Dataset Information

Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland.

ABSTRACT:

Objective：

Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA.

Methods and materials

A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (n = 75; WT = 34, PMA = 41) and external test set (n = 52; WT = 24, PMA = 28). Radiomics features were extracted from axial T1WI and fs-T2WI images. A radiomics signature was constructed, and a radiomics score (Rad-score) was calculated. A clinical factors model was built using demographics and MRI findings. A radiomics nomogram combining the independent clinical factors and Rad-score was constructed. The receiver operating characteristic analysis was used to assess the performance levels of the nomogram, radiomics signature and clinical model.

Results

The radiomics nomogram incorporating the age and radiomics signature showed favourable predictive value for differentiating parotid WT from PMA, with AUCs of 0.953 and 0.918 for the training set and test set, respectively.

Conclusions

The MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.

SUBMITTER: Zheng YM

PROVIDER: S-EPMC8474129 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Publications

Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland.

Zheng Ying-Mei YM Chen Jiao J Xu Qi Q Zhao Wen-Hui WH Wang Xin-Feng XF Yuan Ming-Gang MG Liu Zong-Jing ZJ Wu Zeng-Jie ZJ Dong Cheng C

Dento maxillo facial radiology 20210505 7

<h4>Objective：</h4>Preoperative differentiation between parotid Warthin's tumor (WT) and pleomorphic adenoma (PMA) is crucial for treatment decisions. The purpose of this study was to establish and validate an MRI-based radiomics nomogram for preoperative differentiation between WT and PMA.<h4>Methods and materials</h4>A total of 127 patients with histological diagnosis of WT or PMA from two clinical centres were enrolled in training set (<i>n</i> = 75; WT = 34, PMA = 41) and external test set ( ...[more]

PMID: 33950705

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Project description:ImportanceSurgery is the mainstay of treatment for pleomorphic adenomas (PAs) of the parotid to prevent further growth and potential future malignant transformation. While historical case series have reported transformation rates as high as 10%, there is a lack of contemporary methodologically sound data.ObjectiveTo examine the rate of carcinoma ex pleomorphic adenoma (CXPA) detection in untreated PAs and investigate factors associated with malignant neoplasm.Design, setting, and participantsThis cohort study reviewed all cases of primary PAs managed at a quaternary referral center between December 1990 and January 2015. Patients whose clinical presentation was compatible with a primary benign PA and whose history indicated tumor duration of over 1 year were included. Data were analyzed from January to April 2023.ExposureUntreated PA.Main outcomes and measuresRate of CXPA detection among untreated PAs and association of tumor duration with rates of CXPA detection. Pathology slides of patients who underwent surgery were reviewed by a single expert pathologist for the presence of CXPA. Univariable logistic regression was performed to evaluate possible factors associated with CXPA.ResultsA total of 260 patients (median age, 47 years [IQR, 38-60 years]; 174 [66.9%] female) had a median tumor duration of 3.2 years (range, 1-30 years; mean [SD], 5.7 [5.5] years). Patients were divided into 4 groups by tumor duration: 1 to 4 years (158 [60.7%]), 5 to 9 years (47 [18.1%]), 10 to 14 years (27 [10.4%]), and 15 to 30 years (28 [10.8%]). In 156 of 170 patients who underwent preoperative fine-needle aspiration (91.8%), a benign tumor was diagnosed; 5 of these patients (3.2%; 95% CI, 1.4%-7.3%) were later diagnosed with CXPA on pathology after eventual excision, and the rate of high grade CXPA was 1.3%. None of the patients had permanent facial nerve paralysis. Tumor size at presentation (odds ratio [OR], 1.66; 95% CI, 1.22-2.24) and incremental (per year) increase in age (OR, 1.04; 95% CI, 1.01-1.08) were found to be associated with CXPA, whereas tumor duration was not (OR, 1.00; 95% CI, 1.00-1.01).Conclusions and relevanceIn this study, the rate of malignant neoplasm detection among initially untreated PA was 3.2%. The results suggest that tumor size and older age are associated with the development of CXPA, while tumor duration is not. Observation of PA for longer periods was not associated with serious permanent complications.

Project description:ObjectivesTo differentiate parotid pleomorphic adenoma (PA) from adenolymphoma (AL) using radiomics of grayscale ultrasonography in combination with clinical features.MethodsThis 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.ResultsTo 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.ConclusionA nomogram based on grayscale ultrasonic radiomics and clinical features served as a non-invasive tool capable of differentiating PA and AL.

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.

Project description:IntroductionAccurate differentiation between pleomorphic adenomas (PA) and Warthin tumors (WT) in the parotid gland is challenging owing to overlapping imaging features. This study aimed to evaluate a nomogram combining dual-energy computed tomography (DECT) quantitative parameters and radiomics to enhance diagnostic precision.MethodsThis retrospective study included 120 patients with pathologically confirmed PA or WT, randomly divided into training and test sets (7:3). DECT features, including tumor CT values from 70 keV virtual monochromatic images (VMIs), iodine concentration (IC), and normalized IC (NIC), were analyzed. Independent predictors were identified via logistic regression. Radiomic features were extracted from segmented regions of interest and filtered using the K-best and least absolute shrinkage and selection operator. Radiomic models based on 70 keV VMIs and material decomposition images were developed using logistic regression (LR), support vector machine (SVM), and random forest (RF). The best-performing radiomics model was combined with independent DECT predictors to construct a model and nomogram. Model performance was assessed using ROC curves, calibration curves, and decision curve analysis (DCA).ResultsIC (venous phase), NIC (arterial phase), and NIC (venous phase) were independent DECT predictors. The DECT feature model achieved AUCs of 0.842 and 0.853 in the training and test sets, respectively, outperforming the traditional radiomics model (AUCs 0.836 and 0.834, respectively). The DECT radiomics model using arterial phase water-based images with LR showed improved performance (AUCs 0.883 and 0.925). The combined model demonstrated the highest discrimination power, with AUCs of 0.910 and 0.947. The combined model outperformed the DECT features and conventional radiomics models, with AUCs of 0.910 and 0.947, respectively (P<0.05). While the difference in AUC between the combined model and the DECT radiomics model was not statistically significant (P>0.05), it showed higher specificity, accuracy, and precision. DCA found that the nomogram gave the greatest net therapeutic effect across a broad range of threshold probabilities.DiscussionThe nomogram combining DECT features and radiomics offers a promising non-invasive tool for differentiating PA and WT in clinical practice.

Dataset Information

Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland.

Objective：

Methods and materials

Results

Conclusions

Publications

Development and validation of an MRI-based radiomics nomogram for distinguishing Warthin's tumour from pleomorphic adenomas of the parotid gland.

Similar Datasets

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