-based texture analysis improves diagnostic performance for the diagnosis of parotid gland tumors compared to conventional radiological approach.

METHODS

Patients with parotid gland tumors who underwent salivary glands MRI between 2008 and 2019 were retrospectively selected. MRI analysis included a qualitative assessment by two radiologists (one of which subspecialized on head and neck imaging), and texture analysis on various sequences. Diagnostic performances including sensitivity, specificity, and area under the receiver operating characteristic curve (AUROC) of qualitative features, radiologists' diagnosis, and radiomic models were evaluated.

RESULTS

Final study cohort included 57 patients with 74 tumors (27 pleomorphic adenomas, 40 Warthin tumors, 8 malignant tumors). Sensitivity, specificity, and AUROC for the diagnosis of malignancy were 75%, 97% and 0.860 for non-subspecialized radiologist, 100%, 94% and 0.970 for subspecialized radiologist and 57.2%, 93.4%, and 0.927 using a MRI radiomics model obtained combining texture analysis on various MRI sequences. Sensitivity, specificity, and AUROC for the differential diagnosis between pleomorphic adenoma and Warthin tumors were 81.5%, 70%, and 0.757 for non-subspecialized radiologist, 81.5%, 95% and 0.882 for subspecialized radiologist and 70.8%, 82.5%, and 0.808 using a MRI radiomics model based on texture analysis of T₂ weighted sequence. A combined radiomics model obtained with all MRI sequences yielded a sensitivity of 91.5% for the diagnosis of pleomorphic adenoma.

CONCLUSION

MRI qualitative radiologist assessment outperforms radiomic analysis for the diagnosis of malignancy. MRI predictive radiomics models improves the diagnostic performance of non-subspecialized radiologist for the differential diagnosis between pleomorphic adenoma and Warthin tumor, achieving similar performance to the subspecialized radiologist.

ADVANCES IN KNOWLEDGE

Radiologists outperform radiomic analysis for the diagnosis of malignant parotid gland tumors, with some MRI qualitative features such as ill-defined margins, perineural spread, invasion of adjacent structures and enlarged lymph nodes being highly specific for malignancy. A radiomic model based on texture analysis of T₂ weighted images yields higher specificity for the diagnosis of pleomorphic adenoma compared to a radiologist non-subspecialized in head and neck radiology, thus minimizing false-positive pleomorphic adenoma diagnosis rate and reducing unnecessary surgical complications.

Feasibility evaluation of amide proton transfer-weighted imaging in the parotid glands: a strategy to recognize artifacts and measure APT value

Background

The feasibility and image quality of three-dimensional (3D) amide proton transfer (APT)-weighted (APTw) in parotid tumor lesions have not been well established in previous studies. This study aimed to evaluate the utility of APT imaging in parotid lesions and glands.

Methods

Patients with parotid lesions received 3D turbo spin echo (TSE) APTw on a 3.0T scanner. Two radiologists, who were blinded to the clinical data, independently evaluated the APTw image quality using 4-point Likert scales (1= poor, 4= excellent) in terms of integrity and hyperintensity artifacts. An image quality selection protocol was built based on the two scores. Evaluable images (integrity score >1) and trustable images (integrity score >3 and hyperintensity artifacts score >2) were then enrolled for APTw value comparison between parotid lesions and glands.

Results

Forty consecutive patients were included in this study. Four patients were excluded due to severe motion (n=3) or dental (n=1) artifacts, and 36 patients received the APT sequence. Among these, more parotid tumor lesions (34/36, 94.4%) than normal parotid glands (23/31, 74.2%) revealed excellent integrity scores (score =4) (P=0.034). Most parotid tumor lesions (24/34, 70.6%) and glands (16/28, 57.1%) revealed no or little hyperintensity artifacts for diagnosis (scores 3 and 4). APT values of parotid lesions and glands in the evaluable groups were 2.11%±1.15% and 1.60%±1.56%, respectively, and the difference was not significant (P=0.197). APT values of parotid lesions and glands in the trustable groups were 1.99%±1.18% and 1.03%±1.09%, respectively, and the difference was statistically significant (P=0.018).

Conclusions

3D APTw could be used to differentiate parotid tumors and normal parotid glands; however, the technology still needs to be improved to remove artifacts. In our study, most APTw images of tumor lesions in parotid glands had acceptable image quality, and these APTw images are feasible for diagnostic use.

Salivary gland carcinoma in children and adolescents: The EXPeRT/PARTNER diagnosis and treatment recommendations

Salivary gland carcinomas (SGCs) are rare during childhood and adolescence. Consequently, no standardized recommendations for the diagnosis and therapeutic management of pediatric SGC are available, and pediatric oncologists and surgeons generally follow adult guidelines. Complete surgical resection with adequate margins constitutes the cornerstone of treatment. However, the indications and modalities of adjuvant therapy remain controversial and may be challenging in view of the potential long-term toxicities in the pediatric population. This paper presents the consensus recommendations for the diagnosis and treatment of children and adolescents with SGCs, established by the European Cooperative Study Group for Pediatric Rare Tumors (EXPeRT) within the EU-funded PARTNER project (Paediatric Rare Tumours Network - European Registry).

