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

Abstract

BackgroundMRI‐based radiomics has been used to diagnose breast lesions; however, little research combining quantitative pharmacokinetic parameters of dynamic contrast‐enhanced MRI (DCE‐MRI) and diffusion kurtosis imaging (DKI) exists.PurposeTo develop and validate a multimodal MRI‐based radiomics model for the differential diagnosis of benign and malignant breast lesions and analyze the discriminative abilities of different MR sequences.Study TypeRetrospective.PopulationIn all, 207 female patients with 207 histopathology‐confirmed breast lesions (95 benign and 112 malignant) were included in the study. Then 159 patients were assigned to the training group, and 48 patients comprised the validation group.Field Strength/SequenceT2‐weighted (T2W), T1‐weighted (T1W), diffusion‐weighted MR imaging (b‐values = 0, 500, 800, and 2000 seconds/mm2) and quantitative DCE‐MRI were performed on a 3.0T MR scanner.AssessmentRadiomics features were extracted from T2WI, T1WI, DKI, apparent diffusion coefficient (ADC) maps, and DCE pharmacokinetic parameter maps in the training set. Models based on each sequence or combinations of sequences were built using a support vector machine (SVM) classifier and used to differentiate benign and malignant breast lesions in the validation set.Statistical TestsOptimal feature selection was performed by Spearman's rank correlation coefficients and the least absolute shrinkage and selection operator algorithm (LASSO). Receiver operating characteristic (ROC) curves were used to assess the diagnostic performance of the radiomics models in the validation set.ResultsThe area under the ROC curve (AUC) of the optimal radiomics model, including T2WI, DKI, and quantitative DCE‐MRI parameter maps was 0.921, with an accuracy of 0.833. The AUCs of the models based on T1WI, T2WI, ADC map, DKI, and DCE pharmacokinetic parameter maps were 0.730, 0.791, 0.770, 0.788, and 0.836, respectively.Data ConclusionThe model based on radiomics features from T2WI, DKI, and quantitative DCE pharmacokinetic parameter maps has a high discriminatory ability for benign and malignant breast lesions.Level of Evidence3Technical Efficacy Stage2 J. Magn. Reson. Imaging 2020;52:596–607.

Radiomics Nomogram for Identifying Sub-1 cm Benign and Malignant Thyroid Lesions

“…To construct the radiomics signature, a LASSO logistic regression model was used to reduce the radiomics features. This method is widely used in discriminating benign and malignant lesions (37,40,41), and it is designed to avoid overfitting (42,43). In our study, 13 radiomics features were finally selected as the most closely related features to the sub-1 cm thyroid lesion status, including 1 shape feature, 6 first order statistics features, 2 gray level dependence matrix (GLDM)-derived texture features, 2 gray level run-length matrix (GLRLM)-derived texture features, and 2 gray level size zone matrix (GLSZM)-derived texture features.…”

Radiomics Nomograms Based on Multi-Parametric MRI for Preoperative Differential Diagnosis of Malignant and Benign Sinonasal Tumors: A Two-Centre Study

“…Previous studies showed that radiomics had a strong ability to identify malignant from benign tumors. Zhang et al (34) reported that the model based on radiomics features from TWI, DKI, and quantitative DCE pharmacokinetic parameter maps was a good tool to differentiate malignant and benign breast lesions, with an AUC of 0.92 in the test set. Wang et al (35) reported that the radiomics nomogram can be used to classify between malignant and benign soft-tissue masses in the extremities with an AUC of 0.94 in the test set.…”

“…Zhang et al. ( 34 ) reported that the model based on radiomics features from TWI, DKI, and quantitative DCE pharmacokinetic parameter maps was a good tool to differentiate malignant and benign breast lesions, with an AUC of 0.92 in the test set. Wang et al.…”

AI-enhanced Synchronized Multiparametric 18F-FDG PET/MRI for Accurate Breast Cancer Diagnosis

“…Several studies have been published on the use of AI applied to MRI for breast cancer diagnosis, mainly aiming at increasing its relatively low speci city, compared to the high sensitivity, with accuracy values ranging from 0.728 to 0.920 [33][34][35][36][37]. Similar to our work, the group of Zhang et al also explored the possibility to improve the accuracy of the ML classi er combining radiomics features extracted from both morphological and functional contrast-enhanced and diffusion kurtosis MRI images of 207 histologically proven breast lesions.…”

“…Similar to our work, the group of Zhang et al also explored the possibility to improve the accuracy of the ML classi er combining radiomics features extracted from both morphological and functional contrast-enhanced and diffusion kurtosis MRI images of 207 histologically proven breast lesions. They found that the model based on radiomics features from T2-w, DKI and quantitative DCE pharmacokinetic parameter maps had the best discriminatory ability for benign and malignant breast lesions (AUC = 0.921) [33]. Radiomics coupled to machine learning analysis applied to DCE-MRI, including both radiomics features and clinical data, also proved to be accurate in the characterization of < 1 cm breast lesions in ninety-six high-risk BRCA mutation carriers, with a diagnostic accuracy of 81.5%, signi cantly higher than qualitative morphological assessment with BI-RADS classi cation (AUC 53.4%) [37].…”