Different MRI-based radiomics models for differentiating misdiagnosed or ambiguous pleomorphic adenoma and Warthin tumor of the parotid gland: a multicenter study
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SUBMITTER: Yang J
PROVIDER: S-EPMC11208325 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:BackgroundAccurately distinguishing between pleomorphic adenoma (PA) and Warthin tumor (WT) is beneficial for their respective management. Preoperative magnetic resonance imaging (MRI) can provide valuable information due to its excellent soft tissue contrast. This study explored the value of semiquantitative contrast-enhanced MRI parameters in the differential diagnosis of PA and WT.MethodsData from 106 patients, 62 with PA and 44 with WT (confirmed by histopathology) were retrospectively and consecutively analyzed. The tumor-to-spinal cord contrast ratios (TSc-CR) based on the mean, maximum, and minimum signal intensity (T1-mean TSc-CR, T1-max TSc-CR, and T1-min TSc-CR, respectively) in the early and delayed phases were calculated on contrast-enhanced T1-weighted images as semiquantitative parameters, and then compared between PA and WT. Receiver operating characteristic (ROC) curve analysis and areas under the curve (AUCs) were used to determine the performance of these parameters in the differential diagnosis of PA from WT.ResultsExcept T1-min TSc-CR in the early phase, all semiquantitative MRI parameters differed significantly between PA and WT (all P<0.05). T1-max TSc-CR showed higher sensitivity {70.45% [95% confidence interval (CI): 0.548-0.832]} and specificity [70.97% (95% CI: 0.581-0.818)] and had a higher AUC [0.707 (95% CI: 0.610-0.791)] in the early phase when using a cutoff value of 1.89. T1-max TSc-CR showed higher sensitivity [88.64% (95% CI: 0.754-0.962)], specificity [72.58% (95% CI: 0.598-0.831)], and AUC [0.854 (95% CI: 0.772-0.915)] in the delayed phase when using a cutoff value of 2.33. The sensitivity, specificity, and AUC were improved to 90.91% (95% CI: 0.783-0.975), 93.55% (95% CI: 0.843-0.982), and 0.960 (95% CI: 0.903-0.988), respectively, after combination of all semiquantitative parameters in the early and delayed phases. The two radiologists had excellent interobserver agreement on TSc-CRs [all interclass correlation coefficient (ICC) >0.75].ConclusionsSemiquantitative parameters using TSc-CR are valuable in distinguishing PA from WT, and a combination of these parameters can improve the differential diagnostic efficiency.
| S-EPMC10498251 | biostudies-literature
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.
| S-EPMC11914106 | biostudies-literature
Project description:ObjectiveTo evaluate the diagnostic performance of computed tomography (CT) radiomics analysis for differentiating pleomorphic adenoma (PA), Warthin tumor (WT), and basal cell adenoma (BCA).MethodsA total of 189 patients with PA (n = 112), WT (n = 53) and BCA (n = 24) were divided into a training set (n = 133) and a test set (n = 56). The radiomics features were extracted from plain CT and contrast-enhanced CT images. After dimensionality reduction, plain CT, multiphase-enhanced CT, integrated radiomics signature models and radiomics score (Rad-score) were established and calculated. The receiver operating characteristic (ROC) curve analysis was taken for the assessment of the model performance, and then comparison was conducted among these models. Decision curve analysis (DCA) was adopted to assess the clinical benefits of the models. Diagnostic performances including sensitivity, specificity, and accuracy of the radiologists were evaluated.ResultsSeven, nine, fourteen, and fourteen optimal features were used to constructed plain scan, arterial phase, venous phase, and integrated radiomics signature models, respectively. ROC analysis showed these four models were able to differentiate PA from BCA and WT, with the area under the ROC curve (AUC) values of 0.79, 0.90, 0.87, and 0.94 in the training set, and 0.79, 0.89, 0.86, and 0.94 in the test set, respectively. The integrated model had better diagnostic performance than single-phase radiomics model, but it had similar diagnostic performance to that of the radiomics model based on the arterial phase (p > 0.05). The sensitivity, specificity, and accuracy in the diagnosis of PA were 0.86, 0.46, and 0.70 for the non-subspecialized radiologist and 0.88, 0.77, and 0.84 for the subspecialized radiologist, respectively. Six venous phase parameters were finally selected in differentiating WT from BCA. The predictive effect of the model was favorable, with AUC value of 0.95, sensitivity of 0.96, specificity of 0.83, and accuracy of 0.92. The sensitivity, specificity, and accuracy in the diagnosis between WT and BCA were 0.26, 0.87, and 0.45 for the non-subspecialized radiologist and 0.85, 0.58, and 0.77 for the subspecialized radiologist, respectively.ConclusionThe CT-based radiomics analysis showed favorable predictive performance for differentiating PA, WT, and BCA, thus may be helpful in the clinical decision-making.