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.

Management of Salivary Gland Malignancy: ASCO Guideline

PURPOSE

To provide evidence-based recommendations for practicing physicians and other healthcare providers on the management of salivary gland malignancy.

METHODS

ASCO convened an Expert Panel of medical oncology, surgical oncology, radiation oncology, neuroradiology, pathology, and patient advocacy experts to conduct a literature search, which included systematic reviews, meta-analyses, randomized controlled trials, and prospective and retrospective comparative observational studies published from 2000 through 2020. Outcomes of interest included survival, diagnostic accuracy, disease recurrence, and quality of life. Expert Panel members used available evidence and informal consensus to develop evidence-based guideline recommendations.

RESULTS

The literature search identified 293 relevant studies to inform the evidence base for this guideline. Six main clinical questions were addressed, which included subquestions on preoperative evaluations, surgical diagnostic and therapeutic procedures, appropriate radiotherapy techniques, the role of systemic therapy, and follow-up evaluations.

RECOMMENDATIONS

When possible, evidence-based recommendations were developed to address the diagnosis and appropriate preoperative evaluations for patients with a salivary gland malignancy, therapeutic procedures, and appropriate treatment options in various salivary gland histologies.Additional information is available at www.asco.org/head-neck-cancer-guidelines.

Magnetic resonance imaging of salivary gland tumours: Key findings for imaging characterisation

Salivary gland tumours are rare, representing only 3% of all head and neck neoplasms, with the parotid gland being the most common site (80 %). The risk of malignancy is inversely proportional to the size of the gland: lesions arising in the sublingual or minor salivary glands are more likely to be malignant, whereas parotid gland neoplasms are mostly benign. Fine needle aspiration cytology and core needle biopsy are considered the most accurate modalities for the diagnosis of a salivary gland neoplasm; however, they are not always conclusive due to procedural sampling errors and for the presence of a cytological / histological overlap between benign and malignant tumours. Moreover, they cannot be easily performed for parotid deep portion localisation. The role of magnetic resonance imaging (MRI) is growing and advanced techniques (diffusion-weighted and dynamic contrast-enhanced perfusion-weighted imaging) can provide useful additional information for the assessment of salivary gland neoplasms. The aim of this review is to present the main MRI and clinical features of salivary gland tumours to improve their comprehensive evaluation and characterisation.

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.

Radiomic model for differentiating parotid pleomorphic adenoma from parotid adenolymphoma based on MRI images

Background

Distinguishing parotid pleomorphic adenoma (PPA) from parotid adenolymphoma (PA) is important for precision treatment, but there is a lack of readily available diagnostic methods. In this study, we aimed to explore the diagnostic value of radiomic signatures based on magnetic resonance imaging (MRI) for PPA and PA.

Methods

The clinical characteristic and imaging data were retrospectively collected from 252 cases (126 cases in the training cohort and 76 patients in the validation cohort) in this study. Radiomic features were extracted from MRI scans, including T1-weighted imaging (T1WI) sequences and T2-weighted imaging (T2WI) sequences. The radiomic features from three sequences (T1WI, T2WI and T1WI combined with T2WI) were selected using univariate analysis, LASSO correlation and Spearman correlation. Then, we built six quantitative radiomic models using the selected features through two machine learning methods (multivariable logistic regression, MLR, and support vector machine, SVM). The performances of the six radiomic models were assessed and the diagnostic efficacies of the ideal T1-2WI radiomic model and the clinical model were compared.

Results

The T1-2WI radiomic model using MLR showed optimal discriminatory ability (accuracy = 0.87 and 0.86, F-1 score = 0.88 and 0.86, sensitivity = 0.90 and 0.88, specificity = 0.82 and 0.80, positive predictive value = 0.86 and 0.84, negative predictive value = 0.86 and 0.84 in the training and validation cohorts, respectively) and its calibration was observed to be good (p > 0.05). The area under the curve (AUC) of the T1-2WI radiomic model was significantly better than that of the clinical model for both the training (0.95 vs. 0.67, p < 0.001) and validation (0.90 vs. 0.68, p = 0.001) cohorts.

Conclusions

The T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-021-00581-9.

Cross-sectional imaging and cytologic investigations in the preoperative diagnosis of parotid gland tumors - An updated literature review

An accurate preoperative diagnosis of parotid tumors is essential for the selection and planning of surgical treatment. Various modern cross-sectional imaging and cytologic investigations can support the differential diagnosis of parotid tumors. The aim of this study was to achieve a comprehensive and updated review of modern imaging and cytologic investigations used in parotid tumor diagnosis, based on the latest literature data. This literature review could serve as a guide for clinicians in selecting different types of investigations for the preoperative differential diagnosis of parotid tumors. Magnetic resonance imaging (MRI) with its dynamic and advanced sequences is the first-line imaging investigation used in differentiating parotid tumors. Computed tomography (CT) and positron emission tomography (PET)-CT provide limited indications in differentiating parotid tumors. Fine needle aspiration biopsy and core needle biopsy can contribute with satisfactory results to the cytological diagnosis of parotid tumors. Dynamic MRI with its dynamic contrast-enhanced and diffusion-weighted sequences provides the best accuracy for the preoperative differential diagnosis of parotid tumors. CT allows the best evaluation of bone invasion, being useful when MRI cannot be performed, and PET-CT has value in the follow-up of cancer patients. The dual cytological and imaging approach is the safest method for an accurate differential diagnosis of parotid tumors.

