Discrimination of malignant and benign breast masses using automatic segmentation and features extracted from dynamic contrast‑enhanced and diffusion‑weighted MRI

Abstract

Magnetic resonance imaging exhibits high sensitivity but low specificity for breast cancer. The present study aimed to investigate whether combining morphology, texture features and kinetic features with diffusion-weighted imaging using quantitative analysis improves the accuracy of discriminating malignant from benign breast masses. In total, 104 and 171 malignant lesions in 205 women were included. Additionally, 13 texture and 11 morphology features were computed from each lesion using a semi-automated segmentation method. To increase prediction accuracy, a newly designed classification model, difference-weighted local hyperplane, was used for statistical analysis of the combined effects of the features for predicting lesion type. The mean apparent diffusion coefficient (ADC) value for each lesion was calculated. Diagnostic performances of morphology and texture features, kinetic features and ADC alone and the combination of them were evaluated using receiver operating characteristics analysis. Malignant lesions had lower mean ADCs than benign lesions. By using 10-fold cross validation scheme, combined morphological and kinetic features achieved a diagnostic average accuracy of 0.87. Adding an ADC threshold of 1.37×10−3 mm2/sec increased the overall averaged accuracy to 0.90. A multivariate model combining ADC values with 6 morphological and kinetic parameters best discriminated malignant from benign lesions. Incorporating morphology and texture features, kinetic features and ADC into a multivariable diagnostic model improves the discriminatory power of breast lesions.

Machine learning in breast MRI

“…Other than DCE‐MRI, MR sequences such as nonenhanced T 1 ‐weighted images, diffusion‐weighted images, and T 2 ‐weighted images have been used to improve lesion characterization. Investigators have also developed multiparametric models combining diffusion‐weighted imaging with DCE‐MR for lesion discrimination, with accuracies up to 0.93. Specific features that were significantly different between benign and malignant lesions included entropy and signal enhancement ratio .…”

“…Specific features that were significantly different between benign and malignant lesions included entropy and signal enhancement ratio . Jiang et al found that adding additional parameters to a multiparametric model improved accuracy of discrimination between benign and malignant lesions. For example, adding kinetic features to a model using shape and texture features from DCE‐MR improved accuracy, and adding an ADC threshold to this model improved accuracy again.…”

“…The classifiers used in these studies include linear discriminant analysis, Bayesian, difference‐weighted local hyperplane, and SVM . Cai et al evaluated four machine‐learning models and found that SVM was the best classifier among the machine‐learning models (less accurate algorithms were Naïve Bayes, k‐nearest neighbors, and logistic regression).…”

Breast MRI texture analysis for prediction of BRCA-associated genetic risk

“…Radiomics allowed the association of image traits with phenotypes, tissue characteristics, genomic signatures and protein expression patterns of a tumor [26][27][28][29][30]. To date, analysis of breast MRI textural features has been applied for the discrimination between malignant and benign lesions [31][32][33][34][35], correlation with tumor histological and molecular subtypes [36][37][38][39][40] and even prediction of chemotherapy response [41].…”

Editorial on “Diagnosis of Benign and Malignant Breast Lesions on DCE‐MRI by Using Radiomics and Deep Learning With Consideration of Peritumor Tissue”

“…Diffusion‐weighted imaging has also been found to significantly improve lesion characterization . The addition of diffusion‐weighted imaging to multiparametric deep‐learning models has already been shown to improve accuracy of lesion characterization …”