Ultrasound-guided core biopsy in the diagnosis of parotid neoplasia: an overview and update with a review of the literature

Hurry, Julia; Karunaratne, Dilhara; Westley, Suzanne; Booth, Joelle; Ramesar, K.; Zhang, Ting Ting; Williams, M. D.; Howlett, David

doi:10.1259/bjr.20210972

Cited by 9 publications

(12 citation statements)

References 37 publications

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“…The studies were from four categories: before-after (pre-post) studies with no control group [ 15 , 16 , 17 , 18 , 19 ], observational cohort and cross-sectional studies [ 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ], systematic reviews and meta-analyses [ 42 , 43 , 44 , 45 , 46 , 47 ], and non-randomized studies [ 48 , 49 , 50 , 51 , 52 ].…”

Section: Resultsmentioning

confidence: 99%

“…Since the cytological diagnosis of needle aspiration specimens has reduced accuracy for non-neoplastic specimens, US-guided CNB has been introduced as an alternative diagnostic method in the preoperative evaluation of salivary gland lesions due to its high sensitivity and specificity [ 42 ]. US-guided CNB is a method that allows collecting tissue fragments, including both the peripheral and central portion of the lesion, under an ultrasound guide, allowing the collection of a greater amount of cytological material than needle aspiration, preserving more of the histological architecture of the specimen, evaluating the extracapsular tumor invasion, and more precisely classifying the lesion [ 43 , 46 ]. After harvesting, the material is fixed in 10% formalin, placed in a paraffin block, 4–5 μm thick sections are made, and finally stained with various reagents to evaluate the architecture, cell morphology, and relationships of the lesion with the surrounding healthy glandular tissue [ 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

“…US-guided CNB is a method that allows collecting tissue fragments, including both the peripheral and central portion of the lesion, under an ultrasound guide, allowing the collection of a greater amount of cytological material than needle aspiration, preserving more of the histological architecture of the specimen, evaluating the extracapsular tumor invasion, and more precisely classifying the lesion [ 43 , 46 ]. After harvesting, the material is fixed in 10% formalin, placed in a paraffin block, 4–5 μm thick sections are made, and finally stained with various reagents to evaluate the architecture, cell morphology, and relationships of the lesion with the surrounding healthy glandular tissue [ 42 , 43 ]. Immunohistochemical staining appears to be more reliable in samples obtained by percutaneous ultrasound-guided biopsy [ 46 ].…”

Section: Discussionmentioning

confidence: 99%

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Current Salivary Glands Biopsy Techniques: A Comprehensive Review

et al. 2022

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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.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Current Salivary Glands Biopsy Techniques: A Comprehensive Review

et al. 2022

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“…Thus, a simple and effective diagnostic method is crucial and necessary for the differential diagnosis of parotid tumours before surgical treatment. Routine fine needle aspiration is largely dependent on the experience of the clinical operators, as the diagnostic accuracy is sometimes poor due to insufficient or nonrepresentative aspiration (5). In addition, the conventional radiological features of different parotid tumour types may considerably overlap (6).…”

Section: Introductionmentioning

confidence: 99%

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

Liu²,

Liu³

et al. 2023

Front. Oncol.

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ObjectiveThis 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.MethodsIn 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.ResultsThe 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.ConclusionOur 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.

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“…Therefore, accurate preoperative identification is critical to the choice of treatment and prognosis for patients. At present, ultrasound-guided core biopsy (CB) and fine-needle aspiration (FNA) are the major methods for preoperative differentiation of the types of parotid tumors, with the risk of serious surgical complications, such as facial paralysis and tumor implantation metastasis ( 5 , 6 ). Additionally, ultrasound imaging of deep-lobe parotid tumors is occluded by tissue structures such as the mandible, which affects the evaluation of the tumors, thus making sampling difficult and the accuracy of the results largely dependendent on the operator’ experience ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2022

Front. Oncol.

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ObjectiveThis 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).MethodsThis 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.ResultsAmong 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).ConclusionMultiphasic 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.

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Ultrasound-guided core biopsy in the diagnosis of parotid neoplasia: an overview and update with a review of the literature

Cited by 9 publications

References 37 publications

Current Salivary Glands Biopsy Techniques: A Comprehensive Review

Current Salivary Glands Biopsy Techniques: A Comprehensive Review

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

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

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