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.

Multiparametric magnetic resonance imaging of parotid tumors: A systematic review

PURPOSE

The purpose of this systematic review was to provide an overview of the contribution of multiparametric magnetic resonance imaging (MRI) in the diagnosis of parotid tumors (PT) and recommendations based on current evidences.

MATERIAL AND METHODS

We performed a retrospective systematic search of PubMed, EMBASE, and Cochrane Library databases from inception to January 2020, using the keywords "magnetic resonance imaging" and "salivary gland neoplasms".

RESULTS

The initial search returned 2345 references and 90 were deemed relevant for this study. A total of 54 studies (60%) reported the use of diffusion-weighted imaging (DWI) and 28 studies (31%) the use of dynamic contrast-enhanced (DCE) imaging. Specific morphologic signs of frequent benign PT and suggestive signs of malignancy on conventional sequences were reported in 37 studies (41%). DWI showed significant differences in apparent diffusion coefficient (ADC) values between benign and malignant PT, and especially between pleomorphic adenomas and malignant PT, with cut-off ADC values between 1.267×10^-3mm²/s and 1.60×10^-3mm²/s. Perfusion curves obtained with DCE imaging allowed differentiating among pleomorphic adenomas, Warthin's tumors, malignant PT and cystic lesions. The combination of morphological MRI sequences, DCE imaging and DWI helped increase the diagnostic accuracy of MRI.

CONCLUSION

Multiparametric MRI, including morphological MRI sequences, DWI and DCE imaging, is the imaging modality of choice for the characterization of focal PT and provides features that are highly suggestive of a specific diagnosis.

Feasibility study of using simultaneous multi-slice RESOLVE diffusion weighted imaging to assess parotid gland tumors: comparison with conventional RESOLVE diffusion weighted imaging

Background

To evaluate the feasibility of using simultaneous multi-slice (SMS) readout segmentation of long variable echo-trains (RESOLVE) diffusion-weighted imaging (DWI) to assess parotid gland tumors, compared with conventional RESOLVE DWI.

Methods

From September 2018 to December 2018, 20 consecutive patients with parotid tumors who underwent MRI scan for pre-surgery evaluation were enrolled. SMS-RESOLVE DWI and conventional RESOLVE DWI were scanned with matched imaging parameters, respectively. The scan time of two DWI sequences was recorded. Qualitative (anatomical structure differentiation, lesion display, artifact, and overall image quality) and quantitative (apparent diffusion coefficient, ADC; ratio of signal-to-noise ratio, SNR ratio; ratio of contrast-to-noise ratio, CNR ratio) assessments of image quality were performed, and compared between SMS-RESOLVE DWI and conventional RESOLVE DWI by using Paired t-test. Two-sided P value less than 0.05 indicated significant difference.

Results

The scan time was 3 min and 41 s for SMS-RESOLVE DWI, and 5 min and 46 s for conventional RESOLVE DWI. SMS-RESOLVE DWI produced similar qualitative image quality with RESOLVE DWI (anatomical structure differentiation, P = 0.164; lesion display, P = 0.193; artifact, P = 0.330; overall image quality, P = 0.083). Meanwhile, there were no significant difference on ADC_Lesion (P = 0.298), ADC_Masseter (P = 0.122), SNR ratio (P = 0.584) and CNR ratio (P = 0.217) between two DWI sequences.

Conclusion

Compared with conventional RESOLVE DWI, SMS-RESOLVE DWI could provide comparable image quality using markedly reduced scan time. SMS could increase the clinical usability of RESOLVE technique for DWI of parotid gland.

Clinical utility of apparent diffusion coefficient and diffusion-weighted magnetic resonance imaging for resectability assessment of head and neck tumors with skull base invasion

BACKGROUND

The usefulness of apparent diffusion coefficient (ADC) and diffusion-weighted magnetic resonance imaging (DWI) in the detection of malignant tumors has been reported. The purpose of this study is to clarify the role of ADC and DWI for diagnosis of skull base tumors.

METHODS

A total of 27 patients with head and neck tumors with skull base invasions undergoing skull base surgery were enrolled in this study. Pathological findings of dural invasion and bone invasion were compared with the diagnostic imaging.

RESULTS

Advanced magnetic resonance imaging techniques revealed that ADC values in regions of pathological bone and dural invasions were significantly lower than in regions of no invasion. The area under the curve of ADC in bone invasions and dural invasions were 0.957 and 0.894, respectively.

CONCLUSIONS

Our findings indicate that ADC and DWI are useful tools for the diagnosis of head and neck tumors with skull base invasion.

