Diagnosis Of Parotid Gland Tumors Research Articles

MR Image Fusion-Based Parotid Gland Tumor Detection.

The differentiation of benign and malignant parotid gland tumors is of major significance as it directly affects the treatment process. In addition, it is also a vital task in terms of early and accurate diagnosis of parotid gland tumors and the determination of treatment planning accordingly. As in other diseases, the differentiation of tumor types involves several challenging, time-consuming, and laborious processes. In the study, Magnetic Resonance (MR) images of 114 patients with parotid gland tumors are used for training and testing purposes by Image Fusion (IF). After the Apparent Diffusion Coefficient (ADC), Contrast-enhanced T1-w (T1C-w), and T2-w sequences are cropped, IF (ADC, T1C-w), IF (ADC, T2-w), IF (T1C-w, T2-w), and IF (ADC, T1C-w, T2-w) datasets are obtained for different combinations of these sequences using a two-dimensional Discrete Wavelet Transform (DWT)-based fusion technique. For each of these four datasets, ResNet18, GoogLeNet, and DenseNet-201 architectures are trained separately, and thus, 12 models are obtained in the study. A Graphical User Interface (GUI) application that contains the most successful of these trained architectures for each data is also designed to support the users. The designed GUI application not only allows the fusing of different sequence images but also predicts whether the label of the fused image is benign or malignant. The results show that the DenseNet-201 models for IF (ADC, T1C-w), IF (ADC, T2-w), and IF (ADC, T1C-w, T2-w) are better than the others, with accuracies of 95.45%, 95.96%, and 92.93%, respectively. It is also noted in the study that the most successful model for IF (T1C-w, T2-w) is ResNet18, and its accuracy is equal to 94.95%.

The value of diffusion kurtosis imaging and dynamic contrast-enhanced magnetic resonance imaging in the differential diagnosis of parotid gland tumors.

Parotid gland tumors (PGTs) are the most common benign tumors of salivary gland tumors. However, the diagnostic value of relative values of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion kurtosis imaging (DKI) parameters for PGTs has not been extensively studied. Therefore, this study aimed to evaluate the diagnostic performance of combined DKI and DCE-MRI for differentiating PGTs by introducing the concept of relative value. The DCE-MRI and DKI imaging data of 142 patients with PGTs between June 2018 and August 2022 were collected. Patients were divided into four groups by histopathology: malignant tumors (MTs), pleomorphic adenomas (PAs), Warthin tumors (WTs), and basal cell adenomas (BCAs). All MRI examinations were conducted using a 3 T MRI scanner with a 20-channel head and neck coil. Quantitative parameters of DCE-MRI and DKI and their relative values were determined. Kruskal-Wallis H test, post-hoc test with Bonferroni correction, one-way analysis of variance (ANOVA) and post-hoc test with least significant difference (LSD) method, and the receiver operating characteristic (ROC) curve were used for statistical analysis. Statistical significance was set at P<0.05. Only the combination of DKI and DCE-MRI parameters could reliably distinguish BCAs from other PGTs. PAs demonstrated the lowest transfer constant from plasma to extravascular extracellular space (Ktrans) value [0.09 (0.06, 0.20) min-1], relative Ktrans (rKtrans) [-0.24 (-0.64, 1.00)], rate constant from extravascular extracellular space to plasma (Kep) value [0.32 (0.22, 0.53) min-1], relative Kep (rKep) [0.32 (0.22, 0.53) min-1], and initial area under curve (iAUC) value [0.15 (0.09, 0.26) mmol·s/kg] compared with WTs, BCAs, and MTs (all P<0.05). The Ktrans values for MTs were substantially lower [0.17 (0.10, 0.31) min-1] than those for WTs (P=0.01). The Kep values for MTs [0.71 (0.52, 1.28) min-1] were substantially lower (all P<0.05) than those for WTs and BCAs. PAs and BCAs had higher diffusion coefficient (D) values and lower diffusion kurtosis (K) values and relative K (rK) values than MTs and WTs. However, the D and K values did not differ significantly even in their relative values of PAs and BCAs (all P>0.05). By using logistic regression, the combination of K value and rKep value further enhanced their discriminatory power between PAs and WTs [area under the ROC curve (AUC), 0.986], the combination of K and rKep value further enhanced their discriminatory power between PAs and MTs (AUC, 0.915), and the combination of D and Kep value further enhanced their discriminatory power between BCAs and MTs (AUC, 0.909). DKI and DCE-MRI can be used to differentiate PGTs quantitatively and can complement each other. The combined use of DKI and DCE-MRI parameters can improve the diagnostic accuracy of distinguishing PGTs.

