Efficacy of digital zoning design for the resection of benign parotid gland tumor.

The objective of this study is to verify the role of digital modified parotid tumor zoning method in modified parotid incision. The data of patients with parotid benign tumors from November 2021 to December 2023 were collected. Through the use of digital technology for soft tissue reconstruction, the parotid tumor was divided into four areas according to the digital image marker points. We designed the surgical incision according to the parotid gland division, found that it was feasible to guide the incision selection by division, and summarized the common incision and division corresponding, zone I was I and V-shaped incision, zone II was V incision, zone III was V and C- shaped incision, and zone IV was C- shaped incision. We conclude that the digital modified parotid gland zoning method can provide a better distinction in the surgical incision, and provide a better cosmetic incision and prognosis.

Swin MoCo: Improving parotid gland MRI segmentation using contrastive learning.

BACKGROUND: Segmentation of the parotid glands and tumors by MR images is essential for treating parotid gland tumors. However, segmentation of the parotid glands is particularly challenging due to their variable shape and low contrast with surrounding structures. PURPOSE: The lack of large and well-annotated datasets limits the development of deep learning in medical images. As an unsupervised learning method, contrastive learning has seen rapid development in recent years. It can better use unlabeled images and is hopeful to improve parotid gland segmentation. METHODS: We propose Swin MoCo, a momentum contrastive learning network with Swin Transformer as its backbone. The ImageNet supervised model is used as the initial weights of Swin MoCo, thus improving the training effects on small medical image datasets. RESULTS: Swin MoCo trained with transfer learning improves parotid gland segmentation to 89.78% DSC, 85.18% mIoU, 3.60 HD, and 90.08% mAcc. On the Synapse multi-organ computed tomography (CT) dataset, using Swin MoCo as the pre-trained model of Swin-Unet yields 79.66% DSC and 12.73 HD, which outperforms the best result of Swin-Unet on the Synapse dataset. CONCLUSIONS: The above improvements require only 4 h of training on a single NVIDIA Tesla V100, which is computationally cheap. Swin MoCo provides new approaches to improve the performance of tasks on small datasets. The code is publicly available at https://github.com/Zian-Xu/Swin-MoCo.

Factors influencing Frey syndrome after parotidectomy with acellular dermal matrix.

BACKGROUND: Frey syndrome, also known as ototemporal nerve syndrome or gustatory sweating syndrome, is one of the most common complications of parotid gland surgery. This condition is characterized by abnormal sensations in the facial skin accompanied by episodes of flushing and sweating triggered by cognitive processes, visual stimuli, or eating. AIM: To investigate the preventive effect of acellular dermal matrix (ADM) on Frey syndrome after parotid tumor resection and analyzed the effects of Frey syndrome across various surgical methods and other factors involved in parotid tumor resection. METHODS: Retrospective data from 82 patients were analyzed to assess the correlation between sex, age, resection sample size, operation time, operation mode, ADM usage, and occurrence of postoperative Frey syndrome. RESULTS: Among the 82 patients, the incidence of Frey syndrome was 56.1%. There were no significant differences in sex, age, or operation time between the two groups (P > 0.05). However, there was a significant difference between ADM implantation and occurrence of Frey syndrome (P < 0.05). ADM application could reduce the variation in the incidence of Frey syndrome across different operation modes. CONCLUSION: ADM can effectively prevent Frey syndrome and delay its onset.

Metastasizing Pleomorphic Adenoma of Parotid Gland: A Case Report and Literature Review.

The authors hereby present a case report of metastasizing pleomorphic adenoma (MPA) of the parotid gland with multiple metachronous cervical lymph node metastases and sternocleidomastoid muscle infiltration. Diagnostic evaluation, surgical management, and follow-up are discussed along with a brief review of the literature.

Incidence of Facial Nerve Paralysis After Parotidectomy: Our Experience.

Facial nerve palsy (FNP) is a well-recognized complication following parotidectomy, with varying reported incidence rates in the literature. Understanding the incidence and factors contributing to FNP is crucial for optimizing patient care and surgical outcomes. A retrospective analysis was conducted on 78 patients who underwent parotidectomy at a tertiary care institution (Hospital de Especialidades Carlos Andrade Marin, Quito) over a 36-month period. Demographic data, preoperative pathology reports, surgical details, and postoperative outcomes, including FNP incidence and severity, were analyzed. The mean age of the cohort was 53 years, with a male-to-female ratio of 0.8:1. Fine needle aspiration revealed benign pathology in 70.5% of cases, with superficial parotidectomy being the most common surgical approach (84.6%). Postoperatively, FNP was observed in 51.2% of cases, with transient paralysis in 62.5% and persistent paralysis in 37.5%. The majority of FNP cases were classified as grade II and III according to the House-Brackmann grading system. A tumor size larger than 4 cm was associated with a higher incidence of FNP (57.5%). This study provides valuable insights into the incidence and severity of FNP following parotidectomy. Despite efforts to standardize surgical techniques, persistent paralysis remains a significant concern.

Deep Network-Based Comprehensive Parotid Gland Tumor Detection.

