Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

Nomogram combining dual-energy computed tomography features and radiomics for differentiating parotid warthin tumor from pleomorphic adenoma: a retrospective study

Introduction

Accurate 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.

Methods

This 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).

Results

IC (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.

Discussion

The nomogram combining DECT features and radiomics offers a promising non-invasive tool for differentiating PA and WT in clinical practice.

Multi-slice computed tomography radiomics combined with serum alpha-L-fucosidase: a potential biomarker for precise identification of pleomorphic adenoma and Warthin tumor

Background

The rising incidence of parotid gland tumors, with a focus on pleomorphic adenomas (PMA) and Warthin tumors (WT), necessitates accurate preoperative distinction due to their treatment variability and PMA's malignant potential. Traditional imaging, while valuable, has limited accuracy. This study employs multi-slice computed tomography (MSCT) radiomics coupled with serum alpha-L-fucosidase (AFU) levels to develop a diagnostic model aimed at elevating clinical discernment and precision therapy delivery.

Methods

Ninety-one patients were randomly assigned to one of two cohorts: training or validation, at a ratio of 7:3 (64 vs. 27). The region of interest (ROI) on each tumor from the collected MSCT images was delineated to extract radiomics features. In the training cohort, the least absolute shrinkage and selection operator (LASSO) regression and 5-fold cross-validation were adopted to screen the extracted features and calculate Rad-score. Four diagnostic models were developed after univariate and multivariate logistic regression analysis of Rad-score and clinical factors. The performance of four models was then evaluated in the validation cohort by the comparison of receiver operating characteristic (ROC) curve and calibration curve to select the best one. Finally, the clinical application value of the best model was assessed via the nomogram and decision curve analysis (DCA) curve.

Results

Multivariate logistic regression analysis revealed serum AFU, Rad-score and gender as independent diagnostic factors for PMA and WT distinguishment. The nomogram combining the three factors had an area under the curve (AUC) of 0.934 [95% confidence interval (CI): 0.863-1.000] and 0.987 (95% CI: 0.956-1.000) in the training and validation cohorts, respectively, with great goodness-of-fit and clinical value.

Conclusions

The optimized nomogram combining MSCT radiomics and AFU improved the accuracy of distinguishing PMA from WT, suggesting its potential for developing new biomarkers.

Performance of radiomics in the differential diagnosis of parotid tumors: a systematic review

Purpose

A systematic review and meta-analysis were conducted to evaluate the diagnostic precision of radiomics in the differential diagnosis of parotid tumors, considering the increasing utilization of radiomics in tumor diagnosis. Although some researchers have attempted to apply radiomics in this context, there is ongoing debate regarding its accuracy.

Methods

Databases of PubMed, Cochrane, EMBASE, and Web of Science up to May 29, 2024 were systematically searched. The quality of included primary studies was assessed using the Radiomics Quality Score (RQS) checklist. The meta-analysis was performed utilizing a bivariate mixed-effects model.

Results

A total of 39 primary studies were incorporated. The machine learning model relying on MRI radiomics for diagnosis malignant tumors of the parotid gland, demonstrated a sensitivity of 0.80 [95% CI: 0.74, 0.86], SROC of 0.89 [95% CI: 0.27-0.99] in the validation set. The machine learning model based on MRI radiomics for diagnosis malignant tumors of the parotid gland, exhibited a sensitivity of 0.83[95% CI: 0.76, 0.88], SROC of 0.89 [95% CI: 0.17-1.00] in the validation set. The models also demonstrated high predictive accuracy for benign lesions.

Conclusion

There is great potential for radiomics-based models to improve the accuracy of diagnosing benign and malignant tumors of the parotid gland. To further enhance this potential, future studies should consider implementing standardized radiomics-based features, adopting more robust feature selection methods, and utilizing advanced model development tools. These measures can significantly improve the diagnostic accuracy of artificial intelligence algorithms in distinguishing between benign and malignant tumors of the parotid gland.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023434931.

Enhanced CT-based texture analysis and radiomics score for differentiation of pleomorphic adenoma, basal cell adenoma, and Warthin tumor of the parotid gland

OBJECTIVE

To evaluate the diagnostic performance of computed tomography (CT) radiomics analysis for differentiating pleomorphic adenoma (PA), Warthin tumor (WT), and basal cell adenoma (BCA).

METHODS

A 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.

RESULTS

Seven, 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.

CONCLUSION

The CT-based radiomics analysis showed favorable predictive performance for differentiating PA, WT, and BCA, thus may be helpful in the clinical decision-making.