Differential diagnosis of parotid gland tumors using apparent diffusion coefficient, texture features, and their combination.

Warthin's tumors (WT) and pleomorphic adenomas (PA) are the commonest parotid gland tumors; however, their differentiation remains difficult. This study aimed to investigate the utility of the apparent diffusion coefficient (ADC) value, texture features, and their combination for the differential diagnosis of parotid gland tumors. Patients who underwent magnetic resonance imaging (MRI) between April 2008 and March 2021 for parotid gland tumors were included and divided into two groups according to the tumor type: WT and PA. The tumor types were used as predictor variables, while the ADC value, texture features, and their combination were the outcome variables. Texture features were measured on short tau inversion recovery (STIR) images and selected using the Fisher's coefficient method and probability of error, and average correlation coefficients. The Mann-Whitney U-test was used to analyze bivariate statistics. Receiver operating characteristic curve analysis was used to assess the ability of the ADC value, texture features, and their combination to distinguishing between the two tumor types. A total of 22 patients were included, 11 in each group. The ADC value, 10 texture features, and their combination were significantly different between the two groups (p < .001). Moreover, all three variables had high area under the curve values of 0.93-0.96. The ADC value, texture features, and their combination demonstrated good diagnostic ability to distinguish between WTs and PAs. This method may be used to aid the differential diagnosis of parotid gland tumors, thereby promoting timely and adequate treatment.

Value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging in differentiating parotid gland tumors

PurposeThis study aimed to explore the value of pre-/post-contrast-enhanced T1 mapping and readout segmentation of long variable echo-train diffusion-weighted imaging (RESOLVE-DWI) for the differential diagnosis of parotid gland tumors. MethodsA total of 128 patients with histopathologically confirmed parotid gland tumors [86 benign tumors (BTs) and 42 malignant tumors (MTs)] were retrospectively recruited. BTs were further divided into pleomorphic adenomas (PAs, n = 57) and Warthin’s tumors (WTs, n = 15). MRI examinations were performed before and after contrast injection to measure the longitudinal relaxation time (T1) value (T1p and T1e, respectively) and the apparent diffusion coefficient (ADC) value of the parotid gland tumors. The reduction in T1 (T1d) values and the percentage of T1 reduction (T1d%) were calculated. ResultsThe T1d and ADC values of the BTs were considerably higher than those of the MTs (all P <.05). The area under the curve (AUC) of the T1d and ADC values for differentiating between BTs and MTs of the parotid was 0.618 and 0.804, respectively (all P <.05). The AUC of the T1p, T1d, T1d%, and ADC values for differentiating between PAs and WTs was 0.926, 0.945, 0.925, and 0.996, respectively (all P >.05). The ADC and T1d% + ADC values performed better in differentiating between PAs and MTs than the T1p, T1d, and T1d% (AUC values: 0.902, 0.909, 0.660, 0.726, and 0.736, respectively). The T1p, T1d, T1d%, and T1d% + T1p values all had high diagnosis efficacy in differentiating WTs from MTs (AUC values: 0.865, 0.890, 0.852, and 0.897, respectively, all P >.05). ConclusionT1 mapping and RESOLVE-DWI can be used to differentiate parotid gland tumors quantitatively and can be complementary to each other.

The Value of Multiparametric Magnetic Resonance Imaging in the Preoperative Differential Diagnosis of Parotid Gland Tumors.

