Diagnostic accuracy of ultrasonography-guided core needle biopsy of parotid gland neoplasms: A large, single-institution experience in United Kingdom

Utility of fine-needle aspiration biopsy (FNAB) in parotid pleomorphic adenoma diagnosis and management

PURPOSE

Pleomorphic adenoma (PA), the most common benign tumour of the parotid gland, requires accurate preoperative diagnosis owing to its capacity for malignant transformation. The aim of this study was to evaluate our experience with ultrasound-guided fine-needle aspiration biopsy (FNAB) in the diagnostic algorithm for patients with PA and to assess clinical outcomes for those with different surgical approaches.

MATERIAL AND METHODS

We carried out a retrospective analysis of patients treated for parotid gland mass between 2010 and 2016. These had had preoperative FNAB and had undergone subsequent surgery.

RESULTS

165 patients had FNAB with the result of PA and the definitive histology confirmed PA in 159 cases (96.4%). On the other hand, in 179 patients, the definitive histology showed PA and the preoperative FNAB result corresponded in 159 cases (88.9%). The measured sensitivity, specificity and accuracy of ultrasound-guided FNAB in the diagnosis of PA were, respectively, 88.83%, 96.23% and 92.31%. Most of the patients underwent superficial or partial superficial parotidectomy, followed by extracapsular dissection which was associated with statistically lower risk of facial nerve injury (P=0.04).

CONCLUSION

Ultrasound-guided FNAB is simple, accurate and valuable in the diagnosis of PA and provides results that can lead to the choice of less invasive operative treatment.

Current Salivary Glands Biopsy Techniques: A Comprehensive Review

Biopsy is a surgical procedure performed to collect a portion of tissue or organ for diagnostic studies. The aim of the present manuscript is to describe state-of-the-art major and minor salivary gland biopsy techniques and assess the indications and complications of other salivary gland biopsy techniques. A search was performed using the following MeSH terms: biopsy, fine-needle biopsies, image-guided biopsies, frozen sections, and salivary glands disease. A current overview of major and minor salivary glands biopsy techniques was provided. In the oncological field, a comparison was made between the most widely used biopsy method, ultrasound-guided fine-needle aspiration biopsy (US-FNAB), and an alternative method, ultrasound-guided core needle biopsy (US-guided CNB), highlighting the advantages and disadvantages of each. Finally, intra-operative frozen sections (IOFSs) were presented as an additional intraoperative diagnostic method. Minor salivary gland biopsy (MSGB) is the simplest diagnostic method used by clinicians in the diagnosis of inflammatory and autoimmune diseases. In neoplastic lesions, US-FNAB represents the most performed method; however, due to its low diagnostic accuracy for non-neoplastic specimens, US-guided CNB has been introduced as an alternative method.

Multiphasic CT-Based Radiomics Analysis for the Differentiation of Benign and Malignant Parotid Tumors

Objective

This study aims to investigate the value of machine learning models based on clinical-radiological features and multiphasic CT radiomics features in the differentiation of benign parotid tumors (BPTs) and malignant parotid tumors (MPTs).

Methods

This retrospective study included 312 patients (205 cases of BPTs and 107 cases of MPTs) who underwent multiphasic enhanced CT examinations, which were randomly divided into training (N = 218) and test (N = 94) sets. The radiomics features were extracted from the plain, arterial, and venous phases. The synthetic minority oversampling technique was used to balance minority class samples in the training set. Feature selection methods were done using the least absolute shrinkage and selection operator (LASSO), mutual information (MI), and recursive feature extraction (RFE). Two machine learning classifiers, support vector machine (SVM), and logistic regression (LR), were then combined in pairs with three feature selection methods to build different radiomics models. Meanwhile, the prediction performances of different radiomics models based on single phase (plain, arterial, and venous phase) and multiphase (three-phase combination) were compared to determine which model construction method and phase were more discriminative. In addition, clinical models based on clinical-radiological features and combined models integrating radiomics features and clinical-radiological features were established. The prediction performances of the different models were evaluated by the area under the receiver operating characteristic (ROC) curve (AUC) and the drawing of calibration curves.

Results

Among the 24 established radiomics models composed of four different phases, three feature selection methods, and two machine learning classifiers, the LASSO-SVM model based on a three-phase combination had the optimal prediction performance with AUC (0.936 [95% CI = 0.866, 0.976]), sensitivity (0.78), specificity (0.90), and accuracy (0.86) in the test set, and its prediction performance was significantly better than with the clinical model based on LR (AUC = 0.781, p = 0.012). In the test set, the combined model based on LR had a lower AUC than the optimal radiomics model (AUC = 0.933 vs. 0.936), but no statistically significant difference (p = 0.888).

Conclusion

Multiphasic CT-based radiomics analysis showed a machine learning model based on clinical-radiological features and radiomics features has the potential to provide a valuable tool for discriminating benign from malignant parotid tumors.