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10.18383/j.tom.2016.00241
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Article

DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response

by
Guillaume Thibault
1,*,
Alina Tudorica
2,
Aneela Afzal
3,
Stephen Y-C. Chui
4,5,
Arpana Naik
4,6,
Megan L. Troxell
4,7,
Kathleen A. Kemmer
4,5,
Karen Y. Oh
2,
Nicole Roy
2,
Neda Jafarian
2,
Megan L. Holtorf
4,
Wei Huang
3,4 and
Xubo Song
8
1
OHSU Center for System Spatial Biomedicine, BME, 2730 SW Moody Ave, CLSB 3N046.11, Portland ,OR 97201-5042, USA
2
Department of Diagnostic Radiology, Oregon Health & Science University, Portland, Oregon 97239, USA
3
Department of Advanced Imaging Research Center, Oregon Health & Science University, Portland, Oregon 97239, USA
4
Knight Cancer Institute, Oregon Health & Science University, Portland, Oregon 97210, USA
5
Department of Medical Oncology, Oregon Health & Science University, Portland, Oregon 97239-3098, USA
6
Department of Surgical Oncology, Oregon Health & Science University, Portland, Oregon 97239, USA
7
Department of Pathology, Oregon Health & Science University, Portland, Oregon 97239, USA
8
Center for Spoken Language Understanding, Oregon Health & Science University, Portland, Oregon 97239, USA
*
Author to whom correspondence should be addressed.
Tomography 2017, 3(1), 23-32; https://doi.org/10.18383/j.tom.2016.00241
Submission received: 12 May 2017 / Revised: 11 June 2017 / Accepted: 13 July 2017 / Published: 1 August 2017
Versions Notes

Abstract

This study investigates the effectiveness of hundreds of texture features extracted from voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps for early prediction of breast cancer response to neoadjuvant chemotherapy (NAC). In total, 38 patients with breast cancer underwent DCE-MRI before (baseline) and after the first of the 6–8 NAC cycles. Quantitative pharmacokinetic (PK) parameters and semiquantitative metrics were estimated from DCE-MRI time-course data. The residual cancer burden (RCB) index value was computed based on pathological analysis of surgical specimens after NAC completion. In total, 1043 texture features were extracted from each of the 13 parametric maps of quantitative PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by correlating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature–map combinations that showed effectiveness for response prediction with 4 correlation coefficients >0.7. The 3-dimensional gray-level cooccurrence matrix was the most effective feature extraction method for therapy response prediction, and, in general, the statistical features describing texture heterogeneity were the most effective features. Quantitative PK parameters, particularly those estimated with the shutter-speed model, were more likely to generate effective features for prediction response compared with the semiquantitative metrics. The best feature–map pair could predict pathologic complete response with 100% sensitivity and 100% specificity using our cohort. In conclusion, breast tumor heterogeneity in microvasculature as measured by texture features of voxel-based DCE-MRI parametric maps could be a useful biomarker for early prediction of NAC response.
Keywords: breast cancer; DCE-MRI; neoadjuvant chemotherapy; early prediction; 3D textural features; statistical matrices; residual cancer burden breast cancer; DCE-MRI; neoadjuvant chemotherapy; early prediction; 3D textural features; statistical matrices; residual cancer burden

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MDPI and ACS Style

Thibault, G.; Tudorica, A.; Afzal, A.; Chui, S.Y.-C.; Naik, A.; Troxell, M.L.; Kemmer, K.A.; Oh, K.Y.; Roy, N.; Jafarian, N.; et al. DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response. Tomography 2017, 3, 23-32. https://doi.org/10.18383/j.tom.2016.00241

AMA Style

Thibault G, Tudorica A, Afzal A, Chui SY-C, Naik A, Troxell ML, Kemmer KA, Oh KY, Roy N, Jafarian N, et al. DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response. Tomography. 2017; 3(1):23-32. https://doi.org/10.18383/j.tom.2016.00241

Chicago/Turabian Style

Thibault, Guillaume, Alina Tudorica, Aneela Afzal, Stephen Y-C. Chui, Arpana Naik, Megan L. Troxell, Kathleen A. Kemmer, Karen Y. Oh, Nicole Roy, Neda Jafarian, and et al. 2017. "DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response" Tomography 3, no. 1: 23-32. https://doi.org/10.18383/j.tom.2016.00241

APA Style

Thibault, G., Tudorica, A., Afzal, A., Chui, S. Y. -C., Naik, A., Troxell, M. L., Kemmer, K. A., Oh, K. Y., Roy, N., Jafarian, N., Holtorf, M. L., Huang, W., & Song, X. (2017). DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response. Tomography, 3(1), 23-32. https://doi.org/10.18383/j.tom.2016.00241

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