MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland

Zheng, Ying-Mei; Li, Jian; Liu, Song; Cui, Jiufa; Zhan, Jinfeng; Pang, Jing; Zhou, Ruizhi; Li, Xiaoli; Dong, Cheng

doi:10.1007/s00330-020-07483-4

Cited by 57 publications

(46 citation statements)

References 36 publications

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“…Recent literature has shown a growing interest in the clinical applicability of radiomics for the parotid tumor characterization ( 12 , 25 , 26 ), thanks to significant improvements in diagnostic accuracy obtained with a multiparametric approach to quantitative MRI ( 2 , 4 , 6 , 30 , 31 , 36 , 37 ). However, no consensus exists regarding the most appropriate sequences to consider for the extraction of radiomics features and only a few studies used standardized software, previously validated according to the updated IBSI guidelines ( 16 ).…”

Section: Discussionmentioning

confidence: 99%

“…Some recent studies report that texture analysis of MRI may provide a useful and objective description of signal patterns, which contribute to accurate diagnosis between tumors that look alike by a visual inspection (11,13). The value of a computer-assisted discrimination of benign and malignant tumors has been explored in various organs (19)(20)(21), but only a few studies have assessed the contribution in parotid masses (12,(22)(23)(24)(25)(26).…”

Section: Discussionmentioning

confidence: 99%

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MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

et al. 2021

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BackgroundThe differentiation between benign and malignant parotid lesions is crucial to defining the treatment plan, which highly depends on the tumor histology. We aimed to evaluate the role of MRI-based radiomics using both T2-weighted (T2-w) images and Apparent Diffusion Coefficient (ADC) maps in the differentiation of parotid lesions, in order to develop predictive models with an external validation cohort.Materials and MethodsA sample of 69 untreated parotid lesions was evaluated retrospectively, including 37 benign (of which 13 were Warthin’s tumors) and 32 malignant tumors. The patient population was divided into three groups: benign lesions (24 cases), Warthin’s lesions (13 cases), and malignant lesions (32 cases), which were compared in pairs. First- and second-order features were derived for each lesion. Margins and contrast enhancement patterns (CE) were qualitatively assessed. The model with the final feature set was achieved using the support vector machine binary classification algorithm.ResultsModels for discriminating between Warthin’s and malignant tumors, benign and Warthin’s tumors and benign and malignant tumors had an accuracy of 86.7%, 91.9% and 80.4%, respectively. After the feature selection process, four parameters for each model were used, including histogram-based features from ADC and T2-w images, shape-based features and types of margins and/or CE. Comparable accuracies were obtained after validation with the external cohort.ConclusionsRadiomic analysis of ADC, T2-w images, and qualitative scores evaluating margins and CE allowed us to obtain good to excellent diagnostic accuracies in differentiating parotid lesions, which were confirmed with an external validation cohort.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

et al. 2021

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“… 9 , 15 In addition, the procedure involved with FNAC creates a risk of facial nerve palsy and also has the potential to cause tumor cells spread along the needle path. 9 , 16 , 17 Compared with ultrasound and computed tomography, magnetic resonance (MR) imaging provides higher soft tissue resolution and multiparameter imaging and, in recent years, has been more commonly used for parotid tumor diagnosis. Conventional MR imaging, and advanced methods such as dynamic contrast-enhanced perfusion-weighted imaging (DCE-PWI) and diffusion-weighted imaging (DWI) have been reported in previous studies.…”

Section: Introductionmentioning

confidence: 99%

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

Wei

Shao

Tian

et al. 2021

CMAR

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To investigate the value of the signal intensity on T2-weighted magnetic resonance (MR) imaging using quantitative analysis in the differentiation of parotid tumors. Materials and Methods: MR data of 80 pleomorphic adenomas (PAs), 68 Warthin tumors (WTs), and 34 malignant tumors (MTs) confirmed by surgery and histology were retrospectively analyzed. The signal intensities of tumor, normal parotid gland, spinal cord, and buccal subcutaneous fat were measured, and the signal intensity ratios (SIRs) between the tumor and the three references were calculated. Receiver operating characteristic curve was used to determine the optimal threshold and diagnostic efficiency of SIR for differentiating PAs, WTs, and MTs. Results: The area under the curve (AUC) of tumor to parotid gland SIR (SIR P ), tumor to spinal cord SIR (SIR C ), and tumor to buccal subcutaneous fat SIR (SIR F ) for differentiating PAs and WTs was 0.922, 0.918, and 0.934, respectively. The sensitivity and specificity at an optimal SIR threshold were 86.3% and 91.2%, 80.0% and 97.1%, and 85.0% and 94.1%, respectively. The AUC of SIR P , SIR C , and SIR F for distinguishing PAs from MTs was 0.793, 0.802, and 0.774, respectively. The sensitivity and specificity at an optimal SIR threshold was 86.3% and 61.8%, 80.0% and 73.5%, and 82.5% and 73.5%, respectively. The AUC of SIR P , SIR C , and SIR F for distinguishing WTs from MTs was 0.716, 0.709, and 0.759, respectively. The sensitivity and specificity at an optimal SIR threshold were 61.8% and 82.4%, 55.9% and 82.4%, and 64.7% and 86.8%, respectively. Conclusion: SIR P , SIR C , and SIR F on T2-weighted MR images had high diagnostic efficiency for differentiating between PAs and WTs, while SIR P and SIR C for differentiating between PAs and MTs, and SIR F for differentiating between WTs and MTs had relatively high diagnostic efficiency.

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“…Radiomics is an emerging approach that allows the extraction of quantitative data from medical images (including MRI) which, correlated with genomic and clinical parameters, are able to improve diagnostic accuracy and aid individualized patient treatment and outcome prediction [10] . Over the last years, some articles have been published trying to differentiate benign from malignant salivary gland tumors, but only some of them have addressed the discrimination of WT from PA, yielding mixed results [11] , [12] , [13] , [14] , [15] , [16] , [17] .…”

Section: Introductionmentioning

confidence: 99%

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

Faggioni

Gabelloni

Vietro

et al. 2022

European Journal of Radiology Open

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MRI-Based radiomics nomogram for differentiation of benign and malignant lesions of the parotid gland

Cited by 57 publications

References 36 publications

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

MRI-Based Radiomics to Differentiate between Benign and Malignant Parotid Tumors With External Validation

Quantitative Analysis and Pathological Basis of Signal Intensity on T2-Weighted MR Images in Benign and Malignant Parotid Tumors

Usefulness of MRI-based radiomic features for distinguishing Warthin tumor from pleomorphic adenoma: performance assessment using T2-weighted and post-contrast T1-weighted MR images

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