Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting

Breast Cancer Research - Tập 23 - Trang 1-12 - 2021
John Virostko1,2,3,4, Anna G. Sorace5,6,7, Kalina P. Slavkova8, Anum S. Kazerouni9, Angela M. Jarrett4, Julie C. DiCarlo2,4, Stefanie Woodard5, Sarah Avery10, Boone Goodgame11, Debra Patt12, Thomas E. Yankeelov1,2,3,4,13,14
1Department of Diagnostic Medicine, University of Texas at Austin, Austin, USA
2Livestrong Cancer Institutes, University of Texas at Austin, Austin, USA
3Department of Oncology, University of Texas at Austin, Austin, USA
4Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, USA
5Department of Radiology, University of Alabama at Birmingham, Birmingham, USA.
6Department of Biomedical Engineering, University of Alabama at Birmingham, Birmingham, USA.
7O’Neal Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, USA
8Department of Physics, University of Texas at Austin, Austin, USA
9Department of Radiology, University of Washington, Seattle, USA
10Austin Radiological Association, Austin, USA
11Dell Seton Medical Center at the University of Texas, Austin, USA
12Texas Oncology, Austin, USA
13Department of Biomedical Engineering, University of Texas at Austin, Austin, USA
14Department of Imaging Physics, MD Anderson Cancer Center, Houston, USA

Tóm tắt

The purpose of this study was to determine whether advanced quantitative magnetic resonance imaging (MRI) can be deployed outside of large, research-oriented academic hospitals and into community care settings to predict eventual pathological complete response (pCR) to neoadjuvant therapy (NAT) in patients with locally advanced breast cancer. Patients with stage II/III breast cancer (N = 28) were enrolled in a multicenter study performed in community radiology settings. Dynamic contrast-enhanced (DCE) and diffusion-weighted (DW)-MRI data were acquired at four time points during the course of NAT. Estimates of the vascular perfusion and permeability, as assessed by the volume transfer rate (Ktrans) using the Patlak model, were generated from the DCE-MRI data while estimates of cell density, as assessed by the apparent diffusion coefficient (ADC), were calculated from DW-MRI data. Tumor volume was calculated using semi-automatic segmentation and combined with Ktrans and ADC to yield bulk tumor blood flow and cellularity, respectively. The percent change in quantitative parameters at each MRI scan was calculated and compared to pathological response at the time of surgery. The predictive accuracy of each MRI parameter at different time points was quantified using receiver operating characteristic curves. Tumor size and quantitative MRI parameters were similar at baseline between groups that achieved pCR (n = 8) and those that did not (n = 20). Patients achieving a pCR had a larger decline in volume and cellularity than those who did not achieve pCR after one cycle of NAT (p < 0.05). At the third and fourth MRI, changes in tumor volume, Ktrans, ADC, cellularity, and bulk tumor flow from baseline (pre-treatment) were all significantly greater (p < 0.05) in the cohort who achieved pCR compared to those patients with non-pCR. Quantitative analysis of DCE-MRI and DW-MRI can be implemented in the community care setting to accurately predict the response of breast cancer to NAT. Dissemination of quantitative MRI into the community setting allows for the incorporation of these parameters into the standard of care and increases the number of clinical community sites able to participate in novel drug trials that require quantitative MRI.

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Breast Cancer Research

Tập 23

1-12

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