Controversies in the Workup and Surgical Management of Parotid Neoplasms

OBJECTIVE

Parotid neoplasms are a rare heterogeneous group of tumors with varied clinical presentation and behavior. Here we provide an evidence-based review of the contemporary approach to evaluation and surgical management of parotid tumors.

DATA SOURCE

PubMed and Web of Science Databases.

REVIEW METHODS

Searches of the PubMed and Web of Science databases were performed on subjects related to the diagnosis and surgical management of parotid neoplasms. Particular emphasis was placed on the following areas: evaluation of parotid tumors, including imaging workup and the utility of fine-needle aspiration; extent of surgery of the primary lesion, including the extent of parotidectomy as well as oncologic management of the facial nerve; the extent of surgery of involved and at-risk cervical lymphatics; and parotid bed reconstruction. Articles published from 2014 to the present were prioritized, supplementing with information from prior studies in areas where data are lacking.

CONCLUSION

A summary of the literature in these areas is outlined to provide an evidence-based approach to evaluation and management of parotid neoplasms.

IMPLICATIONS FOR PRACTICE

While data are available to help guide many aspects of workup and management of parotid neoplasms, further research is needed to refine protocols for this heterogeneous group of diseases.

Diffusion MR Imaging in the Head and Neck: Principles and Applications

Diffusion imaging is a functional MR imaging tool that creates tissue contrast representative of the random, microscopic translational motion of water molecules within human body tissues. Long considered a cornerstone MR imaging sequence for brain imaging, diffusion-weighted imaging (DWI) increasingly is used for head and neck imaging. This review reports the current state of diffusion techniques for head and neck imaging, including conventional DWI, DWI trace with apparent diffusion coefficient map, diffusion tensor imaging, intravoxel incoherent motion, and diffusion kurtosis imaging. This article describes background physics, reports supportive evidence and potential pitfalls, highlights technical advances, and details practical clinical applications.

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

PURPOSE

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

METHODS

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

RESULTS

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

CONCLUSION

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

A Preliminary Study of CT Texture Analysis for Characterizing Epithelial Tumors of the Parotid Gland

Objective

The aim of this study was to explore and validate the diagnostic performance of whole-volume CT texture features in differentiating the common benign and malignant epithelial tumors of the parotid gland.

Materials and Methods

Contrast-enhanced CT images of 83 patients with common benign and malignant epithelial tumors of the parotid gland confirmed by histopathology were retrospectively analyzed, including 50 patients with pleomorphic adenoma (PA) and 33 patients with malignant epithelial tumors. Quantitative texture features of tumors were extracted from CT images of arterial phase. The diagnostic performance of texture features was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC). The specificity and sensitivity were respectively discussed by the maximum Youden’s index.

Results

All the texture features were subject to normal distribution and homoscedasticity. Energy, mean, correlation, and sum entropy of epithelial malignancy group were significantly higher than those of PA group (P<0.05). There were no statistically significant differences between PA group and epithelial malignancy group in uniformity, entropy, skewness, kurtosis, contrast, and difference entropy (P>0.05). The AUC of each texture feature and joint diagnostic model was 0.887 (energy), 0.734 (mean), 0.739 (correlation), 0.623 (sum entropy), 0.888 (energy-mean), 0.883 (energy-correlation), 0.784 (mean-correlation). The diagnostic efficiency of energy-mean was the best. Based on the maximum Youden’s index, the specificity of energy-correlation was the highest (97%) and the sensitivity of energy was the highest (97%).

Conclusion

Energy, mean, correlation, and sum entropy can be the effective quantitative texture features to differentiate the benign and malignant epithelial tumors of the parotid gland. With higher AUC, energy and energy-mean are superior to other indexes or joint diagnostic models in differentiating the benign and malignant epithelial tumors of the parotid gland. CT texture analysis can be used as a noninvasive and valuable means of preoperative assessment of parotid epithelial tumors without additional cost to the patients.

The role of diffusion-weighted and dynamic contrast enhancement perfusion-weighted imaging in the evaluation of salivary glands neoplasms

OBJECTIVES

To evaluate the association of magnetic resonance diffusion-weighted imaging (DwI) and dynamic contrast-enhanced perfusion-weighted imaging (DCE-PwI) with a temporal resolution of 5 s, wash-in < 120 s, and wash-out ratio > 30% in the evaluation of salivary glands neoplasms.

METHODS

DwI and DCE-PwI of 92 salivary glands neoplasms were assessed. The apparent diffusion coefficient (ADC) was calculated by drawing three regions of interest with an average area of 0.30-0.40 cm² on three contiguous axial sections. The time/intensity curve was generated from DCE-PwI images by drawing a region of interest that included at least 50% of the largest lesion section. Vessels, calcifications, and necrotic/haemorrhagic or cystic areas within solid components were excluded. The association of ADC ≥ 1.4 × 10^-3 mm²/s with type A curves (progressive wash-in) and ADC 0.9-1.4 × 10^-3 mm²/s with type C curves (rapid wash-in/slow wash-out) were tested as parameters of benignity and malignancy, respectively. Type B curve (rapid wash-in/rapid wash-out) was not used as a reference parameter.

