Dataset Information

PRISMA-Extracapsular Dissection Versus Superficial Parotidectomy in Treatment of Benign Parotid Tumors: Evidence From 3194 Patients.

ABSTRACT: Benign parotid tumor is one of the most common neoplasms in head and neck region. Its therapeutic methods have been debatable topics over the past 100 years. Recently, some surgeons suggest that extracapsular dissection (ECD) instead of superficial parotidectomy (SP) for treatment of benign parotid tumor. This study aimed to compare ECD with SP in the treatment of benign parotid tumors by a meta-analysis.We searched Cochrane Library, PubMed, Embase, Ovid, and Web of Science databases on February 14, 2015 for studies that assessed clinical outcomes of SP and ECD as surgical techniques for the management of benign parotid tumors. Outcome data were evaluated by pooled risk ratio (RR) and corresponding 95% confidence interval (CI).After serious scrutiny, a total of 14 cohort studies with 3194 patients were included in this meta-analysis. The pooled RR revealed that there were no significant difference in tumor recurrence rate between ECD and SP (fixed-effect model: RR?=?0.71, 95% CI?=?0.40-1.27, P?=?0.249; random-effect model: RR?=?0.67, 95% CI?=?0.38-1.23, P?=?0.197). However, there were significantly lower incidences of transient facial nerve dysfunction (FND), permanent FND, and Frey's syndrome in patients of ECD group compared with SP group.ECD might be a good choice in treatment of the benign parotid tumor that were mobile, small, located in superficial lobe and without adhesion to facial nerve; ECD should be performed by the experienced surgeons with ability of dissection facial nerve, who should perform SP if tumor is found adhere to facial nerve during an operation; and a multicenter randomized control trial study is necessary to decide the optimal treatment of benign parotid tumor.

SUBMITTER: Xie S

PROVIDER: S-EPMC4602923 | biostudies-literature | 2015 Aug

REPOSITORIES: biostudies-literature

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PRISMA-Extracapsular Dissection Versus Superficial Parotidectomy in Treatment of Benign Parotid Tumors: Evidence From 3194 Patients.

Xie Shang S Wang Kan K Xu Hui H Hua Rui-Xi RX Li Tian-Zhu TZ Shan Xiao-Feng XF Cai Zhi-Gang ZG

Medicine 20150801 34

Benign parotid tumor is one of the most common neoplasms in head and neck region. Its therapeutic methods have been debatable topics over the past 100 years. Recently, some surgeons suggest that extracapsular dissection (ECD) instead of superficial parotidectomy (SP) for treatment of benign parotid tumor. This study aimed to compare ECD with SP in the treatment of benign parotid tumors by a meta-analysis.We searched Cochrane Library, PubMed, Embase, Ovid, and Web of Science databases on February ...[more]

PMID: 26313768

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Project description: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.

Project description:ObjectiveThis study aimed to develop and validate an integrated prediction model based on clinicoradiological data and computed tomography (CT)-radiomics for differentiating between benign and malignant parotid gland (PG) tumors via multicentre cohorts.Materials and methodsA cohort of 87 PG tumor patients from hospital #1 who were diagnosed between January 2017 and January 2020 were used for prediction model training. A total of 378 radiomic features were extracted from a single tumor region of interest (ROI) of each patient on each phase of CT images. Imaging features were extracted from plain CT and contrast-enhanced CT (CECT) images. After dimensionality reduction, a radiomics signature was constructed. A combination model was constructed by incorporating the rad-score and CT radiological features. An independent group of 38 patients from hospital #2 was used to validate the prediction models. The model performances were evaluated by receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the models. The radiomics signature model was constructed and the rad-score was calculated based on selected imaging features from plain CT and CECT images.ResultsAnalysis of variance and multivariable logistic regression analysis showed that location, lymph node metastases, and rad-score were independent predictors of tumor malignant status. The ROC curves showed that the accuracy of the support vector machine (SVM)-based prediction model, radiomics signature, location and lymph node status in the training set was 0.854, 0.772, 0.679, and 0.632, respectively; specificity was 0.869, 0.878, 0.734, and 0.773; and sensitivity was 0.731, 0.808, 0.723, and 0.742. In the test set, the accuracy was 0.835, 0.771, 0.653, and 0.608, respectively; the specificity was 0.741, 0.889, 0.852, and 0.812; and the sensitivity was 0.818, 0.790, 0.731, and 0.716.ConclusionsThe combination model based on the radiomics signature and CT radiological features is capable of evaluating the malignancy of PG tumors and can help clinicians guide clinical tumor management.

Dataset Information

PRISMA-Extracapsular Dissection Versus Superficial Parotidectomy in Treatment of Benign Parotid Tumors: Evidence From 3194 Patients.

Publications

PRISMA-Extracapsular Dissection Versus Superficial Parotidectomy in Treatment of Benign Parotid Tumors: Evidence From 3194 Patients.

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