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

Guillaume Thibault,Megan L Holtorf,Wei Huang,Neda Jafarian,Stephen Y-C Chui,Arpana Naik,Alina Tudorica,Megan L Troxell,Aneela Afzal,Xubo Song,Karen Y Oh,Kathleen A Kemmer,Nicole Roy

doi:10.18383/j.tom.2016.00241

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.

Highlights

Neoadjuvant 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 [1,2,3]
Ability for each of the 1043 3D texture features extracted from a total of 13 dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parametric maps
It clearly needs to be validated with larger patient populations, this noninvasive 3D imaging feature-extraction approach has the potential to become an important clinical tool in the emerging era of precision medicine to identify, in the early stages of treatment, nonresponding patients for alternative personalized therapy regimens, and stratify patients for better surgical decision-making, and after surgery care planning based on accurate prediction of residual cancer burden (RCB)

Summary

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 [1,2,3]. Compared with adjuvant (postoperative) chemotherapy, NAC has been shown to increase the breast-conserving surgery rate. 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 longterm toxicities associated with ineffective therapies. 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

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