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Journal of Medical Imaging • Open Access • new

Dynamic contrast-enhanced magnetic resonance imaging and diffusion-weighted magnetic resonance imaging for predicting the response of locally advanced breast cancer to neoadjuvant therapy: a meta-analysis
Author(s): John Virostko; Allison Hainline; Hakmook Kang; Lori R. Arlinghaus; Richard G. Abramson; Stephanie L. Barnes; Jeffrey D. Blume; Sarah Avery; Debra Patt; Boone Goodgame; Thomas E. Yankeelov; Anna G. Sorace

Paper Abstract

This meta-analysis assesses the prognostic value of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and diffusion-weighted MRI (DW-MRI) performed during neoadjuvant therapy (NAT) of locally advanced breast cancer. A systematic literature search was conducted to identify studies of quantitative DCE-MRI and DW-MRI performed during breast cancer NAT that report the sensitivity and specificity for predicting pathological complete response (pCR). Details of the study population and imaging parameters were extracted from each study for subsequent meta-analysis. Metaregression analysis, subgroup analysis, study heterogeneity, and publication bias were assessed. Across 10 studies that met the stringent inclusion criteria for this meta-analysis (out of 325 initially identified studies), we find that MRI had a pooled sensitivity of 0.91 [95% confidence interval (CI), 0.80 to 0.96] and specificity of 0.81(95% CI, 0.68 to 0.89) when adjusted for covariates. Quantitative DCE-MRI exhibits greater specificity for predicting pCR than semiquantitative DCE-MRI ( p < 0.001 ). Quantitative DCE-MRI and DW-MRI are able to predict, early in the course of NAT, the eventual response of breast tumors, with a high level of specificity and sensitivity. However, there is a high degree of heterogeneity in published studies highlighting the lack of standardization in the field.

Paper Details

Date Published: 24 November 2017
PDF: 13 pages
J. Med. Imag. 5(1) 011011 doi: 10.1117/1.JMI.5.1.011011
Published in: Journal of Medical Imaging Volume 5, Issue 1
Show Author Affiliations
John Virostko, The Univ. of Texas at Austin (United States)
LIVESTRONG Cancer Institutes, The Univ. of Texas at Austin (United States)
Allison Hainline, Vanderbilt Univ. (United States)
Hakmook Kang, Vanderbilt Univ. (United States)
Ctr. for Quantitative Sciences, Vanderbilt Univ. (United States)
Lori R. Arlinghaus, Vanderbilt Univ. Medical Ctr. (United States)
Richard G. Abramson, Ctr. for Quantitative Sciences, Vanderbilt Univ. (United States)
Vanderbilt Univ Medical Ctr. (United States)
Stephanie L. Barnes, Institute for Computational Engineering and Sciences, Univ. of Texas at Austin (United States)
Univ. of Texas at Austin (United States)
Jeffrey D. Blume, Vanderbilt Univ. (United States)
Sarah Avery, Austin Radiological Assoc. (United States)
Debra Patt, Texas Oncology (United States)
Boone Goodgame, Seton Medical Ctr. Austin (United States)
The Univ. of Texas at Austin (United States)
Thomas E. Yankeelov, The Univ. of Texas at Austin (United States)
LIVESTRONG Cancer Institutes, The Univ. of Texas at Austin (United States)
Institute for Computational Engineering and Sciences, Univ. of Texas at Austin (United States)
Anna G. Sorace, The Univ. of Texas at Austin (United States)
LIVESTRONG Cancer Institutes, The Univ. of Texas at Austin (United States)


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