Editorial for “Radiomics Based on Multimodal MRI for the Differential Diagnosis of Benign and Malignant Breast Lesions”

Nogueira, Luísa; Nunes, Rita G.

doi:10.1002/jmri.27168

Cited by 2 publications

(2 citation statements)

References 9 publications

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“…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%

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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