Comprehensive analysis of parotid mass: A retrospective study of 369 cases

Multi-feature Fusion Network on Gray Scale Ultrasonography: Effective Differentiation of Adenolymphoma and Pleomorphic Adenoma

RATIONALE AND OBJECTIVES

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.

CONCLUSION

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.

Parotid cancer: analysis of preoperative parameters for adaptation of the therapeutic strategy

PURPOSE

To establish typical clinical and radiological profiles of primary low-grade parotid cancers in order to tailor therapeutic strategy.

MATERIALS AND METHODS

Retrospective study of 57 patients operated on for primary parotid cancer between 2010 and 2021, with review of preoperative MRI and histopathology according to a standardized scoring grid.

OBJECTIVE

To study prognostic factors and determine the preoperative clinical and radiological profile of low-grade cancers.

RESULTS

Good prognostic factors for specific survival were: staging ≤ cT3 (p = 0.014), absence of adenopathy on cN0 MRI (p < 0.001), superficial lobe location (p = 0.033), pN0 (p < 0.001), absence of capsular rupture (p = 0.004), as well as the absence of peri-tumoral nodules (p = 0.033), intra-parotid adenopathies (p < 0.001), vascular emboli (p < 0.001), peri-neural sheathing (p = 0.016), nuclear atypia (p = 0.031), and necrosis (p = 0.002). It was not possible to define a reliable clinical and radiological profile for low-grade cancers (sensitivity 38%, specificity 79%).

CONCLUSION

Our study demonstrated multiple factors of good prognosis, but it was not possible to define a clinical and radiological profile of patients likely to benefit from more limited surgery, nor to diagnose, a priori, low-grade cancers.

Deep learning-assisted diagnosis of benign and malignant parotid tumors based on ultrasound: a retrospective study

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.

Radiomic nomogram for discriminating parotid pleomorphic adenoma from parotid adenolymphoma based on grayscale ultrasonography

Objectives

To differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.

Methods

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.

Results

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.

Conclusion

A nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

A solitary fibrous tumor of the parotid gland: Case report

INTRODUCTION

Solitary fibrous tumor is a rare neoplasm that can affect any part of the body, also head and neck region. Etiology is unknown. The incidence is slightly higher in males, the age ranges from 11 to 79 years.

PRESENTATION OF CASE

It's the first case in our country of left parotid solitary fibrous tumor, removed by partial parotidectomy with facial nerve preservation. Histology examination showed diffuse spindle-shaped cells proliferation, moderate polymorphism, low mitotic index (<4 mitoses per 10 HPF), partially bordered by fibrous capsule. Immunohistochemistry showed STAT6, CD34, CD99 positivity. Six-months follow-up didn't show sign of recurrence.

DISCUSSION

Solitary fibrous tumor is a mesenchymal spindle cell neoplasm with fibroblastic differentiation ubiquitous in soft tissues, that involved the head and neck region in 6 % of cases. Etiology is unknown. The possible pathogenesis is NAB2-STAT6 gene fusion. It's asymptomatic or symptoms are related to space-occupying mass. Diagnostic work up involves imaging, immunohistochemistry, histology. Radiographic finding may lead to incorrect assessment of the mass: the same imaging features are present in pleomorphic adenoma, the most frequent tumor of salivary glands.

CONCLUSION

This case report aims to stress that, although rare, solitary fibrous tumor should be considered in differential diagnosis in case of indolent salivary gland mass, since it may require more invasive approach (e.g., total parotidectomy, adjuvant radiotherapy). It would like to highlight the role of multidisciplinary team to define the best therapy, tailored for the patient, as well as to give awareness to a rare but sometimes aggressive tumor.

Is the combined use of ultrasonography (USG) and fine needle aspiration biopsy (FNAB) safe in parotis masses? Retrospective comprehensive comparison of 123 cases

OBJECTIVES

The purpose of the study was to compare final pathology results with ultrasonography (USI) and fine needle aspiration biopsy (FNAB) results in parotis masses.

METHODS

A total of 123 patients with primary parotis mass who applied to our center between 2010 and 2020 were selected for the study. Among these, 100 patients with preoperative USI, preoperative FNAB, and postoperative final pathology were included in the study. USI, FNAB, pathology results, surgery types, and demographic characteristics of the patients were analyzed.