RATIONALE AND OBJECTIVES: Salivary gland tumors constitute 2%-6% of all head and neck tumors and are most common in the parotid gland. Magnetic resonance (MR) imaging is the most sensitive imaging modality for diagnosis. Tumor type, localization, and relationship with surrounding structures are important factors for treatment. Therefore, parotid gland tumor segmentation is important. Specialists widely use manual segmentation in diagnosis and treatment. However, considering the development of artificial intelligence-based models today, it is seen that artificial intelligence-based automatic segmentation models can be used instead of manual segmentation, which is a time-consuming technique. Therefore, we segmented parotid gland tumor (PGT) using deep learning-based architectures in the paper. MATERIALS AND METHODS: The dataset used in the study includes 102 T1-w, 102 contrast-enhanced T1-w (T1C-w), and 102 T2-w MR images. After cropping the raw and manually segmented images by experts, we obtained the masks of these images. After standardizing the image sizes, we split these images into approximately 80% training set and 20% test set. Hereabouts, we trained six models for these images using ResNet18 and Xception-based DeepLab v3+. We prepared a user-friendly Graphical User Interface application that includes each of these models. RESULTS: From the results, the accuracy and weighted Intersection over Union values of the ResNet18-based DeepLab v3+ architecture trained for T1C-w, which is the most successful model in the study, are equal to 0.96153 and 0.92601, respectively. Regarding the results and the literature, it can be seen that the proposed system is competitive in terms of both using MR images and training the models independently for T1-w, T1C-w, and T2-w. Expressing that PGT is usually segmented manually in the literature, we predict that our study can contribute significantly to the literature. CONCLUSION: In this study, we prepared and presented a software application that can be easily used by users for automatic PGT segmentation. In addition to predicting the reduction of costs and workload through the study, we developed models with meaningful performance metrics according to the literature.

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

PURPOSE: To investigate the diagnostic performance of histogram features of diffusion parameters in characterizating parotid gland tumors. METHOD: From 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. RESULTS: For 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). CONCLUSIONS: Whole-tumor histogram analysis of IVIM and combined diffusion model could further improve the diagnostic performance for differentiating BPGTs from MPGTs.

Classification of benign and malignant parotid tumors based on CT images combined with stack generalization model.

Parotid tumors are among the most prevalent tumors in otolaryngology, and malignant parotid tumors are one of the main causes of facial paralysis in patients. Currently, the main diagnostic modality for parotid tumors is computed tomography, which relies mainly on the subjective judgment of clinicians and leads to practical problems such as high workloads. Therefore, to assist physicians in solving the preoperative classification problem, a stacked generalization model is proposed for the automated classification of parotid tumor images. A ResNet50 pretrained model is used for feature extraction. The first layer of the adopted stacked generalization model consists of multiple weak learners, and the results of the weak learners are integrated as input data in a meta-classifier in the second layer. The output results of the meta-classifier are the final classification results. The classification accuracy of the stacked generalization model reaches 91%. Comparing the classification results under different classifiers, the stacked generalization model used in this study can identify benign and malignant tumors in the parotid gland effectively, thus relieving physicians of tedious work pressure.

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.

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

OBJECTIVES: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Factors of perioperative depressive and anxiety symptoms in patients with parotid gland tumor and its influence on postoperative complication and quality of life: a cohort study.

Background: Parotid gland tumors (PGTs) are the most common benign neoplasms of salivary gland tumor. Due to its tendency to affect the aesthetic appearance of the face, patients with PGTs are often prone to negative emotions, which seriously affects their recovery. However, there are fewer studies on risk factors associated with the development of anxiety and depression in patients. This study aimed to analyze the risk factors for negative emotions in the perioperative period of PGT patients and their effects on postoperative complication and quality of life and provide a reference basis for improving patient prognosis. Methods: We retrospectively analyzed 186 patients with PGT admitted to our hospital from August 2017 to August 2021. We evaluated and compiled the general clinical characteristics of the patients. The t-test and chi-square test were used to analyze the relationship between patients' negative emotions and prognosis. Multiple linear regression was used to analyze the independent risk factors for negative emotions and poor prognosis of patients in the perioperative period. Patients were followed up 6 months after discharge by outpatient or telephone follow-up. Results: Among 186 patients with PGT, a total of 43 (23.1%) patients showed symptoms of anxiety and 46 (24.7%) patients showed depression. Multiple linear regression analysis showed that the patients' education (P=0.001), with or without hypertension (P=0.048), tumor characteristics (P=0.001), postoperative complications of transient or permanent facial palsy (P=0.008, P=0.027), and recurrence (P=0.018) were independent risk factors for anxiety among patients, while the patients' educational level (P=0.018), tumor metastasis (P=0.044), and postoperative Frey syndrome (P=0.011) were independent risk factors for depression among patients. In addition, we found that the patients' negative emotions (P=0.000), educational level (P=0.015), residency (P=0.027), with or without hyperlipidemia (P=0.042), tumor characteristics (P=0.000), surgery time (P=0.037), and postoperative complications (P=0.041) were independent risk factors for patient prognosis. Conclusions: PGT patients are prone to various postoperative complications and have a high likelihood of having negative emotions and seriously affects the prognosis. Clinical efforts should pay attention to patients' emotions and demeanor, the identification of relevant risk factors, the adoption of targeted measures to alleviate patients' anxiety and depression, and improve the prognosis.

