Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response

Archana Machireddy, Guillaume Thibault, Wei Huang, Xubo Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publication40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages682-685
Number of pages4
Volume2018-July
ISBN (Electronic)9781538636466
DOIs
StatePublished - Oct 26 2018
Event40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 - Honolulu, United States
Duration: Jul 18 2018Jul 21 2018

Other

Other40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018
CountryUnited States
CityHonolulu
Period7/18/187/21/18

Fingerprint

Chemotherapy
Magnetic resonance
Magnetic Resonance Imaging
Breast Neoplasms
Imaging techniques
Drug Therapy
Tumors
Textures
Oncology
Capillary Permeability
Therapeutics
Toxicity
Neoplasms
Hand
Neural networks

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

Cite this

Machireddy, A., Thibault, G., Huang, W., & Song, X. (2018). Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018 (Vol. 2018-July, pp. 682-685). [8512301] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/EMBC.2018.8512301

Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response. / Machireddy, Archana; Thibault, Guillaume; Huang, Wei; Song, Xubo.

40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. p. 682-685 8512301.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Machireddy, A, Thibault, G, Huang, W & Song, X 2018, Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response. in 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. vol. 2018-July, 8512301, Institute of Electrical and Electronics Engineers Inc., pp. 682-685, 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018, Honolulu, United States, 7/18/18. https://doi.org/10.1109/EMBC.2018.8512301
Machireddy A, Thibault G, Huang W, Song X. Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response. In 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July. Institute of Electrical and Electronics Engineers Inc. 2018. p. 682-685. 8512301 https://doi.org/10.1109/EMBC.2018.8512301
Machireddy, Archana ; Thibault, Guillaume ; Huang, Wei ; Song, Xubo. / Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response. 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018. Vol. 2018-July Institute of Electrical and Electronics Engineers Inc., 2018. pp. 682-685
@inproceedings{44b5b212326749e7b40a7154e427cb0a,
title = "Analysis of DCE-MRI for Early Prediction of Breast Cancer Therapy Response",
abstract = "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.",
author = "Archana Machireddy and Guillaume Thibault and Wei Huang and Xubo Song",
year = "2018",
month = "10",
day = "26",
doi = "10.1109/EMBC.2018.8512301",
language = "English (US)",
volume = "2018-July",
pages = "682--685",
booktitle = "40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

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

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

UR - http://www.scopus.com/inward/citedby.url?scp=85056650983&partnerID=8YFLogxK

U2 - 10.1109/EMBC.2018.8512301

DO - 10.1109/EMBC.2018.8512301

M3 - Conference contribution

C2 - 30440488

AN - SCOPUS:85056650983

VL - 2018-July

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.

ER -