RESULTS

According to the postoperative final pathology, preoperative USI sensitivity was found to be 100%, specificity was 55, positive predictive value was 84.31%, negative predictive value was 100%, and accuracy was 86.89%. Preoperative FNAB had a sensitivity of 85.7%, a specificity of 92.1%, a positive predictive value of 82.1%, a negative predictive value of 90.2%, and a diagnostic accuracy of 89.3%, according to the postoperative final pathology.

CONCLUSION

Preoperative USI and preoperative FNAB are very valuable diagnostic tools in the evaluation of parotis lesions. When used together, they provide highly accurate and important data for the surgeon.

Clinical parameters predictors of malignant transformation of recurrent parotid pleomorphic adenoma

Malignant transformation (MT) in recurrent parotid pleomorphic adenomas (PAs) is rare; therefore its occurrence lacks reliable predictive factors. Our goal was to clarify the predictors for MT of recurrent parotid PAs based on preoperative clinical parameters. Patients with a clinical diagnosis of recurrent parotid PA were retrospectively enrolled. The association between clinicopathologic variables and MT of PA was assessed using univariate and multivariate analyses. MT occurred in 11.8% of the 467 patients. In univariate analysis, three or more previous recurrences, newly developed facial nerve paralysis, difficulty in mouth opening, tumors with the largest tumor diameter ≥ 2.4 cm, and abnormal neck lymph node enlargement were associated with MT occurrence. Further, multivariate analysis showed that three or more previous recurrences, newly developed facial nerve paralysis, difficulty in mouth opening, and abnormal neck lymph node enlargement were independently related to MT. MT of recurrent PA was not uncommon. Clinical signs of malignancy included newly developed facial nerve paralysis, difficulty in mouth opening, three or more previous recurrences, and abnormal neck lymph node enlargement.

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

Objective

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

Methods

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

Results

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

Conclusion

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

Development and validation of CT-based radiomics nomogram for the classification of benign parotid gland tumors

PURPOSE

Accurate preoperative diagnosis of parotid tumor is essential for the formulation of optimal individualized surgical plans. The study aims to investigate the diagnostic performance of radiomics nomogram based on contrast-enhanced computed tomography (CT) images in the differentiation of the two most common benign parotid gland tumors.

METHODS

One hundred and ten patients with parotid gland tumors including 76 with pleomorphic adenoma (PA) and 34 with adenolymphoma (AL) confirmed by histopathology were included in this study. Radiomics features were extracted from contrast-enhanced CT images of venous phase. A radiomics model was established and a radiomics score (Rad-score) was calculated. Clinical factors including clinical data and CT features were assessed to build a clinical factor model. Finally, a nomogram incorporating the Rad-score and independent clinical factors was constructed. Receiver operator characteristics (ROC) curve was generated and the area under the ROC curve (AUC) was calculated to quantify the discriminative performance of each model on both the training and validation cohorts. Decision curve analysis (DCA) was conducted to evaluate the clinical usefulness of each model.

RESULTS

The radiomics model showed good discrimination in the training cohort [AUC, 0.89; 95% confidence interval (CI), 0.80-0.98] and validation cohort (AUC, 0.89; 95% CI, 0.77-1.00). The radiomics nomogram showed excellent discrimination in the training cohort (AUC, 0.98; 95% CI, 0.96-1.00) and validation cohort (AUC, 0.95; 95% CI, 0.88-1.00) and displayed better discrimination efficacy compared with the clinical factor model (AUC, 0.93; 95% CI, 0.88-0.99) in the training cohort (p < 0.05). The DCA demonstrated that the combined radiomics nomogram provided superior clinical usefulness than clinical factor model and radiomics model.

CONCLUSIONS

The CT-based radiomics nomogram combining Rad-score and clinical factors exhibits excellent predictive capability for differentiating parotid PA from AL, which might hold promise in assisting radiologists and clinicians in the exact differential diagnosis and formulation of appropriate treatment strategy.