RESULTS

ADC ≥ 1.4 × 10^-3 mm²/s and type A curves were observed only in benign neoplasms. ADC of 0.9-1.4 × 10^-3 mm²/s and type C curves association showed specificity of 94.9% and positive predictive value of 81.8% for epithelial malignancies. The association of ADC < 0.9 × 10^-3 mm²/s with type B and C curves showed diagnostic accuracy of 94.6% and 100% for Warthin tumour and lymphoma, respectively.

CONCLUSIONS

ADC ≥ 1.4 × 10^-3 mm²/s and type A curves association was indicative of benignity. Lymphomas exhibited ADC < 0.7 × 10^-3 mm²/s and type C curves. The association of ADC < 0.9 × 10^-3 mm²/s and type B and C curves had accuracy 94.6% and 88.5% for Warthin tumour and epithelial malignancies, respectively.

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

The majority of salivary gland tumors occur in the parotid glands. Characterization (ie, benign or malignant, and histological type), location (deep or superficial), and invasion into the neighboring tissues of parotid tumors determine preoperative treatment planning. MRI gives more information than other imaging methods about the internal structure, localization, and relationship with other tissues of parotid tumors. Functional MRI methods (diffusion-weighted imaging, dynamic contrast-enhanced MRI, perfusion-weighted MRI, MR spectroscopy, etc.) have been increasingly used recently to increase the power of radiologists to characterize the tumors. Although they increase the workload of radiologists, the combined use of functional MRI methods improves accuracy in the differentiation of the tumors. There are a wide range of studies in the literature dealing with the combined use of different functional imaging methods in combination with conventional sequences. The aim of the present review is to evaluate conventional and functional/advanced MR methods, as well as multiparametric MRI applications combining them in the diagnosis of parotid gland tumors. Evidence Level: 5 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;52:11-32.

Recent advances in MRI of the head and neck, skull base and cranial nerves: new and evolving sequences, analyses and clinical applications

MRI is an invaluable diagnostic tool in the investigation and management of patients with pathology of the head and neck. However, numerous technical challenges exist, owing to a combination of fine anatomical detail, complex geometry (that is subject to frequent motion) and susceptibility effects from both endogenous structures and exogenous implants. Over recent years, there have been rapid developments in several aspects of head and neck imaging including higher resolution, isotropic 3D sequences, diffusion-weighted and diffusion-tensor imaging as well as permeability and perfusion imaging. These have led to improvements in anatomic, dynamic and functional imaging. Further developments using contrast-enhanced 3D FLAIR for the delineation of endolymphatic structures and black bone imaging for osseous structures are opening new diagnostic avenues. Furthermore, technical advances in compressed sensing and metal artefact reduction have the capacity to improve imaging speed and quality, respectively. This review explores novel and evolving MRI sequences that can be employed to evaluate diseases of the head and neck, including the skull base.

A multiparametric analysis based on DCE-MRI to improve the accuracy of parotid tumor discrimination

BACKGROUND

Recently, semiquantitative time-intensity curve (TIC) analysis based on DCE-MRI and apparent diffusion coefficient (ADC) value-based diffusion-weighted imaging (DWI) were used to improve the diagnostic efficiency when diagnosing parotid tumors (PTs). However, quantitative DCE-MRI biomarkers have not been emphasized previously.

PURPOSE

To explore the diagnostic efficiency of perfusion parameters alone or in combination based on quantitative DCE-MRI and DWI in the differential diagnosis of PTs.

METHODS

In total, 112 patients with parotid masses were prospectively recruited in our hospital from August 2013 to March 2017. All patients were evaluated with DCE-MRI and DWI before surgery. TIC and quantitative parameters based on DCE MRI and ADCs were analyzed. Receiver operating characteristic analysis and linear discriminant analysis (LDA) was used to determine their diagnostic performance.

RESULTS

In total, 87% (27/31) of pleomorphic adenoma (PA) showed type A TIC, 74% (65/88) of Warthin's tumors showed type B TIC, and 95% (19/20) of malignant tumors showed TIC type C. Pearson X² test showed a significant difference between TIC patterns in benign and malignant tumors (X² = 38.78, p < 0.001). ROC analysis revealed that ADC achieved the best diagnostic performance for distinguishing PA and Warthin's tumor from others, with area under the curve (AUC) values of 0.945 and 0.925 (p < 0.01), respectively. Furthermore, the TIC type was the only useful biomarker for distinguishing malignant from benign PTs, with an AUC of 0.846 (p < 0.01). Concerning the accuracy of the combined application of multiple parameters of DCE-MRI and ADC values, a combination of TIC pattern and extracellular volume ratio (Ve) provided the best results among five protocols, producing the highest accuracy of 0.75, followed by the combined use of the TIC pattern and ADC (accuracy was 0.70).

CONCLUSION

TIC pattern in combination with the Ve biomarker based on DCE-MRI could achieve optimal diagnostic accuracy in the differential diagnosis of PTs.

