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Breast Cancer Research
Published in: Breast Cancer Research 1/2021

Open Access 01-12-2021 | Magnetic Resonance Imaging | Research article

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

Authors: John Virostko, Anna G. Sorace, Kalina P. Slavkova, Anum S. Kazerouni, Angela M. Jarrett, Julie C. DiCarlo, Stefanie Woodard, Sarah Avery, Boone Goodgame, Debra Patt, Thomas E. Yankeelov

Published in: Breast Cancer Research | Issue 1/2021

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Abstract

Background

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.

Methods

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.

Results

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.

Conclusions

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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Metadata
Title
Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting
Authors
John Virostko
Anna G. Sorace
Kalina P. Slavkova
Anum S. Kazerouni
Angela M. Jarrett
Julie C. DiCarlo
Stefanie Woodard
Sarah Avery
Boone Goodgame
Debra Patt
Thomas E. Yankeelov
Publication date
01-12-2021
Publisher
BioMed Central
Published in
Breast Cancer Research / Issue 1/2021
Electronic ISSN: 1465-542X
DOI
https://doi.org/10.1186/s13058-021-01489-6

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