Parotid metastases from primary lung cancer: Case series and systematic review of the features

Most parotid metastases have been reported to come from the head and neck; however, cases metastasized from the lung are extremely rare. Missed diagnoses and misdiagnoses occurred quite a few times. Thus, accurately identifying the clinical features of parotid metastasis of lung cancer is important. However, current studies about this issue are mostly case reports, and little is known about the detailed and systematic aspects. We reported three cases of parotid metastases from lung cancer and then systematically searched similar cases through “Pub-Med” and “Web of Science”. Finally, twenty-three patients were included in the study. Eighty-three percent of which were males, and 19 patients were over 50 years old. In all cases with smoking history mentioned, 93% were smokers. The predominant pathological type was small cell lung cancer (SCLC, 13 patients, 56%). Seventeen combined with other site metastasis, while more than half of which were brain metastases. The survival time ranged from 3months-17years, and as for SCLCs, it was only 3months-40months. It can be concluded that clinical features, such as sex, age, smoking history, pathological types, and metastasis patterns, could provide valuable evidence for diagnosis. The lung seems to be the most common primary site of parotid metastases except for head and neck tumors. The two circumstances, SCLC coexisting with Warthin’s tumor and parotid small cell carcinoma with lung metastasis, should be differentiated from parotid metastasis of lung cancer with caution For cases presented as SCLC, more aggressive strategies, such as chemotherapy with immunotherapy and maintenance therapy, may be more suitable. Due to the greater tendency of brain metastasis in such diseases, whole-brain radiation therapy, stereotactic radiosurgery or prophylactic cranial irradiation should be applied to corresponding patients in time. Additionally, lung cancer parotid metastases may be a marker of poor prognosis.

CT-based radiomics analysis of different machine learning models for differentiating benign and malignant parotid tumors

OBJECTIVES

This study aimed to explore and validate the value of different radiomics models for differentiating benign and malignant parotid tumors preoperatively.

METHODS

This study enrolled 388 patients with pathologically confirmed parotid tumors (training cohort: n = 272; test cohort: n = 116). Radiomics features were extracted from CT images of the non-enhanced, arterial, and venous phases. After dimensionality reduction and selection, radiomics models were constructed by logistic regression (LR), support vector machine (SVM), and random forest (RF). The best radiomic model was selected by using ROC curve analysis. Univariate and multivariable logistic regression was applied to analyze clinical-radiological characteristics and identify variables for developing a clinical model. A combined model was constructed by incorporating radiomics and clinical features. Model performances were assessed by ROC curve analysis, and decision curve analysis (DCA) was used to estimate the models' clinical values.

RESULTS

In total, 2874 radiomic features were extracted from CT images. Ten radiomics features were deemed valuable by dimensionality reduction and selection. Among radiomics models, the SVM model showed greater predictive efficiency and robustness, with AUCs of 0.844 in the training cohort; and 0.840 in the test cohort. Ultimate clinical features constructed a clinical model. The discriminatory capability of the combined model was the best (AUC, training cohort: 0.904; test cohort: 0.854). Combined model DCA revealed optimal clinical efficacy.

CONCLUSIONS

The combined model incorporating radiomics and clinical features exhibited excellent ability to distinguish benign and malignant parotid tumors, which may provide a noninvasive and efficient method for clinical decision making.

KEY POINTS

The current study is the first to compare the value of different radiomics models (LR, SVM, and RF) for preoperative differentiation of benign and malignant parotid tumors. A CT-based combined model, integrating clinical-radiological and radiomics features, is conducive to distinguishing benign and malignant parotid tumors, thereby improving diagnostic performance and aiding treatment.

Fine-Needle Aspiration Cytology of Salivary Gland Tumors Before the Milan System: Ten Years of Experience at a Tertiary Care Center in Turkey

Objective

The role of fine-needle aspiration cytology (FNAC) is well established for preoperative evaluation of patients with salivary gland lesions. However, the lack of a uniform system for salivary gland FNAC has limited its effectiveness. In recent years, the Milan System for Reporting Salivary Gland Cytopathology (MSRSGC) has been in use around the world to report the cytology results. We aimed to investigate the efficacy and accuracy of FNAC examined according to pre-MSRSGC era dichotomous benign/malignant classification in salivary gland tumors.

