Deep Learning approach predicting breast tumor response to neoadjuvant treatment using DCE-MRI volumes acquired before and after chemotherapy

Purpose: In breast cancer medical follow-up, due to the lack of specialized aided diagnosis tools, many breast cancer patients may continue to receive chemotherapy even if they do not respond to the treatment. In this work, we propose a new approach for early prediction of breast cancer response to chemotherapy from two follow-up DCE-MRI exams. We present a method that takes advantage of a deep convolutional neural network (CNN) model to classify patients who are responsive or non-responsive to chemotherapy. Methods and material: To provide an early prediction of breast cancer response to chemotherapy, we used a two branch Convolution Neural Network (CNN) architecture, taking as inputs two breast tumor MRI slices acquired before and after the first round of chemotherapy. We trained our model on a 693 x 2 ROIs belonging to 42 patients with local breast cancer. Image pretreatment, volumetric image registration and tumor segmentation were applied to MRI exams as a preprocessing step. As a ground truth, we used the anapathological standard reference provided of each patient. Results: Within 80 training epochs, an accuracy of 92.72% was obtained using 20% as validation data. The Area Under the Curve (AUC) was 0.96. Conclusion: In this paper, it was demonstrated that deep CNNs models can be used to solve breast cancer follow-up related problems. Therefore, the model obtained in this work can be exploited in future clinical applications after improving its efficiency with the used data.