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

PURPOSE: This 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. METHODS: A 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. RESULTS: The 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). CONCLUSION: T1 mapping and RESOLVE-DWI can be used to differentiate parotid gland tumors quantitatively and can be complementary to each other.

Malignant trichilemmal carcinoma misdiagnosed with parotid gland malignant tumor.

Malignat proliferating trichilemmal tumor (MPTT) are rare tumors usually presenting in photo-exposed areas, especially on the face, scalp, neck,and dorsal part of the hand. Differential diagnosis include squamous cell carcinoma, basal-cell carcinoma, keratoacanthoma and malignant nodular melanoma, so that only incisional biopsy can lead to pre-operative diagnosis. We present case MPTT misdiagnosed with parotid gland malignant tumor that underwent radical surgical excision and adijuvant radiotherapy.

A rare case of canalicular adenoma in the parotid gland: Highlighting diagnostic limitations of fine-needle aspiration.

BACKGROUND: Canalicular adenoma is a rare, benign tumor of primarily salivary gland origin that presents mostly in the upper lip. However, there are only six reports in the English literature detailing canalicular adenoma of the parotid gland, none of which discuss discrepancy between preoperative cytology and surgical pathology. In this report, we present a rare case of parotid gland canalicular adenoma where preoperative ultrasound-guided fine-needle aspiration (USFNA) suggested malignancy. The patient was treated with deep lobe parotidectomy due to the FNA results and her multiple comorbidities. However, her tumor may have been treated with observation alone if canalicular adenoma had been suspected prior to surgery. MAIN FINDINGS: A 59-year-old female with a history of heart and lung disease presented with a 1.6 cm well defined, enhancing lesion involving the superficial portion of the right parotid gland. This lesion was incidentally noted on CT angiography (CTA) of the neck and chest. The well-defined characteristics of this lesion on CT imaging suggested benign neoplasm. However, USFNA results were suggestive of a malignant parotid lesion. The patient subsequently underwent right deep lobe parotidectomy with facial nerve dissection and superficial musculoaponeurotic system (SMAS) rotational flap reconstruction. Surgical pathology and immunohistochemistry yielded a final diagnosis of benign canalicular adenoma. CONCLUSIONS: USFNA diagnosis of CA is extremely difficult due to its low-grade neoplastic cells mimicking neoplastic cells in other benign and malignant tumors of the head and neck. FNA remains a useful tool for assessing malignancy risk, but the results always have some level of uncertainty and do not provide sufficient detail. Therefore, FNA results should be interpreted in concert with imaging and patients' medical history. Cytopathologists can also report salivary gland FNA results in a more uniform and detailed manner by utilizing the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC).

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

OBJECTIVES: 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. MATERIALS AND METHODS: 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. RESULTS: 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. CONCLUSION: 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.

PURPOSE: To evaluate the value of diffusion-weighted imaging and dynamic contrast-enhanced MRI for the diagnosis of parotid gland tumors. METHODS: 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. RESULTS: Inclusion of 133 patients (mean age: 53 years). 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. CONCLUSIONS: 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.

Comparison of different methods for evaluating the relationship between facial nerve and benign parotid tumors.

Objectives: The goals of benign parotid gland tumor resection are complete resection of the lesion and preservation of the facial nerve function. As the facial nerve cannot be directly visualized via imaging modalities, several methods, including the facial nerve line, Utrecht line, retromandibular vein, Stenon duct, and minimum fascia-tumor distance techniques, have been developed to estimate its location. However, there are no reports on their accuracy in determining tumor location. In the present study, we aimed to assess the diagnostic accuracy of these methods based on tumor location. Methods: This retrospective study analyzed medical records and histological reports of 359 patients with various types of benign parotid gland tumors who underwent a parotidectomy between April 2014 and March 2020. The tumor location was subdivided into the following sections: anterior, superior, inferior, and middle. The tumor location was estimated using five methods: facial nerve line, Utrecht line, retromandibular vein, Stenon duct, and minimum fascia-tumor distance. The final diagnosis of superficial or deep lobe tumor was made based on surgical findings. Results: Each method showed a higher accuracy for superficial tumors (was more than 90%) than for deep lobe tumors. In contrast, for deep lobe tumors, the accuracy of diagnosis with the facial nerve line, Utrecht line, and retromandibular vein methods was low, in the 30% range. Among all methods, the Stenon duct method had the highest accuracy in the diagnosis of deep lobe tumors. The SD method was most useful in cases where both the duct and tumors were detected. The minimum fascia-tumor distance method had the second highest diagnostic accuracy (63%); however, for anterior tumors, it tended to provide false negatives. Conclusions: All tested methods were useful in diagnosing superficial lobe tumors; however, they were not helpful in diagnosing deep lobe tumors, especially anterior tumors.