Integration of dynamic contrast-enhanced magnetic resonance imaging and T2-weighted imaging radiomic features by a canonical correlation analysis-based feature fusion method to predict histological grade in ductal breast carcinoma.

Tumour histological grade has prognostic implications in breast cancer. Tumour features in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and T2-weighted (T2W) imaging can provide related and complementary information in the analysis of breast lesions to improve MRI-based histological status prediction in breast cancer. A dataset of 167 patients with invasive ductal carcinoma (IDC) was assembled, consisting of 72 low/intermediate-grade and 95 high-grade cases with preoperative DCE-MRI and T2W images. The data cohort was separated into development (n=111) and validation (n=56) cohorts. Each tumour was segmented in the precontrast and the intermediate and last postcontrast DCE-MR images and was mapped to the tumour in the T2W images. Radiomic features, including texture, morphology, and histogram distribution features in the tumour image, were extracted for those image series. Features from the DCE-MR and T2W images were fused by a canonical correlation analysis (CCA)-based method. The support vector machine (SVM) classifiers were trained and tested on the development and validation cohorts, respectively. SVM-based recursive feature elimination (SVM-RFE) was adopted to identify the optimal features for prediction. The areas under the ROC curves (AUCs) for the T2W images and the DCE-MRI series of precontrast, intermediate and last postcontrast images were 0.750±0.047, 0.749±0.047, and 0.788±0.045, respectively, for the development cohort and 0.715±0.068, 0.704±0.073, and 0.744±0.067, respectively, for the validation cohort. After the CCA-based fusion of features from the DCE-MRI series and T2W images, the AUCs increased to 0.751±0.065, 0.803±0.0600 and 794±0.060 in the validation cohort. Moreover, the method of fusing features between DCE-MRI and T2W images using CCA achieved better performance than the concatenation-based feature fusion or classifier fusion methods. Our results demonstrated that anatomical and functional MR images yield complementary information, and feature fusion of radiomic features by matrix transformation to optimize their correlations produced a classifier with improved performance for predicting the histological grade of IDC.