Methods

Patients who underwent surgery between January 2011 and December 2020 due to major salivary gland tumors were retrospectively analyzed. Two hundred and four patients were included in the analysis. Preoperative FNAC results and final histopatological diagnoses were grouped as benign or malignant. Final histopatological diagnoses were compared with the preoperative FNAC results. Also, sensitivity, specificity, and accuracy of the preoperative FNAC results, as well as the agreement between both tests were investigated.

Results

The sensitivity, specificity, accuracy, positive and negative predictive values of the preoperative FNAC for the diagnosis of malignancy were 59.09%, 97.85%, 93.75%, 76.47%, and 95.29%, respectively. There was a moderate agreement between the preoperative FNAC results and final histopatological diagnoses.

Conclusion

The accuracy of the preoperative FNAC and the information given about malignancy risk are the most important criteria for patient management and decision-making. The MSRSGC, which consists of a six-tiered classification rather than a dichotomous "yes/no" system, may contribute to patient management and decision-making by increasing the efficacy and accuracy of FNAC.

Management of Salivary Gland Malignancy: ASCO Guideline

PURPOSE

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

METHODS

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

RESULTS

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

RECOMMENDATIONS

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

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

Background

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

Methods

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

Results

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

Conclusions

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

Supplementary Information

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

Retrospective study of 124 cases of salivary gland tumors and literature review

Background

Salivary gland tumors are a rare and morphologically diverse group of lesions and their frequency is still unknown in several parts of the world. The knowledge of its population characteristics contributes to a better understanding of its etiopathogenesis. Objectives: This study investigated the frequency of salivary gland tumors in a region of southern Brazil and compared these data in a literature review.

Material and Methods

A retrospective study was conducted of salivary gland tumors diagnosed at two pathology centers from 1995 to 2016. Patient age and gender, tumor site and frequency, histopathological diagnosis, and symptomatology were evaluated. Chi-squared tests were used to assess the associations between variables. To compare our data, we also conducted a literature review of publications in the PubMed and LILACS databases of retrospective studies of salivary gland tumors.

Results

A total of 124 salivary gland tumor cases was identified, 81 (65.3%) of which were classified as benign and 43 (34.6%) as malignant. Most tumors occurred in the parotid gland (57.2%). Pleomorphic adenoma was the most common tumor (59.6%), followed by adenocarcinoma not otherwise specified (8.8%). The tumors occurred more often in women (54.8%) than in men (45.2%). Malignant tumors were associated with pain in 31.4% of cases (p<0.05). The literature review included 35 articles from different countries. Women were most affected, with a mean age of 41.7 years. The most common benign tumor was pleomorphic adenoma (48.2%) and the most common malignant tumor was mucoepidermoid carcinoma (8.7%).

Conclusions

The results of the present study showed that salivary gland tumors are rare. The parotid gland is the most common location and pleomorphic adenoma are the most frequent lesions. The malignant tumors presented as several histological types and the incidence was variable globally. Key words:Salivary gland neoplasms, salivary gland diseases, oral surgery, epidemiology.

Positive Surgical Margins in Submandibular Malignancies: Facility and Practice Variation

OBJECTIVES

Identify positive margin rate in a national cohort of patients with submandibular carcinoma, identify predictors of positive margins, and associate margins with overall survival.

STUDY DESIGN

Retrospective cohort.

SETTING

Commission on Cancer-accredited hospitals.

SUBJECTS AND METHODS

We included patients in the National Cancer Database from 2004 to 2014 who were diagnosed with submandibular carcinoma and underwent primary surgical resection. We determined the rate of positive surgical margins and associated patient, tumor, and treatment factors with positive margins via univariable and multivariable logistic regression analysis. We associated margin status with overall survival by Kaplan-Meier curve and Cox proportional hazards regression.

RESULTS

We identified 1150 patients with submandibular malignancy undergoing surgical resection. Positive margin rate was 41.0%. Increased odds of positive margins were seen in patients with advanced T stage (vs T1, T3: odds ratio [OR] = 3.04, P < .001; T4a: OR = 2.89, P < .001), adenoid cystic carcinoma histology (OR = 1.54, P = .020), and those treated at nonacademic facilities (OR = 1.41, P = .008). Patients who underwent a preoperative diagnostic biopsy had decreased odds of positive margins (OR = 0.72, P = .014). Positive margins were associated with reduced overall survival (58% vs 69% 5-year overall survival, P < .001; hazard ratio = 1.49, P = .001) when controlling for patient, tumor, and management factors.

