OBJECTIVE: The aim of this retrospective article is to evaluate postoperative outcomes after extracapsular dissection for small benign superficial parotid neoplasms (<3 cm) in patients who received Superficial Musculoaponeurotic System (SMAS) flap and in patients who did not receive it.
METHODS: Two groups were created and statistically compared regarding Frey\'s syndrome and aesthetic satisfaction by data collected through the POI-8 validated questionnaire and through an aesthetic satisfaction scale ranging from 1 to 10. The difference between these two groups was the utilization of SMAS flap. SMAS flap was harvested in one of these two group, meanwhile was not used in the other.
RESULTS: The p-value analysis between group 1 and group 2 on these complications, resulted statistically not significant. Also, the aesthetic satisfaction resulted not statistically significant between group 1 and group 2. Gender, localization, and facial palsy resulted statistically correlated with the aesthetic satisfaction (p-value < 0.05).
CONCLUSIONS: In conclusion, there is no statistical difference in the use of SMAS flap for benign parotid neoformations of the superficial lobe, with a diameter of less than 3 cm for which extracapsular dissection is adopted as a surgical technique.
METHODS:

Intraparotid gland neurofibroma is a rare benign tumor that arises from Schwann cells of the facial nerve within the parotid gland. This case report discusses a 41-year-old woman who experienced a painless preauricular swelling on her right side for over 5 years. Clinical examination and ultrasound revealed a well-defined mass in the parotid gland. The patient underwent total mass excision, resulting in transient facial nerve dysfunction but complete recovery. These tumors often manifest as solitary masses in the parotid region and may compress nearby structures, causing facial paralysis or numbness. Their diagnosis can be challenging due to similarities with other parotid gland tumors and possible associations with neurofibromatosis. Managing intraparotid tumors, including neurofibromas, involves a multidisciplinary approach with input from cytopathologists, radiologists, and surgeons.

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OBJECTIVE: to develop a deep learning radiomics graph network (DLRN) that integrates deep learning features extracted from gray scale ultrasonography, radiomics features and clinical features, for distinguishing parotid pleomorphic adenoma (PA) from adenolymphoma (AL) MATERIALS AND METHODS: A total of 287 patients (162 in training cohort, 70 in internal validation cohort and 55 in external validation cohort) from two centers with histologically confirmed PA or AL were enrolled. Deep transfer learning features and radiomics features extracted from gray scale ultrasound images were input to machine learning classifiers including logistic regression (LR), support vector machines (SVM), KNN, RandomForest (RF), ExtraTrees, XGBoost, LightGBM, and MLP to construct deep transfer learning radiomics (DTL) models and Rad models respectively. Deep learning radiomics (DLR) models were constructed by integrating the two features and DLR signatures were generated. Clinical features were further combined with the signatures to develop a DLRN model. The performance of these models was evaluated using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test.
RESULTS: In the internal validation cohort and external validation cohort, comparing to Clinic (AUC=0.767 and 0.777), Rad (AUC=0.841 and 0.748), DTL (AUC=0.740 and 0.825) and DLR (AUC=0.863 and 0.859), the DLRN model showed greatest discriminatory ability (AUC=0.908 and 0.908) showed optimal discriminatory ability.
CONCLUSIONS: The DLRN model built based on gray scale ultrasonography significantly improved the diagnostic performance for benign salivary gland tumors. It can provide clinicians with a non-invasive and accurate diagnostic approach, which holds important clinical significance and value. Ensemble of multiple models helped alleviate overfitting on the small dataset compared to using Resnet50 alone.

BACKGROUND: To develop a deep learning(DL) model utilizing ultrasound images, and evaluate its efficacy in distinguishing between benign and malignant parotid tumors (PTs), as well as its practicality in assisting clinicians with accurate diagnosis.
METHODS: A total of 2211 ultrasound images of 980 pathologically confirmed PTs (Training set: n = 721; Validation set: n = 82; Internal-test set: n = 89; External-test set: n = 88) from 907 patients were retrospectively included in this study. The optimal model was selected and the diagnostic performance evaluation is conducted by utilizing the area under curve (AUC) of the receiver-operating characteristic(ROC) based on five different DL networks constructed at varying depths. Furthermore, a comparison of different seniority radiologists was made in the presence of the optimal auxiliary diagnosis model. Additionally, the diagnostic confusion matrix of the optimal model was calculated, and an analysis and summary of misjudged cases\' characteristics were conducted.
RESULTS: The Resnet18 demonstrated superior diagnostic performance, with an AUC value of 0.947, accuracy of 88.5%, sensitivity of 78.2%, and specificity of 92.7% in internal-test set, and with an AUC value of 0.925, accuracy of 89.8%, sensitivity of 83.3%, and specificity of 90.6% in external-test set. The PTs were subjectively assessed twice by six radiologists, both with and without the assisted of the model. With the assisted of the model, both junior and senior radiologists demonstrated enhanced diagnostic performance. In the internal-test set, there was an increase in AUC values by 0.062 and 0.082 for junior radiologists respectively, while senior radiologists experienced an improvement of 0.066 and 0.106 in their respective AUC values.
CONCLUSIONS: The DL model based on ultrasound images demonstrates exceptional capability in distinguishing between benign and malignant PTs, thereby assisting radiologists of varying expertise levels to achieve heightened diagnostic performance, and serve as a noninvasive imaging adjunct diagnostic method for clinical purposes.

