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The performance of the classification models was evaluated with receiver operating characteristic (ROC) curves. Two machine learning methods, decision tree (DT) and support vector machine (SVM), were used to establish classification models with a 10-fold cross-validation method. The Kolmogorov-Smirnov test was first performed on texture features selected by LASSO to test whether the samples followed a normal distribution.
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The least absolute shrinkage and selection operator (LASSO) method was used to select the optimal feature subset in the training cohort. Patients were divided into training (n = 209) and validation (n = 90) cohorts by different MRI scanners. A total of 467 texture features were extracted from the whole tumor area and the three subregions, respectively. According to the time to peak of the contrast agent, the whole tumor area was partitioned into three subregions: early, moderate, and late. The whole tumor area was semi-automatically segmented on the basis of subtraction images of DCE-MRI in Matlab 2018b. Materials and Methods: A total of 299 patients with pathologically verified breast tumors who underwent breast DCE-MRI examination were enrolled in this study, including 124 benign cases and 175 malignant cases. Purpose: To evaluate the potential of the texture features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) intratumoral subregions to distinguish benign from malignant breast tumors. 2Department of Radiology, Shengjing Hospital of China Medical University, Shenyang, China.1School of Medicine and Bioinformatics Engineering, Northeastern University, Shenyang, China.Bin Zhang 1, Lirong Song 2 and Jiandong Yin 2*