CONCLUSIONS

The national positive margin rate of submandibular carcinoma is 41.0%. Preoperative biopsy and treatment at academic institutions independently decreased the risk of positive margins, and positive margins were independently associated with diminished overall survival. Positive margin rate for submandibular carcinoma may be considered a benchmark for quality of care.

Is fine needle aspiration biopsy reliable in the diagnosis of parotid tumors? Comparison of preoperative and postoperative results and the factors affecting accuracy

Introduction

Fine needle aspiration biopsy is a valuable tool in preoperative evaluation of head and neck tumors. However, its accuracy in management of salivary gland tumors is debatable.

Objective

We aimed to investigate the efficacy and the accuracy of fine needle aspiration biopsy in parotid gland tumors.

Methods

Patients who underwent parotidectomy between January 2008 and June 2017 due to parotid gland tumor were examined retrospectively. Patients with both preoperative fine needle aspiration biopsy and postoperative surgical pathologies were included. Preoperative fine needle aspiration biopsy was categorized as benign, malignant or suspicious for malignancy. Surgical pathology was grouped as benign or malignant. Surgical pathology was compared with fine needle aspiration biopsy, and sensitivity, specificity, accuracy and agreement between both tests were investigated.

Results

217 cases were evaluated and 23 cases were excluded because the fine needle aspiration biopsy diagnosis was non-diagnostic or unavailable. 194 cases were included. The mean age of the patients was 47.5 ± 15.88 (7–82). There were 157 benign, 37 malignant cases in fine needle aspiration biopsy, 165 benign and 29 malignant cases in surgical pathology. The most common benign tumor was pleomorphic adenoma (43.3%), and malignant tumor was mucoepidermoid carcinoma (4.13%). The diagnostic accuracy for fine needle aspiration biopsy when detecting malignancy was 86.52%. Sensitivity and specificity were 68.96% and 89.63% respectively. Positive predictive value was 54.05% and negative predictive value was 94.23%. There was moderate agreement between fine needle aspiration biopsy and surgical pathology (κ = 0.52). The sensitivity was 54.54% in tumors less than 2 cm while 77.77% in larger tumors. In tumors extending to the deep lobe, sensitivity was 80%.

Conclusion

Fine needle aspiration biopsy is an important diagnostic tool for evaluating parotid gland tumors. It is more accurate in detecting benign tumors. In tumors greater than 2 cm and extending to the deep lobe, the sensitivity of fine needle aspiration biopsy is high. The use of fine needle aspiration biopsy in conjunction with clinical and radiological evaluation may help to reduce false positive and false negative results.

Oncological outcomes of parotid gland malignancies: a retrospective analysis of 74 patients

INTRODUCTION

Salivary gland malignancies are rare neoplasms whose management has been evolving over the last two decades. Nevertheless, patient outcomes have not improved accordingly.

OBJECTIVE

In the present paper, factors and variables that could influence Overall, Disease-Specific and Disease-Free Survival, and Loco-Regional Control were analyzed.

METHODS

Chart data from 74 patients who underwent parotid gland surgery were retrospectively analyzed and stratified for tumor histology, grading, size, pT stage, pN stage, extracapsular spread, involved salivary gland lobe, and age at diagnosis. Major outcomes were estimated at 5 years by Kaplan-Meier curves.

RESULTS

Advanced stage, high grade, and lymph nodes involvement greatly impaired patient outcomes. Furthermore, in our cohort, the age at diagnosis ≥ 55 was a cause of poorer disease survival likely due to a different distribution in tumor histotypes between older and younger patients. Despite the two groups were homogeneous for the numerosity of squamous cell carcinomas, older patients were more rarely affected by mucoepidermoid and acinic cell carcinomas, which have generally better prognosis. Finally, patients aged ≥ 55 had a more frequent pathological involvement of the deep lobe of the parotid gland if compared to the younger counterpart.

CONCLUSION

The rarity of some salivary gland tumor histotypes requires further high-number series to fully understand the prognostic factors for both patient survival and recurrence development. In our cohort, the age at diagnosis ≥ 55 raises concerns that play crucial roles in disease survival shortening.