The aim of the present study was to determine the value of multiparametric MRI in the preoperative differential diagnosis of parotid tumors, which is essential for therapeutic strategy selection. A three-year prospective study was conducted with 65 patients. Each patient was investigated preoperatively with multiparametric MRI and surgical excision of the tumor was performed. The preoperative imaging diagnosis was compared with the histopathological report. Several MRI parameters were analyzed, including T1 and T2 weighted image (WI), apparent diffusion coefficient (ADC), time to peak (TTP), and the time intensity curve (TIC). In the differential diagnosis of benign from malignant tumors, T2WI and ADC showed statistically significant differences. Multiparametric MRI had a sensitivity, specificity, and accuracy of 81.8%, 88.6% and 92.3%, respectively. All of the studied parameters (T1, T2, TIC, TTP, ADC) were significantly different in the comparison between pleomorphic adenomas and Warthin tumors. With reference to the scope of this study, the conjunction of multiparametric and conventional MRI demonstrated a sensitivity, specificity, and accuracy of 94.1%, 100%, and 97.8%, respectively. Morphological analysis using conventional MRI combined with diffusion-weighted imaging (DW) and dynamic contrast-enhanced (DCE) multiparametric MRI improved the preoperative differential diagnosis of parotid gland tumors.

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.

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.

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.

Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors.

To create a new artificial intelligence approach based on deep learning (DL) from multiparametric MRI in the differential diagnosis of common parotid tumors. Parotid tumors were classified using the InceptionResNetV2 DL model and majority voting approach with MRI images of 123 patients. The study was conducted in three stages. At stage I, the classification of the control, pleomorphic adenoma, Warthin tumor and malignant tumor (MT) groups was examined, and two approaches in which MRI sequences were given in combined and non-combined forms were established. At stage II, the classification of the benign tumor, MT and control groups was made. At stage III, patients with a tumor in the parotid gland and those with a healthy parotid gland were classified. A stage I, the accuracy value for classification in the non-combined and combined approaches was 86.43% and 92.86%, respectively. This value at stage II and stage III was found respectively as 92.14% and 99.29%. The approach presented in this study classifies parotid tumors automatically and with high accuracy using DL models.

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

Diagnosis of parotid gland tumors is sometimes challenging due to their diversity and pleomorphic histological appearance. B-scan sonography along with color-coded duplex sonography is the gold standard in the diagnostic workup of these lesions, whereas histopathology is to date the gold standard for the final diagnosis. To date no single imaging technique provides the chance for an art-diagnosis with highly diagnostic accuracy. Contrast enhanced ultrasonography (CEUS) on the other hand provides information of the perfusion down to the capillary level. Currently there are only a few papers published with systematical examination of the perfusion in benign parotid gland tumors and its diagnostic significance. One hundred patients with a parotid gland tumor were examined. The examinations included conventional B-scan sonography, color-coded duplexsonography along with contrast enhanced ultrasonography (CEUS). B-scan sonographic parameters, i.e. echogenicity, shape, size, demarcation, and borders of a lesion along with vascularization estimated by color-coded-duplexsonography were analyzed. Analysis of quantitative CEUS parameters was performed using 8 regions of interest (ROI), which were standardized located throughout the entire tumors. The perfusion parameters were analyzed for particular tumor entities. Qualitative CEUS analysis with estimating the perfusion pattern was additionally performed. Histological examination revealed benign tumors in 92 cases, with pleomorphic adenomas and Warthin´s tumors were the most frequent entities. Malignant conditions were found in 8 cases. CEUS revealed a centripetal perfusion pattern in malignant tumors significantly more frequently than in benign tumors. CEUS showed a significant heterogenic perfusion in all tumors, with a higher perfusion in the medial parts of the tumors and in some cases also in the center. Perfusion patterns of PA and WT were different. WT displayed centrifugal, centripetal, and central diffuse perfusion more often than PA, whereas in PA perfusion often was limited to the capsule or periphery. Oncocytoma had the highest perfusion values. Intraglandular cysts showed no intralesional perfusion. CEUS analysis in different parts of benign tumors revealed a significant heterogeneity in tumor perfusion. Some perfusion pattern could be identified which might be characteristic for particular lesions. Based on this, the diagnostic accuracy of CEUS in the differential diagnosis of parotid gland tumors can be increased. In particular, the perfusion analysis within the tumors using ROIs located standardized throughout the entire tumor provides additional information which are important for the art diagnosis and in differentiation of tumor entity.

