Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response
- Resource Type
- Conference
- Authors
- Machireddy, Archana; Thibault, Guillaume; Huang, Wei; Song, Xubo
- Source
- 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) IEEE Engineering in Medicine and Biology Society (EMBC), 2018 40th Annual International Conference of the. :682-685 Jul, 2018
- Subject
- Bioengineering
Tumors
Feature extraction
Breast cancer
Surgery
Chemotherapy
Imaging
Convolutional neural networks
- Language
- ISSN
- 1558-4615
Positive response to neoadjuvant chemotherapy (NACT) has been correlated to better long-term outcomes in breast cancer treatment. Early prediction of response to NACT can help modify the regimen for non-responding patients, sparing them of potential toxicities of ineffective therapies. It has been observed that tumor functions such as vascularization and vascular permeability change even before noticeable changes occur in the tumor size in response to the treatment. Therefore, it is essential to have reliable imaging based features to measure these changes. Texture analysis on parametric maps from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) has shown to be a good predictor of breast cancer response to NACT at an early stage. But hand crafted texture features might not be able to capture the rich spatio-temporal information in the parametric maps. In this work, we studied the ability of convolutional neural networks in predicting the response to NACT at an early stage.