Dynamic Contrast-Enhanced MRI to Differentiate Parotid Neoplasms Using Golden-Angle Radial Sparse Parallel Imaging

BACKGROUND AND PURPOSE

Conventional imaging frequently shows overlapping features between benign and malignant parotid neoplasms. We investigated dynamic contrast-enhanced MR imaging using golden-angle radial sparse parallel imaging in differentiating parotid neoplasms.

MATERIALS AND METHODS

For this retrospective study, 41 consecutive parotid neoplasms were imaged with dynamic contrast-enhanced MR imaging with golden-angle radial sparse parallel imaging using 1-mm in-plane resolution. The temporal resolution was 3.4 seconds for 78.2 seconds and 8.8 seconds for the remaining acquisition. Three readers retrospectively and independently created and classified time-intensity curves as follows: 1) continuous wash-in; 2) rapid wash-in, subsequent plateau; and 3) rapid wash-in with washout. Additionally, time-intensity curve-derived semiquantitative metrics normalized to the ipsilateral common carotid artery were recorded. Diagnostic performance for the prediction of neoplasm type and malignancy was assessed. Subset multivariate analysis (n = 32) combined semiquantitative time-intensity curve metrics with ADC values.

RESULTS

Independent time-intensity curve classification of the 41 neoplasms produced moderate-to-substantial interreader agreement (κ = 0.50-0.79). The time-intensity curve classification threshold of ≥2 predicted malignancy with a positive predictive value of 56.0%-66.7%, and a negative predictive value of 92.0%-100%. The time-intensity curve classification threshold of <2 predicted pleomorphic adenoma with a positive predictive value of 87.0%-95.0% and a negative predictive value of 76.0%-95.0%. For all readers, type 2 and 3 curves were associated with malignant neoplasms (P < .001), and type 1 curves, with pleomorphic adenomas (P < .001). Semiquantitative analysis for malignancy prediction yielded an area under the receiver operating characteristic curve of 0.85 (95% CI, 0.73-0.99). Combining time-to-maximum and ADC predicts pleomorphic adenoma better than either metric alone (P < .001).

CONCLUSIONS

Golden-angle radial sparse parallel MR imaging allows high spatial and temporal resolution permeability characterization of parotid neoplasms, with a high negative predictive value for malignancy prediction. Combining time-to-maximum and ADC improves pleomorphic adenoma prediction compared with either metric alone.

Differential diagnostic value of diffusion-weighted and dynamic contrast-enhanced MR imaging in non-cystic lesions in floor of the mouth

METHODS:

A total of 82 patients were included in this study. The apparent diffusion coefficient (ADC) values and time-signal intensity curves (TICs) were measured. Clinical characteristics, ADC value, and TIC pattern were compared between benign and malignant FOM lesions. Receiver operating characteristic curve and logistic regression analyses were performed to evaluate respective and combined value of ADC value and TIC pattern for differential diagnosis. The retrospective study was approved by our institutional review board, and the need for informed consent was waived.

RESULTS:

The area under the curve ADC value and TIC pattern were 0.71 and 0.73, respectively. The combined use of ADC value and TIC pattern increased the area under the curve value to 0.81 [95% confidence interval (CI), (0.66-0.97)]. ADC < 1.23 × 10^-3 mm² s^-1 (odds ratio, 45.8; 95% CI, 2.8-737.9) and both the plateau and washout TIC patterns (OR, 6.8; 95% CI, 1.8-24.8) were significantly associated with malignancy of FOM lesions.

CONCLUSIONS:

Our results suggest that both diffusion-weighted imaging and DCE-MRI could contribute to the differential diagnosis of non-cystic FOM lesions, especially when used in combination.

Multimodality fMRI with perfusion, diffusion-weighted MRI and 1 H-MRS in the diagnosis of lympho-associated benign and malignant lesions of the parotid gland

BACKGROUND

Differential diagnosis of the mucosa-associated lymphoid tissue lymphoma (MALToma) and tumor-like benign lymphoepithelial lesion (BLEL) in the parotid gland is difficult.

PURPOSE

To distinguish MALToma and BLEL with multimodality MRI including hydrogenproton magnetic resonance spectroscopy (¹ H-MRS), diffusion-weighted imaging (DWI-MR), and dynamic contrast-enhanced (DCE-MR), and evaluate each sequence.

STUDY TYPE

Retrospective.

POPULATION

Twenty-five patients with parotid tumor-like BLEL and 20 with parotid MALToma.

FIELD STRENGTH/SEQUENCE

1.5-T/T₁ WI, T₂ WI, single-voxel ¹ H-MRS, DWI-MR, and DCE-MR.

ASSESSMENT

All MR images were interpreted and agreed upon by two radiologists who were blinded to clinical information and histopathologic results. The imaging diagnoses were then compared to the histopathologic results.

STATISTICAL TESTS

Youden index was used to determine the optimized threshold value. The receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficiency of different functional (f)MRI methods.