Radiomics and deep learning approach to the differential diagnosis of parotid gland tumors.

Advances in computer technology and growing expectations from computer-aided systems have led to the evolution of artificial intelligence into subsets, such as deep learning and radiomics, and the use of these systems is revolutionizing modern radiological diagnosis. In this review, artificial intelligence applications developed with radiomics and deep learning methods in the differential diagnosis of parotid gland tumors (PGTs) will be overviewed. The development of artificial intelligence models has opened new scenarios owing to the possibility of assessing features of medical images that usually are not evaluated by physicians. Radiomics and deep learning models come to the forefront in computer-aided diagnosis of medical images, even though their applications in the differential diagnosis of PGTs have been limited because of the scarcity of data sets related to these rare neoplasms. Nevertheless, recent studies have shown that artificial intelligence tools can classify common PGTs with reasonable accuracy. All studies aimed at the differential diagnosis of benign vs. malignant PGTs or the identification of the commonest PGT subtypes were identified, and five studies were found that focused on deep learning-based differential diagnosis of PGTs. Data sets were created in three of these studies with MRI and in two with computed tomography (CT). Additional seven studies were related to radiomics. Of these, four were on MRI-based radiomics, two on CT-based radiomics, and one compared MRI and CT-based radiomics in the same patients.

Differential diagnosis of parotid gland tumours: Application of SWI combined with DWI and DCE-MRI

BackgroundParotid tumours (PTs) have a variety of pathological types, and the surgical procedures differ depending on the tumour type. However, accurate diagnosis of PTs from the current preoperative examinations is unsatisfactory. MethodsThis retrospective study was approved by the Ethics Committee of our hospital, and the requirement for informed consent was waived. A total of 73 patients with PTs, including 55 benign and 18 malignant tumours confirmed by surgical pathology, were enrolled. All patients underwent diffusion-weighted imaging (DWI), dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), susceptibility-weighted imaging (SWI), T2-weighted imaging (T2WI), and T1-weighted imaging (T1WI). The signal uniformity and capsule on T2WI, apparent diffusion coefficient (ADC) derived from DWI, semi-quantitative parameter time-intensity curve (TIC) pattern, and quantitative parameters including transfer constant (Ktrans), extravascular extracellular volume fraction (Ve), wash-out constant (Kep) calculated from DCE-MRI, and intratumoural susceptibility signal (ITSS) obtained from SWI were assessed and compared between benign and malignant PTs. Logistic regression analysis was used to select the predictive parameters for the classification of benign and malignant parotid gland tumours, and receiver operating characteristic (ROC) curve analysis was used to evaluate their diagnostic performance. ResultsMalignant PTs tended to exhibit a type C TIC pattern, whereas benign tumours tended to be type A and B (p < 0.001). Benign PTs had less ITSS than malignant tumours (p < 0.001). Multivariate analyses showed that ADC, Ve, and ITSS were predictors of tumour classification. ROC analysis showed that the area under the curve (AUC) of ADC, Ve, ITSS, and ADC combined with Ve were 0.623, 0.615, 0.826, and 0.782, respectively, in differentiating between malignant and benign PTs. When ITSS was added, the AUCs of ADC, Ve, and ADC combined with Ve increased to 0.882, 0.848, and 0.930, respectively. ConclusionSWI offers incremental diagnostic value to DWI and DCE-MRI in the characterisation of parotid gland tumours.