| S-EPMC9974237 | biostudies-literature
Project description:BackgroundDistinguishing 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.MethodsThe 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.ResultsThe 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.ConclusionsThe T1-2WI radiomic model in our study is complementary to the current knowledge of differential diagnosis for PPA and PA.
| S-EPMC7981906 | biostudies-literature
Project description:The accessory parotid gland (APG) is a distinct salivary tissue in the cheek that is located on the masseter muscle and is separate from the main parotid gland. APG tumors (APGTs) are rare. Due to aesthetic reasons and the risk of both malignancy and recurrence, APGTs are best treated with surgical resection. The resection should be conducted carefully due to potential complications such as Stensen duct and facial nerve injuries. Notably, plastic surgeons rarely see APGT cases because they are classified as parotid gland tumors and are thus mostly treated by otorhinolaryngologists. Nonetheless, because they are subcutaneous tumors in the cheek, patients with APGTs do occasionally visit the plastic surgery outpatient clinic. We report a case of APGT in a 59-year-old woman. She presented in our hospital with a rigid mass in the right cheek that was difficult to diagnose on the basis of clinical findings. After magnetic resonance imaging, APGT was considered along with several other possibilities. However, it was only diagnosed after histopathology on the resected tissues. Thus, plastic surgeons treating subcutaneous tumors of the cheek should consider APGT in their differential diagnosis and seek an accurate preoperative diagnosis, because this will help avoid postoperative complications.
| S-EPMC11446589 | biostudies-literature
Project description:Incomplete excision of pleomorphic adenoma (PA) may result in recurrent pleomorphic adenoma (RPA). Furthermore, long-term neglected PA may become carcinoma ex pleomorphic adenoma (CXPA). In the present study, the relationships between mast cell-derived chymase and these tumors were examined. The tumor tissues of PA consisted of either or both glandular and fibrotic structures. Histological features of RPA were almost similar to those of PA, except that they showed multinodular structures. CXPA is composed of a mixture of PA and carcinoma. The main stromal cells in PA were myofibroblasts, whereas fibroblasts constituted the main cellular portion in the stromal tissue of RPA. Cancer-associated fibroblasts (CAFs) were present abundantly in CXPA. With increased VEGF expression, neovascularization tended to increase in RPA or CXPA. Compared with PA, chymase-positive mast cells, as well as chymase gene expression, were increased in the tumor tissues from patients with RPA or CXPA. SCF, TGFβ1, and PCNA-positive staining was widely observed in these tumor tissues. The above results suggest that mast cell-derived chymase through its direct or cooperative effects with other mediators may participate in the pathophysiology of RPA and CXPA.
| S-EPMC8657626 | biostudies-literature
Project description:ObjectiveCapsular characteristics of pleomorphic adenoma (PA) has various forms. Patients without complete capsule has a higher risk of recurrence than patients with complete capsule. We aimed to develop and validate CT-based intratumoral and peritumoral radiomics models to make a differential diagnosis between parotid PA with and without complete capsule.MethodsData of 260 patients (166 patients with PA from institution 1 (training set) and 94 patients (test set) from institution 2) were retrospectively analyzed. Three Volume of interest (VOIs) were defined in the CT images of each patient: tumor volume of interest (VOItumor), VOIperitumor, and VOIintra-plus peritumor. Radiomics features were extracted from each VOI and used to train nine different machine learning algorithms. Model performance was evaluated using receiver operating characteristic (ROC) curves and the area under the curve (AUC).ResultsThe results showed that the radiomics models based on features from VOIintra-plus peritumor achieved higher AUCs compared to models based on features from VOItumor. The best performing model was Linear discriminant analysis, which achieved an AUC of 0.86 in the tenfold cross-validation and 0.869 in the test set. The model was based on 15 features, including shape-based features and texture features.ConclusionsWe demonstrated the feasibility of combining artificial intelligence with CT-based peritumoral radiomics features can be used to accurately predict capsular characteristics of parotid PA. This may assist in clinical decision-making by preoperative identification of capsular characteristics of parotid PA.
| S-EPMC10203084 | biostudies-literature
Project description:IntroductionPleomorphic adenoma is the most common benign neoplasm of the salivary glands. Extensive lipomatous involvement of the tumor is, however, a very rare finding.Case reportHerein, a rare case of lipomatous pleomorphic adenoma arising in the parotid gland of a 14-year-old Japanese woman is presented.ConclusionThis is the sixth case of lipomatous pleomorphic adenoma in the English literature. Recognition of this rare subtype of pleomorphic adenoma is important for clinical diagnosis and management. On CT scan, it may not be detected possibly due to the extensive fatty component.
| S-EPMC2710317 | biostudies-literature
Project description: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 materialsA 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.ResultsThe 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.ConclusionsThe MRI-based radiomics nomogram had good performance in distinguishing parotid WT from PMA, which could optimize clinical decision-making.
| S-EPMC8474129 | biostudies-literature
Project description: Not available
| S-EPMC10447796 | biostudies-literature