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. |
ACCESS THE FULL ARTICLE
No SPIE Account? Create one
CITATIONS
Cited by 3 scholarly publications.
Breast
Tumors