RESULTS

Fisher's exact test indicated a significant difference between the ¹ H-MRS images of the two lesions (P < 0.001). The sensitivity, specificity, and accuracy of positive choline (Cho) peak in ¹ H-MRS of parotid MALToma were 80%, 76%, and 77.7%, respectively. The mean apparent diffusion coefficient (ADC) was 0.992 × 10^-3 mm² /s in patients with parotid tumor-like BLEL and 0.634 × 10^-3 mm² /s in patients with parotid MALToma, and the difference was statistically significant (t-test, P < 0.001). Choosing the Youden index as 0.669 × 10^-3 mm² /s, the sensitivity, specificity, and accuracy of the assay were 78.9%, 95.8%, and 88.4%, respectively. Assuming that time-intensity curve (TIC) type I indicated parotid MALToma (positive), and type II and type III indicated parotid tumor-like BLEL (negative), the sensitivity, specificity, and accuracy of time-to-peak (TTP) and initial slope of increase (ISI) in diagnosing MALToma were 94.1%, 95.2%, and 94.7%, respectively. Combining methods of TTP, ADC, and Cho peak reached the highest AUC (1.000).

DATA CONCLUSION

Combined use ¹ H-MRS, DWI-MR, and DCE-MR increased the accuracy of the differential diagnosis between these lesions to 100%. Cho peak in ¹ H-MRS, ADC less than 0.669 × 10^-3 mm² /s, TIC type I together indicated parotid MALToma.

LEVEL OF EVIDENCE

3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:423-432.

The diagnostic value of combining conventional, diffusion-weighted imaging and dynamic contrast-enhanced MRI for salivary gland tumors

OBJECTIVE

To determine the diagnostic value of combining conventional MRI, diffusion-weighted imaging (DWI) and dynamic contrast enhanced MRI (DCE-MRI) in salivary gland tumors.

METHODS

45 patients with salivary gland tumors were evaluated with conventional MRI, DWI and DCE-MRI prior to surgery and confirmed by pathologic findings. The apparent diffusion coefficient (ADC) was calculated from DWI that was obtained with a factor of 0 and 1000 s mm^-2. A time-intensity curve (TIC) was obtained from DCE-MRI.

RESULTS

In conventional MRI, benign tumors often showed well-defined and clear margins, malignant tumors showed irregular margins or infiltration into the surrounding tissue. There were significant differences with regard to the ADC values between pleomorphic adenoma (1.72 ± 0.29 × 10^-3 mm² s^-1) and malignant tumors (0.95 ± 0.09 × 10^-3 mm² s^-1, p < 0.05) and between adenolymphoma (0.74 ± 0.05 × 10^-3 mm² s^-1) and malignant tumors (p < 0.05). However, there was no significant differences in term of the ADC values between benign tumors (1.33 ± 0.52×10^-3 mm² s^-1) and malignant tumors. DCE-MRI showed benign tumors with A-type, B-type and D-type of TICs, and the malignant tumors with C-type TICs. A combination of all of these parameters yielded sensitivity, specificity, accuracy, and positive and negative predictive values of 90%, 97%, 95%, 90 and 97%, respectively.

CONCLUSION

An evaluation combining MRI morphologic findings and functional MRI (ADCs and TIC) appears to be useful in differentiating benign from malignant tumors in salivary gland tumors. Advances in knowledge: The study firstly dealt with the combination of conventional MRI, DWI-MRI with DCE-MRI in salivary gland tumors.

Diagnostic accuracy of magnetic resonance imaging techniques for parotid tumors, a systematic review and meta-analysis

OBJECTIVE

To assess the added benefit of combining different MRI techniques for preoperative diagnosis of parotid tumors when compared to conventional MRI and advanced MRI techniques alone with meta-analysis.

METHODS

A comprehensive PubMed electronic database search was performed for original diagnostic studies up to July 2017. The methodologic quality of each study was evaluated by two independent reviewers who used the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Statistical analysis included pooling of sensitivity and specificity with 95% confidence intervals (CI). All analyses were conducted using STATA (version 12.0), RevMan (version 5.2), and Meta-Disc 1.4 software programs.

RESULTS

Pooled sensitivity and specificity of conventional MRI, diffusion weighted imaging (DWI), dynamic contrast enhanced (DCE) and the above combination were 76% (95%CI)/91% (95%CI)/80% (95%CI)/86% (95%CI) and 83% (95%CI)/56% (95%CI)/90% (95%CI)/90% (95%CI).

CONCLUSION

Conventional MRI combined with DWI and DCE showed higher diagnostic accuracy than conventional or advanced MRI alone, supporting their use in parotid tumors diagnosis.

Cross-sectional Imaging of Parotid Gland Nodules: A Brief Practical Guide

Clinical evaluation and ultrasound examination are the first steps in the evaluation of a patient with a swelling of the parotid region. After the detection of a nodular lesion, cytological or histological confirmation is usually performed to achieve the diagnosis, while the choice of cross-sectional imaging (computed tomography scan and magnetic resonance imaging) may significantly vary from one physician to another, on the basis of the degree of confidence that both radiologist and surgeon have with this kind of imaging. This work focuses on some essential “reporting points” in cross-sectional imaging evaluation of parotid nodules, chiefly helpful to the radiologist when the ultrasonography assessment is considered incomplete and requires a further evaluation.