DIAGNOSTIC ACCURACY OF COLOR ANDPULSED DOPPLER IN THE EVALUATION OF PAROTID GLAND TUMORS

Background: The parotid glands are the largest salivary glands in humans and are frequently involved in various primary &amp; systemic disease processes. The most common benign parotid gland tumor is pleomorphic adenoma. Around 20% of parotid tumors are malignant with most common parotid tumors being mucoepidermoid carcinoma and adenoid cystic carcinoma.&#x0D; Objective:&#x0D; To determine the diagnostic accuracy of pulsed and color Doppler for the evaluation of parotid gland cancer. &#x0D; Methods:&#x0D; It was a cross sectional study by design carried out in Department of Radiology and Department of Maxillofacial Surgery, Mayo Hospital – Lahore in a duration of three year. All the patients were examined with mind-ray 7.5 MHz linear transducer.&#x0D; There are three methods used collectively; Grey Scale, color Doppler and Pulsed Doppler in sonographic examination. Color Doppler and pulsed Doppler demonstrate number of blood vessels within the tumor, their Peak Systolic Velocity and Resistive Index respectively.&#x0D; Histo-pathological results were correlated post operatively with the data obtained from Doppler ultrasound. Data was analyzed using SPSS 23.0 &#x0D; Results:&#x0D; In this study, 192 patients were included .The mean age was 39.42 ± 11.93 years. 58.85% (112) were male patients and 41.15 %( 80) patients were females. The solid masses with regular margins were evaluated and the diagnostic accuracy of pulsed Doppler was found to be 87.5% taking histopathology as gold standard. &#x0D; Conclusion:&#x0D; This study shows pulsed Doppler to be more accurate tool for diagnosis of parotid gland tumor as compared to color Doppler taking histopathology as gold standard.

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland was evaluated. Seventy-eight patients participated in this study and underwent magnetic resonance diffusion-weighted imaging. Three regions of interest, including the parotid gland tumor, the peritumor parotid gland, and the contralateral parotid gland, were manually contoured for 92 tumors, including 20 malignant tumors (MTs), 42 Warthin tumors (WTs), and 30 pleomorphic adenomas (PMAs). We recorded multiple apparent diffusion coefficient (ADC) features and applied a machine-learning method with the features to classify the three types of tumors. With only mean ADC of tumors, the area under the curve of the classification model was 0.63, 0.85, and 0.87 for MTs, WTs, and PMAs, respectively. The performance metrics were improved to 0.81, 0.89, and 0.92, respectively, with multiple features. Apart from the ADC features of parotid gland tumor, the features of the peritumor and contralateral parotid glands proved advantageous for tumor classification. Combining machine learning and multiple features provides excellent discrimination of tumor types and can be a practical tool in the clinical diagnosis of parotid gland tumors.

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.

Actinomycosis of the parotid gland. Challenges in diagnosis and treatment – case report

Introduction: Primary actinomycosis of the parotid gland is a rare infectious disease which can mimic a malignant process both clinically and diagnostically. Case report: We present a case report of actinomycosis of the parotid gland manifesting as a slowly growing tumor in the left neck region. Methods: Computer tomography, histopathologic examination Results: Total sialoadenectomy. Confirmed diagnosis of actinomycosis. Patient introduced to an infectious disease specialist. Conclusion: Although this location of actinomycosis is rare, the disease must be considered in the differential diagnosis of parotid gland tumors.

Perfusion analysis in parotid gland tumors using contrast-enhanced ultrasound (CEUS)

