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Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting
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 therap...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627106/ https://www.ncbi.nlm.nih.gov/pubmed/34838096 http://dx.doi.org/10.1186/s13058-021-01489-6 |
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author | Virostko, John Sorace, Anna G. Slavkova, Kalina P. Kazerouni, Anum S. Jarrett, Angela M. DiCarlo, Julie C. Woodard, Stefanie Avery, Sarah Goodgame, Boone Patt, Debra Yankeelov, Thomas E. |
author_facet | Virostko, John Sorace, Anna G. Slavkova, Kalina P. Kazerouni, Anum S. Jarrett, Angela M. DiCarlo, Julie C. Woodard, Stefanie Avery, Sarah Goodgame, Boone Patt, Debra Yankeelov, Thomas E. |
author_sort | Virostko, John |
collection | PubMed |
description | 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 (K(trans)) 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 K(trans) 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, K(trans), 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-021-01489-6. |
format | Online Article Text |
id | pubmed-8627106 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-86271062021-11-30 Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting Virostko, John Sorace, Anna G. Slavkova, Kalina P. Kazerouni, Anum S. Jarrett, Angela M. DiCarlo, Julie C. Woodard, Stefanie Avery, Sarah Goodgame, Boone Patt, Debra Yankeelov, Thomas E. Breast Cancer Res Research Article 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 (K(trans)) 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 K(trans) 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, K(trans), 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13058-021-01489-6. BioMed Central 2021-11-27 2021 /pmc/articles/PMC8627106/ /pubmed/34838096 http://dx.doi.org/10.1186/s13058-021-01489-6 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Virostko, John Sorace, Anna G. Slavkova, Kalina P. Kazerouni, Anum S. Jarrett, Angela M. DiCarlo, Julie C. Woodard, Stefanie Avery, Sarah Goodgame, Boone Patt, Debra Yankeelov, Thomas E. Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting |
title | Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting |
title_full | Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting |
title_fullStr | Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting |
title_full_unstemmed | Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting |
title_short | Quantitative multiparametric MRI predicts response to neoadjuvant therapy in the community setting |
title_sort | quantitative multiparametric mri predicts response to neoadjuvant therapy in the community setting |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8627106/ https://www.ncbi.nlm.nih.gov/pubmed/34838096 http://dx.doi.org/10.1186/s13058-021-01489-6 |
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