Clinical utility of dynamic-enhanced MRI in salivary gland tumors: retrospective study and literature review

PURPOSE

To improve the diagnoses of the salivary gland tumors, a dynamic-enhanced MRI (dMRI) was investigated.

METHODS

We conducted a retrospective chart review of 93 cases of salivary gland tumors. The histological diagnoses were obtained from all patients using a surgical specimen and/or an open biopsy specimen. The dMRI as well as fine-needle aspiration cytology (FNAC) and intraoperative frozen section (IFS) were analyzed. This study focused on the time-intensity curve (TIC) after injection, peak time (Tpeak), washout ratio (WR) as well as the gradient of enhancement and washout profile.

RESULTS

The histological diagnoses included pleomorphic adenoma (PMA) in 53 cases, the Warthin tumors (WT) in 14 cases and malignant tumors (MT) in 26 cases. Incorrect diagnosis rate of FNAC and IFS were 5.2 and 8.3%, respectively. The TIC revealed differences among the three types of tumors. Tpeak as well as WR also revealed significant differences (p < 0.001). Tpeak were lower in order of WT, MT, PMA, respectively. WR of TICs at 30, 45 and 105 s after Tpeak were higher in order of WT, MT, PMA, respectively (p < 0.001). The gradient of increment and washout in the TIC curve was also an important parameter to distinguish the three types of tumors. In MT, the rapid enhancement pattern was found in high or intermediate histological grade tumors, whereas the slow enhancement pattern was exhibited in low grade tumors.

CONCLUSIONS

Our findings indicate that using Tpeak and WR, it is possible to distinguish between WT, PMA and MT. Additionally, a rapid enhancement pattern may be a potential marker for these tumors.

Non-enhanced MRI in combination with color Doppler flow imaging for improving diagnostic accuracy of parotid gland lesions

PURPOSE

To determine the value of non-enhanced MRI in combination with color Doppler flow imaging (CDFI) for differentiating malignant parotid tumors from benign ones.

METHODS

This retrospective study analyzed 51 parotid gland lesions (39 benign and 12 malignant) in 51 patients who underwent preoperative CDFI as well as non-enhanced MRI including T₁-weighted, T₂-weighted, and diffusion-weighted imaging (DWI). Degrees of intratumor vascularity were categorized into four grades basing on CDFI findings. The relationships between the lesion and its adjacent external carotid artery and retromandibular vein were inspected on T₁-weighted and T₂-weighted images. Apparent diffusion coefficient (ADC) values were calculated from diffusion-weighted images, and were used to classify the parotid gland lesions with and without reference to the CDFI findings. The classification results were compared using the McNemar test. Sensitivity, specificity, and accuracy percentages were calculated when the non-enhanced MRI/CDFI findings were used to differentiate benign lesions from malignant ones.

RESULTS

The diagnostic accuracy (96.1 vs 82.4%) was significantly improved when ADCs were used together with CDFI findings for classifying parotid gland lesions compared to when ADCs were used alone. Pleomorphic adenomas had the highest ADCs. The ADC thresholds were 1.425 × 10^-3 mm²/s for differentiating pleomorphic adenomas from carcinomas, 0.999 × 10^-3 mm²/s for differentiating pleomorphic adenomas from other benign lesions, and 0.590 × 10^-3 mm²/s for differentiating benign lesions other than pleomorphic adenomas from lymphomas.

CONCLUSION

Combining CDFI with non-enhanced MRI can improve the diagnostic accuracy of MRI for classifying parotid gland lesions.

The value of combining conventional, diffusion-weighted and dynamic contrast-enhanced MR imaging for the diagnosis of parotid gland tumours

OBJECTIVES

The aim of this study was to determine the value of combining conventional MRI, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE)-MRI in diagnosing solid neoplasms in the parotid gland.

METHODS

A total of 148 subjects (101 subjects with benign and 47 subjects with malignant tumours) were evaluated with conventional MRI, DWI and DCE-MRI prior to surgery and pathologic verification. The items observed with conventional MRI included the shape, capsule and signal intensity of parotid masses. The apparent diffusion coefficient (ADC) was calculated from DWI that was obtained with a b-factor of 0 and 1000 s mm^-2. A time-intensity curve (TIC) was obtained from DCE-MRI.

RESULTS

There were significant differences (p < 0.01) in the shape, capsule, ADC and TIC between benign and malignant parotid tumours. Irregular neoplasms without a capsule, ADC <1.12 × 10^-3 mm² s^-1 and a plateau enhancement pattern were valuable parameters for predicting malignant neoplasms. A combination of all of these parameters yielded sensitivity, specificity, accuracy, positive-predictive value and negative-predictive value of 85.1%, 94.1%, 91.2%, 87.0% and 93.1%, respectively.

CONCLUSIONS

A combined analysis using conventional MRI, DWI and DCE-MRI is helpful in distinguishing benign from malignant tumours in the parotid gland.