Malignant Parotid Gland Tumors Research Articles

Differentiation of benign and malignant parotid gland tumors based on the fusion of radiomics and deep learning features on ultrasound images.

The pathological classification and imaging manifestation of parotid gland tumors are complex, while accurate preoperative identification plays a crucial role in clinical management and prognosis assessment. This study aims to construct and compare the performance of clinical models, traditional radiomics models, deep learning (DL) models, and deep learning radiomics (DLR) models based on ultrasound (US) images in differentiating between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Retrospective analysis was conducted on 526 patients with confirmed PGTs after surgery, who were randomly divided into a training set and a testing set in the ratio of 7:3. Traditional radiomics and three DL models (DenseNet121, VGG19, ResNet50) were employed to extract handcrafted radiomics (HCR) features and DL features followed by feature fusion. Seven machine learning classifiers including logistic regression (LR), support vector machine (SVM), RandomForest, ExtraTrees, XGBoost, LightGBM and multi-layer perceptron (MLP) were combined to construct predictive models. The most optimal model was integrated with clinical and US features to develop a nomogram. Receiver operating characteristic (ROC) curve was employed for assessing performance of various models while the clinical utility was assessed by decision curve analysis (DCA). The DLR model based on ExtraTrees demonstrated superior performance with AUC values of 0.943 (95% CI: 0.918-0.969) and 0.916 (95% CI: 0.861-0.971) for the training and testing set, respectively. The combined model DLR nomogram (DLRN) further enhanced the performance, resulting in AUC values of 0.960 (95% CI: 0.940- 0.979) and 0.934 (95% CI: 0.876-0.991) for the training and testing sets, respectively. DCA analysis indicated that DLRN provided greater clinical benefits compared to other models. DLRN based on US images shows exceptional performance in distinguishing BPGTs and MPGTs, providing more reliable information for personalized diagnosis and treatment plans in clinical practice.

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

ObjectiveTo compare clinicopathological and imaging and features of micro- and minitumors of the parotid gland and provide a reference for preoperative prediction of benign vs. malignant status. Study DesignPatients with parotid gland tumors treated surgically were selected. Relevant clinicopathological and imaging data were collected for patients with maximum tumor diameters ≤ 20 mm on preoperative computed tomography (CT). The lesions were divided into two groups, microtumors and minitumors, based on maximum tumor diameter. CT imaging features of benign and malignant tumors were compared through binary logistic regression analysis. ResultsMicrotumors and minitumors were categorized by maximum diameters < 10 mm (n = 74) and 10-20 mm (n = 611), respectively. Benign and malignant minitumors exhibited significant differences in boundary, tumor density, margin morphology, spiculation margin, and CT values in the plain and arterial phase (P < 0.05), resembling those found in typical malignant parotid gland tumors. However, no significant differences were observed between benign and malignant microtumors. Logistic regression analysis identified boundary, margin morphology, and spiculation margin as independent predictors of malignancy. The prediction model excelled in identifying benign lesions but was less successful in identifying malignancies. ConclusionParotid gland minitumors had imaging features similar to typical larger malignant tumors. Active exclusion of the malignant risk and early surgical treatment is recommended for these tumors. Statement of Clinical RelevanceThis study analyzed the clinicopathological and imaging features of micro- and minitumors of the parotid gland to provide a reference for preoperative prediction and treatment. Active exclusion of malignant risk and early surgical treatment is recommended for small parotid tumors.

A retrospective analysis of 350 cases of parotid gland tumors and the value of ultrasound in the diagnosis of benign and malignant parotid gland tumors

AbstractObjectiveTo retrospective analysis of the epidemiological and clinical characteristics of parotid gland tumors, and to explore the value of ultrasound in the differentiation of benign and malignant parotid gland tumors.MethodsThe clinical data of 350 patients with parotid gland tumors in our hospital from 2002 to 2022 were retrospectively analyzed. These patients were examined by ultrasound. The postoperative pathological test results were used as the “gold standard” to verify and analyze the value of color Doppler ultrasound in the differential diagnosis of benign and malignant parotid tumors (PT).ResultsThe male‐to‐female ratio of 350 patients with parotid gland tumors was 1.16:1. The age of patients ranged from 12 to 87 years old. Patients aged 40–65 years accounted for 54.6%, which was the most common age of parotid gland tumors. Among 350 cases, 259 were benign and 91 were malignant. The top three benign PTs were pleomorphic adenoma, Warthin tumor, and basal cell adenoma. The top three malignant tumors were mucoepidermoid carcinoma, acinar cell carcinoma, and adenoid cystic carcinoma. The specificity, sensitivity, accuracy, positive predictive value, and negative predictive value of ultrasonography in the diagnosis of benign and malignant parotid masses were 96.53%, 69.23%, 89.43%, 87.50%, and 87.72%, respectively.ConclusionThe pathological types of parotid gland tumors are complex and diverse. Ultrasonography has high accuracy in the diagnosis of PT, which can provide important evidence for the differentiation of benign and malignant PT, and then assist in the clinical selection of individualized surgical programs.