Distinguishing Parotid Polymorphic Adenoma and Warthin Tumor Based on the CT Radiomics Nomogram: A Multicenter Study

RATIONALE AND OBJECTIVES

To develop, validate, and test a comprehensive radiomics prediction model to distinguish parotid polymorphic adenomas (PAs) and warthin tumors (WTs) using clinical data and enhanced computed tomography (CT) from a multicenter cohort.

MATERIALS AND METHODS

A total of 267 patients with PAs (n =172) or WTs (n = 95) from two hospitals were randomly divided into training (n =188) and validation (n =79) datasets. Radiomics features were extracted from the enhanced CT (arterial phase) followed by dimensionality reduction. Clinical and CT features were combined to establish a prediction model. A radiomics nomogram was constructed by combining RadScore and clinical factors. Moreover, an independent dataset of 31 patients from a third hospital was employed to test the model. Thus, the performance of the nomogram, radiomics signature, and clinical models was evaluated on the training, validation, and the independent testing datasets. Receiver operating characteristic (ROC) curves were used to compare the performance, and decision curve analysis (DCA) was used to evaluate the clinical effectiveness of the model.

RESULTS

A total of 15 radiomics features were selected from CT data as the imaging markers to generate RadScores, and demographics or clinical data like age, sex, and smoking factors combined with RadScores were used to distinguish PAs and WTs based on multivariate logistic regression analyses. The results showed that radiomics nomograms combining clinical factors and RadScores provided satisfactory predictive values for distinguishing PAs from WTs, with areas under ROC curves (AUC) of 0.979, 0.922, and 0.903 for the training, validation, and the independent testing datasets, respectively. Decision curve analysis revealed that the radiomics nomogram outperformed the clinical factor models in terms of accuracy and effectiveness.

CONCLUSION

CT-based radiomics nomograms combining RadScores and clinical factors can be used to identify PAs and WTs, which may help tumor management by clinicians.

The Role of Preoperative Computed Tomography Radiomics in Distinguishing Benign and Malignant Tumors of the Parotid Gland

Objective

This 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 Methods

A 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.

Results

Analysis 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.

Conclusions

The 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.

Radiomics-based comparison of MRI and CT for differentiating pleomorphic adenomas and Warthin tumors of the parotid gland: a retrospective study

OBJECTIVE

The objective of this study was to compare the diagnostic performance of magnetic resonance imaging (MRI) and computed tomography (CT) in differentiating pleomorphic adenomas from Warthin tumors using radiomics.

STUDY DESIGN

We retrospectively reviewed 626 patients who underwent preoperative MRI or CT for parotid tumor diagnosis. Patient groups were balanced by propensity score matching (PSM) and 123 radiomic features were extracted from tumor images. Radiomic signatures (rad-scores) were generated using a least absolute shrinkage and selection operator logistic regression model. The Canny edge detector was used to define tumor borders (border index). The diagnostic performance of rad-score and border index before and after PSM was evaluated with area under the receiver operating characteristic curve analysis.

RESULTS

For differentiation of pleomorphic adenomas and Warthin tumors, rad-score and border index areas under the curve for MRI after PSM were 0.911 (95% confidence interval [CI], 0.871-0.951) and 0.716 (95% CI, 0.646-0.787), respectively; those for CT were 0.876 (95% CI, 0.829-0.923) and 0.608 (95% CI, 0.527-0.690), respectively. Tumor border index on MRI, but not CT, had superior diagnostic performance (P < .05); MRI- and CT-based rad-scores showed similar performance (P >.05).

CONCLUSIONS

MRI is superior to CT for tumor margin examination; however, the radiomics features of both modalities showed no difference.

Unilateral Deforming Warthin’s Tumor: Case Report and Literature Review

Warthin’s tumor (WT) is the second most common benign tumor of the parotid gland. Located almost exclusively in the parotid gland and presenting a slow growth rate, WT usually does not exceed 4 cm and rarely benefits from early surgical treatment. The aim of this paper is to present a case of giant parotid Warthin’s tumor. The occurrence of large and deforming WT is rare, previous research showed a single similar reported case. The patient’s computed tomography scans showed a solid and cystic 15 × 13 cm2 mass of the parotid gland, without visible signs of invading the adjacent structures. Superficial parotidectomy with tumor excision was performed, with preservation of glandular and facial nerve functions. The paper also presents a brief literature review addressing the main controversies regarding etiopathology, epidemiology, diagnostic methods and treatment options for this parotid gland tumor.