The diagnosis of parotid gland tumors is challenging due to their rarity and heterogenity. Neither conventional ultrasound nor magnetic resonance imaging (MRI) nor computed tomography (CT) allow areliable pretherapeutic diagnosis. In addition to conventional ultrasound, contrast-enhanced ultrasound (CEUS) enables amore detailed assessment of perfusion in parotid gland tumors, thereby improving evaluation of this tumor entity. Extensive studies with analysis of perfusion characteristics in different regions of interest (ROI) in parotid gland tumors are currently lacking. This study analysed and compared perfusion parameters in different intratumoral areas of malignant and benign parotid gland tumors using CEUS. A total of 100patients with tumors in the parotid gland were examined using B‑mode sonography, colour Doppler sonography and CEUS. The parameters magnitude, echogenicity, demarcation, vascularisation and in particular perfusion characteristics were measured and analysed. Analysis of quantitative CEUS parameters was performed using aspecific method for perfusion analysis with certain ROI, which were allocated in a standardized manner in the entire parotid gland tumors. The perfusion parameters were compared between intratumoral ROI in the tumors and between particular tumor entities. Qualitative CEUS analysis with an estimation of perfusion patterns was additionally performed. Histologically benign tumors were found in 92cases, and malignant tumors in eight cases. CEUS analysis of perfusion patterns revealed acentripetal perfusion pattern in malignant tumors significantly more frequently than in benign tumors. In the perfusion analysis of quantitative CEUS parameters, all tumors showed higher perfusion intensities in the peripheral ROI. In benign tumors, more differences in perfusion intensity between the intratumoral ROIs were detected compared to malignant tumors. The perfusion parameters (centripetal perfusion pattern; area under the curve) evaluated in this study have the potential to improve pretherapeutic diagnostics of parotid gland tumors in terms of differentiation of tumor entity. Further studies with larger patient cohorts are required for subsequent investigation and validation of the diagnostic accuracy of particular parameters to detect perfusion patterns potentially specific to particular tumor entities.

Surgeon-performed ultrasound with fine-needle aspiration biopsy for the diagnosis of parotid gland tumors.

Ultrasound (US) with or without fine-needle aspiration cytology (FNAC) are readily available and can offer in office and rapid diagnosis of parotid lumps. We analyzed 398 of patients who underwent blind FNAC or US with FNAC performed by an operating head and neck surgeon. Specificity, sensitivity, as well as accuracy were calculated for US alone, FNAC alone, and US-FNAC combined. Nondiagnostic US-guided FNAC rate was 3.1% and 7.2% for blind FNAC. With those cases removed, final accuracy for US alone in diagnosis of malignancy, pleomorphic adenoma (PA), and Warthin tumor (WT) were 90.8%, 81%, and 77.4% respectively. For US-FNAC accuracy for malignancy, PA, and WT were 92.1%, 94 0.8%, and 95.4% respectively. US with FNAC is cost-effective and rapid diagnostic tool and may help surgeon to deliver more accurate informed consent to a patient.

The usefulness of fine needle aspiration cytology in the diagnosis of parotid gland tumors

Background: Various parotid gland diseases are seen clinically including inflammation, sialolithiasis, benign and malignant tumors. It is important to differentiate between these to make a correct diagnosis, the aim of this study is to assess the usefulness of fine needle aspiration cytology as a preoperative diagnostic tool in the diagnosis of parotid gland tumors. Method: A retrospective study was done on 110 Iraqi patients with apparent parotid gland swelling, all patients were subjected to FNAC by using 22-gauge needle attached to a 10ml syringe with at least two needle passes were made for each patient. Results: The number of cases in each category were: nondiagnostic 5.5% and in 94.5% were diagnostic. Neoplastic lesions were observed in 71.20% of patients, non-neoplastic lesions in 28.80% of patient. Conclusion: Fine needle aspiration cytology is a reliable examination providing important information to the surgeon in the preoperative diagnostic assessment when Milan system was applied.

Research on the Classification of Benign and Malignant Parotid Tumors Based on Transfer Learning and a Convolutional Neural Network

The classification of benign and malignant parotid tumors is very crucial for the selection of surgical methods and their prognoses. The wide application of deep learning technology in the field of medical imaging also provides new ideas for the computer-aided diagnosis of parotid gland tumors. In addition, because the pathological types of parotid gland tumors are very complicated and the computed tomography (CT) images of benign and malignant patients are also very similar, some clinicians may misjudge tumors due to a lack of experience, which affects the effect of surgical treatment and prognosis. Therefore, this research proposes using deep learning methods to solve this problem. This study uses the four classic pretraining models of VGG16, InceptionV3, ResNet and DenseNet to classify parotid CT images using transfer learning methods and uses an improved convolutional neural network (CNN) model to classify parotid CT images. The experimental results show that the improved CNN model achieves an accuracy of 97.78%, and its classification performance is better than those of the other four transfer learning methods. It can effectively diagnose benign and malignant parotid tumors and improve the diagnostic accuracy.