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

PurposeTo investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors. MethodFrom December 2018 to January 2023, patients who underwent diffusion weighted imaging (DWI), diffusion kurtosis imaging (DKI), and intravoxel incoherent motion (IVIM) were consecutively enrolled in this retrospective study. The histogram features of diffusion parameters, including apparent diffusion coefficient (ADC), diffusion coefficient (Dk), diffusion kurtosis (K), pure diffusion coefficient (D), pseudo-diffusion coefficient (DP), and perfusion fraction (FP) were analyzed. The Mann-Whitney U test was used for comparison between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs). Receiver operating characteristic curve and logistic regression analysis were used to identify the differential diagnostic performance. The Spearman’s correlation coefficient was used to analyze the correlation between diffusion parameters and Ki-67 labeling index. ResultsFor diffusion MRI, twenty-three histogram features of diffusion parameters showed significant differences between BPGTs and MPGTs (all P < 0.05). Compared with the DWI model, the IVIM model and combined model had better diagnostic specificity (58 %, 94 %, and 88 %, respectively; both corrected P < 0.001) and accuracy (64 %, 89 %, and 86 %, respectively; both corrected P = 0.006). The combined model was superior to the single DWI model with improved IDI (IDI improvement 0.25). Significant correlations were found between Ki-67 and ADCmean, Dkmean, Kmean, and Dmean (r = -0.57 to 0.53; all P < 0.05). ConclusionsWhole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Comparative study of benign and malignant parotid gland tumors by infrared thermal imaging

Objective: To analyze the temperature difference of benign and malignant parotid gland tumors in preoperative infrared thermography (IRT), and to provide the basis for predicting tumor properties. Methods: The clinical data of 98 patients with parotid gland tumor admitted to the Department of Oral and maxillofacial Surgery of the First Affiliated Hospital of Bengbu Medical College, from May 2021 to April 2023 were retrospectively analyzed. There were 61 males and 37 females, aged (51.1±16.0) years (10-86 years). In addition to routine examination, the temperature difference between the lesion site of parotid gland and the contralateral mirror area was measured by infrared thermal imager in all patients one day before surgery. The maximum diameter (dmax) and location of the tumor (deep or superficial lobe) were recorded according to preoperative clinical examination and imaging examinations such as CT and ultrasound. The patients were divided into three groups by tumor size: dmax≤2 cm, 2 cm<dmax≤4 cm, dmax>4 cm. The patients were also divided into different groups: deep lobe group and superficial lobe group (according to the tumor location), benign group and malignant group (according to postoperative pathological results). The relationship between temperature difference, pathology, size and location was analyzed. Results: There were 79 cases in the benign group and 19 cases in the malignant group. The temperature difference of the healthy and affected side in the malignant group [(1.73±0.21) ℃] was significantly higher than that in the benign group [(0.73±0.32) ℃] (t=16.70, P<0.001). There was no significant difference in temperature difference between the healthy and affected sides of tumors with different diameters (P>0.05). The temperature difference of healthy and affected side of tumor in superficial lobe [(0.97±0.50) ℃] was significantly higher than that in deep lobe [(0.67±0.44) ℃] (t=2.24, P=0.028). Conclusions: The difference of temperature difference between benign and malignant parotid gland tumors detected by IRT is statistically significant, which can be used to predict tumor properties, and has certain clinical application value.

Construction and Application of a Nomogram for Predicting Benign and Malignant Parotid Tumors.

