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

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

Research output: Contribution to journalArticle

20 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)23-32
Number of pages10
JournalTomography (Ann Arbor, Mich.)
Volume3
Issue number1
DOIs
StatePublished - Mar 1 2017

Keywords

  • 3D textural features
  • breast cancer
  • DCE-MRI
  • early prediction
  • neoadjuvant chemotherapy
  • residual cancer burden
  • statistical matrices

Fingerprint Dive into the research topics of 'DCE-MRI Texture Features for Early Prediction of Breast Cancer Therapy Response'. Together they form a unique fingerprint.

  • Cite this

    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 (Ann Arbor, Mich.), 3(1), 23-32. https://doi.org/10.18383/j.tom.2016.00241