Decrypting the information captured by MRI-radiomic features in predicting the response to neoadjuvant chemotherapy in breast cancer.

Abstract

MRI-based radiomic models have shown promises in predicting the response to neoadjuvant chemotherapy in breast cancer. However, it is difficult to determine which information from the images contributes the most to the prediction: the distribution of gray-levels, the tumour heterogeneity, the shape of the lesions or the intensities of peritumoural regions. The purpose of this study is to dissociate the different sources of information to improve prediction results. Based on pre-treatment MR images from 103 patients, four types of 3D Volumes Of Interest were defined and arranged in multiple combinations. Combining features extracted from different regions proved to increase prediction performances. Clinical relevance- This study proposes a method based on analyses of MRI tumor heterogeneity, margins and peritumoral regions to improve the prediction of the response to neoadjuvant chemotherapy in breast cancer, which would help personalize patient treatment.