A prediction model of benign and malignant differentiation was established by magnetic resonance signs of parotid gland tumors to provide an important basis for the preoperative diagnosis and treatment of parotid gland tumor patients. The data from 138 patients (modeling group) who were diagnosed based on a pathologic evaluation in the Department of Stomatology of Jilin University from June 2019 to August 2021 were retrospectively analyzed. The independent factors influencing benign and malignant differentiation of parotid tumors were selected by logistic regression analysis, and a mathematical prediction model for benign and malignant tumors was established. The data from 35 patients (validation group) who were diagnosed based on pathologic evaluation from September 2021 to February 2022 were collected for verification. Univariate and multivariate logistic regression analysis showed that tumor morphology, tumor boundary, tumor signal, and tumor apparent diffusion coefficient (ADC) were independent risk factors for predicting benign and malignant parotid gland tumors ( P < 0.05). Based on multivariate logistic regression analysis of the modeling group, a mathematical prediction model was established as follows: Y = the ex/(1 + ex) and X = 0.385 + (1.416 × tumor morphology) + (1.473 × tumor border) + (1.306 × tumor signal) + (2.312 × tumor ADC value). The results showed that the area under the receiver operating characteristic curve of the model was 0.832 (95% confidence interval, 0.75-0.91), the sensitivity was 82.6%, and the specificity was 70.65%. The validity of the model was verified using validation group data, for which the sensitivity was 85.71%, the specificity was 96.4%, and the correct rate was 94.3%. The results showed that the area under receiver operating characteristic curve was 0.936 (95% confidence interval, 0.83-0.98). Combined with tumor morphology, tumor ADC, tumor boundary, and tumor signal, the established prediction model provides an important reference for preoperative diagnosis of benign and malignant parotid gland tumors.

The Role of an MRI-Based Radiomic Signature in Predicting Malignancy of Parotid Gland Tumors.

The aim of this study was to assess the ability of MRI radiomic features to differentiate between benign parotid gland tumors (BPGT) and malignant parotid gland tumors (MPGT). This retrospective study included 93 patients who underwent MRI examinations of the head and neck region (78 patients presenting unique PGT, while 15 patients presented double PGT). A total of 108 PGT with histological confirmation were eligible for the radiomic analysis and were assigned to a training group (n = 83; 58 BPGT; 25 MPGT) and a testing group (n = 25; 16 BPGT; 9 MPGT). The radiomic features were extracted from 3D segmentations of the PGT on the T2-weighted and fat-saturated, contrast-enhanced T1-weighted images. Following feature reduction techniques, including LASSO regression analysis, a radiomic signature (RS) was built with five radiomic features. The RS presented a good diagnostic performance in differentiating between PGT, achieving an area under the curve (AUC) of 0.852 (p < 0.001) in the training set and 0.786 (p = 0.017) in the testing set. In both datasets, the RS proved to have lower values in the BPGT group as compared to MPGT group (p < 0.001 and p = 0.023, respectively). The multivariate analysis revealed that RS was independently associated with PGT malignancy, together with the ill-defined margin pattern (p = 0.031, p = 0.001, respectively). The complex model, using clinical data, MRI features and the RS, presented a higher diagnostic performance (AUC of 0.976) in comparison to the RS alone. MRI-based radiomic features could be considered potential additional imaging biomarkers able to discriminate between benign and malignant parotid gland tumors.

A deep learning model for the differential diagnosis of benign and malignant salivary gland tumors based on ultrasound imaging and clinical data.

The preoperative differentiation between benign parotid gland tumors (BPGTs) and malignant parotid gland tumors (MPGTs) is of great significance for therapeutic decision-making. Deep learning (DL), an artificial intelligence algorithm based on neural networks, can help overcome inconsistencies in conventional ultrasonic (CUS) examination outcomes. Therefore, as an auxiliary diagnostic tool, DL can support accurate diagnosis using massive ultrasonic (US) images. This current study developed and validated a DL-based US diagnosis for the preoperative differentiation of BPGT from MPGT. A total of 266 patients, including 178 patients with BPGT and 88 patients with MPGT, were consecutively identified from a pathology database and enrolled in this study. Ultimately, considering the limitations of the DL model, 173 patients were selected from the 266 patients and divided into 2 groups: a training set, and a testing set. US images of the 173 patients were used to construct the training set (including 66 benign and 66 malignant PGTs) and testing set (consisting of 21 benign and 20 malignant PGTs). These were then preprocessed by normalizing the grayscale of each image and reducing noise. Processed images were imported into the DL model, which was then trained to predict the images from the testing set and evaluated for performance. Based on the training and validation datasets, the diagnostic performance of the 3 models was assessed and verified using receiver operating characteristic (ROC) curves. Ultimately, before and after combining the clinical data, we compared the area under the curve (AUC) and diagnostic accuracy of the DL model with the opinions of trained radiologists to evaluate the application value of the DL model in US diagnosis. The DL model showed a significantly higher AUC value compared to doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data (AUC =0.9583 vs. 0.6250, 0.7250, and 0.8025 respectively; all P<0.05). In addition, the sensitivity of the DL model was higher than the sensitivities of the doctors combined with clinical data (97.2% vs. 65%, 80%, and 90% for doctor 1 + clinical data, doctor 2 + clinical data, and doctor 3 + clinical data, respectively; all P<0.05). The DL-based US imaging diagnostic model has excellent performance in differentiating BPGT from MPGT, supporting its value as a diagnostic tool for the clinical decision-making process.

