Vol. 3 No. 1 - Mar 2017

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DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response 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 Center Spatial Systems Biomedicine, BME; 2 Department of Diagnostic Radiology; 3 Department of Advanced Imaging Research Center; 4 Knight Cancer Institute; 5 Department of Medical Oncology; 6 Department of Surgical Oncology; 7 Department of Pathology; and 8 Center for Spoken Language Understanding, Oregon Health & Science University, Portland, Oregon Corresponding Author: Guillaume Thibault OHSU Center for System Spatial Biomedicine, BME, 2730 SW Moody Ave, CLSB 3N046.11, Portland OR, 97201-5042; E-mail: Key Words: breast cancer, DCE-MRI, neoadjuvant chemotherapy, early prediction, 3D textural features, statistical matrices, residual cancer burden Abbreviations: Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), neoadjuvant chemotherapy (NAC), pharmacokinetic (PK), residual cancer burden (RCB), locally advanced breast cancer (LABC), pathologic complete response (pCR), 2-dimensional (2D), 3-dimensional (3D), contrast agent (CA), regions of interest (ROIs), standard Tofts model (SM), shutter-speed model (SSM), area under the curve (AUC), gray-level cooccurrence matrix (GLCM), run length matrix (RLM), size zone matrix (SZM), local binary pattern (LBP), pattern spectrum (PS), pathologic nonresponse (pNR) 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) pa- rameters 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 quantita- tive PK or semiquantitative metric, and their capabilities for early prediction of RCB were examined by corre- lating feature changes between the 2 MRI studies with RCB. There were 1069 pairs of feature–map combi- nations that showed effectiveness for response prediction with 4 correlation coefficients .0.7. The 3-dimen- sional 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% specific- ity 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. INTRODUCTION Neoadjuvant (preoperative) chemotherapy (NAC) was introduced in the 1970s, and over the past 2 decades, it has been established as a standard of care for patients with locally advanced breast cancer (LABC) for both initially operable and inoperable tumors (1-3). Compared with adjuvant (postoperative) chemotherapy, NAC has been shown to increase the breast-conserving surgery rate. Furthermore, the pathologic complete response (pCR) to NAC or minimal post-NAC residual disease has been found to be clearly correlated with disease-free and overall survival rates (1, 4-9). However, patients undergoing NAC do not always achieve pCR, and the pCR rate is reported to vary in the range of 6%– 45% depending on breast cancer subtypes and treatment regimens (10-13). In the emerging era of precision medicine, early prediction of NAC response may allow rapid, personalized treatment regimen alterations for nonresponding patients with breast cancer, and spare them from potential short- and long- term toxicities associated with ineffective therapies. In addition, accurate evaluation of residual disease after NAC is vital for surgical decision-making and could result in surgical treatment plans that are more tailored to individual patients. As a noninvasive imaging method for in vivo measurement of tissue microvascular perfusion and permeability, dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is RESEARCH ARTICLE ABSTRACT © 2017 The Authors. Published by Grapho Publications, LLC This is an open access article under the CC BY-NC-ND license ( ISSN 2379-1381 TOMOGRAPHY.ORG | VOLUME 3 NUMBER 1 | MARCH 2017 23

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