TY - GEN
T1 - Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response
AU - Machireddy, Archana
AU - Thibault, Guillaume
AU - Huang, Wei
AU - Song, Xubo
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/26
Y1 - 2018/10/26
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85056650983&partnerID=8YFLogxK
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U2 - 10.1109/EMBC.2018.8512301
DO - 10.1109/EMBC.2018.8512301
M3 - Conference contribution
C2 - 30440488
AN - SCOPUS:85056650983
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 682
EP - 685
BT - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
Y2 - 18 July 2018 through 21 July 2018
ER -