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

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. 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. 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. 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.

Evaluating Online Patient Health Information on Parotidectomy - Is it Readable and of Good Quality?

Parotidectomy is considered the definitive management for benign and malignant parotid gland tumours. Complications and long-term outcomes of parotidectomy can pose a massive impact on patients’ quality of life. Although there are potential benefits of obtaining health information from the internet, it has its limitations on quality and accuracy of information. This research evaluates the readability and quality of online patient health information(PHI) on parotidectomy.

Deep learning-assisted diagnosis of parotid gland tumors by using contrast-enhanced CT imaging.

Imaging interpretation of the benignancy or malignancy of parotid gland tumors (PGTs) is a critical consideration prior to surgery in view of therapeutic and prognostic values of such discrimination. This study investigates the application of a deep learning-based method for preoperative stratification of PGTs. Using the 3D DenseNet-121 architecture and a dataset consisting of 117 volumetric arterial-phase contrast-enhanced CT scans, we developed a binary classifier for PGT distinction and tested it. We compared the discriminative performance of the model on the test set to that of 12 junior and 12 senior head and neck clinicians. Besides, potential clinical utility of the model was evaluated by measuring changes in unassisted and model-assisted performance of junior clinicians. The model finally reached the sensitivity, specificity, PPV, NPV, F1-score of 0.955 (95% CI 0.751-0.998), 0.667 (95% CI 0.241-0.940), 0.913 (95% CI 0.705-0.985), 0.800 (95% CI 0.299-0.989) and 0.933, respectively, comparable to that of practicing clinicians. Furthermore, there were statistically significant increases in junior clinicians' specificity, PPV, NPV and F1-score in differentiating benign from malignant PGTs when unassisted and model-assisted performance of junior clinicians were compared. Our results provide evidence that deep learning-based method may offer assistance for PGT's binary distinction.

Low intensity whole-body oxidative stress in patients with parotid gland tumors.

Recently, there has been a significant increase in the number of patients hospitalized and undergoing surgery due to parotid gland tumors. We have observed an increase in the total number of salivary gland tumors, and hence an increased number of malignant tumors. Etiological factors of salivary gland tumors are still unknown. Oxidative stress (OS) has been suggested to be an important indicator of carcinogenesis in some tumors. Therefore, the aim of our study was to assess the state of OS in patients with selected benign and malignant parotid gland tumors. After final postoperative histological diagnosis of the tumor, patients were divided into the following groups i.e. pleomorphic adenoma 35 patients, Warthins tumor (WT) - 17 patients, mucoepidermoid carcinoma - 9 patients and 45 controls. The final study group included 106 patients. We found an increase in plasma lipofuscin levels in patients with all investigated parotid gland tumors. Furthermore, the activity of Cu/Zn-SOD in Warthins tumor patients was statistically lower compared to the controls, pleomorphic adenoma or mucoepidermoid carcinoma.In conclusion, we sustain the hypothesis on the presence of a low intensity whole-body OS in patients with parotid gland tumors. Furthermore, LPS may be a useful marker of OS in patients with benign and malignant tumors. Moreover, pathological mechanisms connected with WT seem to be different compared to malignant neoplasms and other benign tumors. It seems that Cu/Zn-SOD, as an antioxidative enzyme, may play an important role in those mechanisms taking part in WT.

Contrast-Enhanced Ultrasound in the Differentiation between the Most Common Benign Parotid Gland Tumors: A Systematic Review and Meta-Analysis.

