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Volume 5
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10.18383/j.tom.2018.00046
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Article

Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps

by
Archana Machireddy
,
Guillaume Thibault
,
Alina Tudorica
,
Aneela Afzal
,
May Mishal
,
Kathleen Kemmer
,
Arpana Naik
,
Megan Troxell
,
Eric Goranson
,
Karen Oh
,
Nicole Roy
,
Neda Jafarian
,
Megan Holtorf
,
Wei Huang
and
Xubo Song
*
Oregon Health and Science University, Portland, OR, USA; [email protected]
*
Author to whom correspondence should be addressed.
Tomography 2019, 5(1), 90-98; https://doi.org/10.18383/j.tom.2018.00046
Submission received: 16 December 2018 / Revised: 13 January 2019 / Accepted: 15 February 2019 / Published: 1 March 2019
Versions Notes

Abstract

We aimed to determine whether multiresolution fractal analysis of voxel-based dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps can provide early prediction of breast cancer response to neoadjuvant chemotherapy (NACT). In total, 55 patients underwent 4 DCE-MRI examinations before, during, and after NACT. The shutter-speed model was used to analyze the DCE-MRI data and generate parametric maps within the tumor region of interest. The proposed multiresolution fractal method and the more conventional methods of single-resolution fractal, gray-level co-occurrence matrix, and run-length matrix were used to extract features from the parametric maps. Only the data obtained before and after the first NACT cycle were used to evaluate early prediction of response. With a training (N = 40) and testing (N = 15) data set, support vector machine was used to assess the predictive abilities of the features in classification of pathologic complete response versus non-pathologic complete response. Generally the multiresolution fractal features from individual maps and the concatenated features from all parametric maps showed better predictive performances than conventional features, with receiver operating curve area under the curve (AUC) values of 0.91 (all parameters) and 0.80 (Ktrans), in the training and testing sets, respectively. The differences in AUC were statistically significant (P < .05) for several parametric maps. Thus, multiresolution analysis that decomposes the texture at various spatial-frequency scales may more accurately capture changes in tumor vascular heterogeneity as measured by DCE-MRI, and therefore provide better early prediction of NACT response.
Keywords: breast cancer; DCE-MRI; neoadjuvant chemotherapy; early prediction; multiresolution fractal analysis breast cancer; DCE-MRI; neoadjuvant chemotherapy; early prediction; multiresolution fractal analysis

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MDPI and ACS Style

Machireddy, A.; Thibault, G.; Tudorica, A.; Afzal, A.; Mishal, M.; Kemmer, K.; Naik, A.; Troxell, M.; Goranson, E.; Oh, K.; et al. Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps. Tomography 2019, 5, 90-98. https://doi.org/10.18383/j.tom.2018.00046

AMA Style

Machireddy A, Thibault G, Tudorica A, Afzal A, Mishal M, Kemmer K, Naik A, Troxell M, Goranson E, Oh K, et al. Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps. Tomography. 2019; 5(1):90-98. https://doi.org/10.18383/j.tom.2018.00046

Chicago/Turabian Style

Machireddy, Archana, Guillaume Thibault, Alina Tudorica, Aneela Afzal, May Mishal, Kathleen Kemmer, Arpana Naik, Megan Troxell, Eric Goranson, Karen Oh, and et al. 2019. "Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps" Tomography 5, no. 1: 90-98. https://doi.org/10.18383/j.tom.2018.00046

APA Style

Machireddy, A., Thibault, G., Tudorica, A., Afzal, A., Mishal, M., Kemmer, K., Naik, A., Troxell, M., Goranson, E., Oh, K., Roy, N., Jafarian, N., Holtorf, M., Huang, W., & Song, X. (2019). Early Prediction of Breast Cancer Therapy Response using Multiresolution Fractal Analysis of DCE-MRI Parametric Maps. Tomography, 5(1), 90-98. https://doi.org/10.18383/j.tom.2018.00046

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