Precise recognition of the intraparotid facial nerve (IFN) is crucial during parotid tumor resection. We aimed to explore the application effect of direct visualization of the IFN in parotid tumor resection. Fifteen patients with parotid tumors were enrolled in this study and underwent specific radiological scanning in which the IFNs were displayed as high-intensity images. After image segmentation, IFN could be preoperatively directly visualized. Mixed reality combined with surgical navigation were applied to intraoperatively directly visualize the segmentation results as real-time three-dimensional holograms, guiding the surgeons in IFN dissection and tumor resection. Radiological visibility of the IFN, accuracy of image segmentation and postoperative facial nerve function were analyzed. The trunks of IFN were directly visible in radiological images for all patients. Of 37 landmark points on the IFN, 36 were accurately segmented. Four patients were classified as House-Brackmann Grade I postoperatively. Two patients with malignancies had postoperative long-standing facial paralysis. Direct visualization of IFN was a feasible novel method with high accuracy that could assist in recognition of IFN and therefore potentially improve the treatment outcome of parotid tumor resection.

BACKGROUND: In the application of APTw protocols for evaluating tumors and parotid glands, inhomogeneity and hyperintensity artifacts have remained an obstacle. This study aimed to improve APTw imaging quality and evaluate the feasibility of difference B1 values to detect parotid tumors.
METHODS: A total of 31 patients received three APTw sequences to acquire 32 lesions and 30 parotid glands (one patient had lesions on both sides). Patients received T2WI and 3D turbo-spin-echo (TSE) APTw imaging on a 3.0 T scanner for three sequences (B1 = 2 μT, 1 μT, and 0.7 μT in APTw 1, 2, and 3, respectively). APTw image quality was evaluated using four-point Likert scales in terms of integrity and hyperintensity artifacts. Image quality was compared between the three sequences. An evaluable group and a trustable group were obtained for APTmean value comparison.
RESULTS: Tumors in both APT2 and APT3 had fewer hyperintensity artifacts than in APT1. With B1 values decreasing, tumors had less integrity in APTw imaging. APTmean values of tumors were higher than parotid glands in traditional APT1 sequence though not significant, while the APTmean subtraction value was significantly different.
CONCLUSIONS: Applying a lower B1 value could remove hyperintensity but could also compromise its integrity. Combing different APTw sequences might increase the feasibility of tumor detection.

UNASSIGNED: To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.
UNASSIGNED: This retrospective study aimed to analyze the clinical and radiographic characteristics of 162 cases from December 2019 to March 2023. The study population consisted of a training cohort of 113 patients and a validation cohort of 49 patients. Grayscale ultrasonography was processed using ITP-Snap software and Python to delineate regions of interest (ROIs) and extract radiomic features. Univariate analysis, Spearman\'s correlation, greedy recursive elimination strategy, and least absolute shrinkage and selection operator (LASSO) correlation were employed to select relevant radiographic features. Subsequently, eight machine learning methods (LR, SVM, KNN, RandomForest, ExtraTrees, XGBoost, LightGBM, and MLP) were employed to build a quantitative radiomic model using the selected features. A radiomic nomogram was developed through the utilization of multivariate logistic regression analysis, integrating both clinical and radiomic data. The accuracy of the nomogram was assessed using receiver operating characteristic (ROC) curve analysis, calibration, decision curve analysis (DCA), and the Hosmer-Lemeshow test.
UNASSIGNED: To differentiate PA from AL, the radiomic model using SVM showed optimal discriminatory ability (accuracy = 0.929 and 0.857, sensitivity = 0.946 and 0.800, specificity = 0.921 and 0.897, positive predictive value = 0.854 and 0.842, and negative predictive value = 0.972 and 0.867 in the training and validation cohorts, respectively). A nomogram incorporating rad-Signature and clinical features achieved an area under the ROC curve (AUC) of 0.983 (95% confidence interval [CI]: 0.965-1) and 0.910 (95% CI: 0.830-0.990) in the training and validation cohorts, respectively. Decision curve analysis showed that the nomogram and radiomic model outperformed the clinical-factor model in terms of clinical usefulness.
UNASSIGNED: A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

OBJECTIVE: Facial nerve paralysis is the most problematic complication of surgery for parotid tumors. This study aimed to examine the progress of recovery from postoperative transient facial nerve paralysis (POFNP).
METHODS: Participants were 203 patients who developed POFNP after benign parotid surgery. A Kaplan-Meier showed the progress of recovery from paralysis. Factors involved in recovery were examined. For factors for which a significant difference was found, recovery from paralysis was examined over time.
RESULTS: Rates of recovery from paralysis were as follows: 28.6% of patients at 1 month, 58.3% at 3 months, 85.9% at 6 months, and 95.1% at 12 months after surgery. Deep lobe tumors were shown to be significantly associated with delayed recovery from paralysis. The relationship between tumor location and the time of recovery from was that deep lobe tumors had a significantly worse recovery from paralysis at 4 and 5 months after surgery.
CONCLUSIONS: Patients who develop POFNP must be informed about the progress of recovery and factors involved in recovery from paralysis. We believe that the results of the present study are a useful reference to that end.

Tuberculosis (TB) is a significant health problem and mortality in most developing countries. It is a chronic granulomatous disease caused by Mycobacterium tuberculosis and M. tuberculosis complex. It can be pulmonary form or Extra pulmonary form. Extrapulmonary tuberculosis involving temporomandibular joint is infrequent presentation of Skeletal TB. Here we present a rare case of extrapulmonary tuberculosis that was initially misdiagnosed as a parotid lesion due to atypical signs and symptoms and multiple in-conclusive FNAC reports. The final diagnosis was established by histopathological report.