Recently, contrast-enhanced ultrasound (CEUS) has become a promising tool in distinguishing benign from malignant parotid gland tumors. However, its usefulness in differentiating various benign parotid tumors has not been determined so far. This study aimed to systematically review the literature to determine the utility of CEUS in the preoperative differentiation between pleomorphic adenomas (PAs) and Warthin's tumors (WTs) of the parotid gland. PubMed, Embase, and Cochrane were searched for English-language articles published until 21 July 2022. Fifteen studies were included. On CEUS examination, a significantly greater percentage of PAs displayed heterogeneous enhancement texture compared to WTs. Contrarily, the enhanced lesion size, the enhancement margin, and the presence of the enhancement rim did not differ significantly between the entities. Significantly longer normalized mean transit time (nMTT) and time to peak (TTP) were observed in PAs. Contrarily, the mean values of area under the curve (AUC) and time from peak to one half (TPH) were significantly higher for WTs. Due to the considerable overlap among the qualitative CEUS characteristics of PAs and WTs, the reproducible, investigator-independent quantitative CEUS measurements have a greater potential to distinguish PAs from WTs, which might influence the selection of an appropriate management strategy.

Comparative analysis of benign and malignant parotid gland tumors: A retrospective study of 992 patients

ObjectiveWe analyzed and compared the clinical characteristics of benign and malignant parotid gland tumors. Patients and methodsA total of 992 patients who underwent surgical treatment for parotid gland tumors from January 2010 to December 2020 were included in this study. This study population was subdivided into benign (n = 812, 81.9 %) and malignant parotid gland tumors (n = 180, 18.1 %). ResultsPleomorphic adenoma is the most common benign tumor and mucoepidermoid carcinoma is the most common malignant tumor. The patients with malignant parotid gland tumors were older than the patients with benign lesions. The duration of symptoms was longer in patients with benign parotid gland tumors compared to those with malignant lesions. The size of the malignant tumors was larger than that of the benign lesions. Preoperative fine-needle aspiration cytology had a diagnostic sensitivity of 50.3 %, diagnostic specificity of 98.7 %, a positive predictive value of 89.5 %, a negative predictive value of 89.9 %, and accuracy of 89.9 % for diagnosing malignant parotid gland tumors. For benign parotid gland tumors, superficial parotidectomy was most frequently performed, and for malignant parotid gland tumors, total parotidectomy was most frequently performed. Facial palsy was observed in 19.4 % of the patients with malignant parotid gland tumors compared to 5.4 % of those with benign tumors. ConclusionThe clinical features of benign and malignant parotid gland tumors showed differences in age, symptoms, duration of symptoms, size and site of the parotid tumors, surgical procedures, and postoperative facial nerve palsy.

Clinico-Epidemiological Analysis of Most Prevalent Parotid Gland Carcinomas in Poland over a 20-Year Period

(1) Background: Malignant tumours of the salivary glands have different clinical and histopathological characteristics. They most commonly involve the parotid gland. Histopathologically, the most common are mucoepidermoid carcinoma (MEC), adenoid cystic carcinoma (AdCC), acinic cell carcinoma (AcCC), adenocarcinoma, carcinoma in pleomorphic adenoma (CPA), and squamous cell carcinoma (SCC). (2) Methods: We analysed 2318 patients with malignant parotid gland tumours reported to the National Cancer Registry (NCR) in Poland over 20 years (1999–2018). The demographic characteristics of patients, clinical factors, and overall survival (OS) were analysed. (3) Results: The average age was 61.33 ± 16.1 years. The majority were males (55%) and urban citizens (64%). High percentage of carcinomas was diagnosed in locoregional (33.7%) and systemic (10.4%) stadium. The most prevalent diagnoses were SCC (33.3%) and adenocarcinoma (19.6%). Surgical resection with adjuvant RT (42.1%) was the most common treatment. The OS analysis showed a median survival time of 5.6 years. The most favorable median OS was found in patients with AcCC (18.30 years), the worst for SCC (1.58 years). (4) Conclusion: AcCC has the best prognosis and SCC the worst. Tumour stadium, treatment, and demographic factors affect prognosis. Improvements in diagnosis and re-evaluation of treatment standards are necessary to enhance the outcome of patients with parotid gland cancers in Poland.

Diffusion-weighted and gadolinium-enhanced dynamic MRI in parotid gland tumors.

To evaluate the value of diffusion-weighted imaging and dynamic contrast-enhanced MRI for the diagnosis of parotid gland tumors. Retrospective review of patients with surgically treated parotid tumors between January 2009 and June 2020, who underwent a preoperative parotid gland MRI including standard morphological sequences, diffusion-weighted echoplanar imaging with apparent diffusion coefficient measurement and T1-weighted gadolinium-enhanced dynamic MRI sequences with Fat Saturation. The lesion was classified between malignant vs benign and precisions regarding its histological type were given when possible. Imaging findings were compared with pathology results. Inclusion of 133 patients (mean age: 53years). Multiparametric MRI had a sensitivity of 90.3%, a specificity of 77.5%, an overall accuracy of 80.5%, a positive predictive value of 54.9% and a negative predictive value of 96.3% to differentiate benign parotid tumor from malignant ones. Specificity (85.5%) and positive predictive value (67.6%) were improved for cases, where anatomical and functional MRI characteristics were conclusive and consistent with clinical findings. Combining diffusion-weighted and gadolinium-enhanced dynamic sequences, in addition to morphological ones enables high (> 90%) sensitivity to detect malignant parotid gland tumors. It also gives the possibility to characterize pleomorphic adenomas and Warthin tumors and to avoid fine-needle aspiration in cases of typical imaging presentation and reassuring clinical findings.

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.

ObjectivesWe 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 MethodsThis 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.ResultsThe 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).ConclusionsRadiomics nomogram yielded excellent diagnostic performance in differentiating BPGT from MPGT, PA from MPGT, and PA from WT.

Single incision-plus approach for gasless endoscopic parotidectomy: a seven-step procedure.

Endoscopic parotidectomy has the potential to become a reliable procedure for benign and low-grade malignant parotid gland tumors. Based on the previous literature review and our own clinical experience, we introduced in detail the surgical procedure of single incision-plus approach for gasless endoscopic parotidectomy. This method contributes a logical approach to achieving endoscopic resection of parotid gland tumor and preservation of facial nerve, which can be summarized into the following seven-step method: preoperative preparation; design of retroauricular-hairline incision and plus-incision; surgical cavities creation and coalescence; separation of surgical boundaries; separation and protection of the facial nerve trunk; processing of the branches of facial nerve; en bloc resection of the superficial parotid gland and tumor. Endoscopic parotidectomy is a more difficult procedure than conventional parotid surgery, requiring more precision as well as more experience and equipment. The learning curve of time and frequency is influenced by many factors, like anatomy, instruments, procedures and patience. We contribute our clinical exploration of anatomical precautions, feasible instruments, and surgical procedures and summarize precautions under single incision-plus in gasless endoscopic parotidectomy. Given the growing interest in the aesthetic process of the parotid region, the seven-step method may have the potential to be a method for teaching gasless endoscopic parotidectomy.

Diagnosis of parotid gland tumours with Contrast-Enhanced Ultrasound: a systematic review and meta-analysis.

Contrast-enhanced ultrasound (CEUS) appears to be a promising application for the diagnosis of parotid glandtumours. We aimed to systematically review and meta-analyse the ability of CEUS in distinguishing benign from malignantparotid gland tumours. PubMed was searched for relevant studies. Data on area under time intensitycurve (AUC) in arbitrary unit (AU), and mean transit time (MTT) in seconds (sec) were analysed using the Cochrane ReviewManager Software. Nine studies met the eligibility criteria comprising a total number of 498 parotid gland tumours(benign, number (n)=423; malignant, n=75). Descriptive evaluation of parotid gland tumours following CEUS administrationshowed overlap characteristics in benign and malignancies. Two publications assessed AUC and MTT in 72 and 60 parotidgland tumours, respectively. AUC was significantly lower in benign compared to malignant tumours following contrast administration(AUC, mean difference (MD) -266.77 AU, 95% confidence intervals (CI) -433.22, -100.33, p=0.002). No significantdifferent in MTT between benign and malignant tumours (p=0.12). Heterogeneity was statistically significant in AUC (p=0.04)and MTT (p<0.00001). Descriptive evaluation of parotid gland tumours showed overlap CEUS characteristics.Perfusion related CEUS parameters analysis is promising in differentiating benign parotid tumours from malignancies.