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Open AccessArticle

Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer

1
Department of Biomedical Engineering, The University of Texas at Austin, Austin, TX 78712, USA
2
Department of Medicine, Vanderbilt University Medical Center, Nashville, TN 37232, USA
3
Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin, Austin, TX 78712, USA
4
Department of Biomedical Engineering, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
5
Department of Radiology, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
6
O’Neal Comprehensive Cancer Center, The University of Alabama at Birmingham, Birmingham, AL 35294, USA
7
Department of Diagnostic Medicine, The University of Texas at Austin, Austin, TX 78712, USA
8
Department of Oncology, The University of Texas at Austin, Austin, TX 78712, USA
9
Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
*
Author to whom correspondence should be addressed.
Cancers 2020, 12(6), 1682; https://doi.org/10.3390/cancers12061682
Received: 19 May 2020 / Revised: 20 June 2020 / Accepted: 22 June 2020 / Published: 24 June 2020
(This article belongs to the Special Issue Radiomics and Cancers)
This study identifies physiological tumor habitats from quantitative magnetic resonance imaging (MRI) data and evaluates their alterations in response to therapy. Two models of breast cancer (BT-474 and MDA-MB-231) were imaged longitudinally with diffusion-weighted MRI and dynamic contrast-enhanced MRI to quantify tumor cellularity and vascularity, respectively, during treatment with trastuzumab or albumin-bound paclitaxel. Tumors were stained for anti-CD31, anti-Ki-67, and H&E. Imaging and histology data were clustered to identify tumor habitats and percent tumor volume (MRI) or area (histology) of each habitat was quantified. Histological habitats were correlated with MRI habitats. Clustering of both the MRI and histology data yielded three clusters: high-vascularity high-cellularity (HV-HC), low-vascularity high-cellularity (LV-HC), and low-vascularity low-cellularity (LV-LC). At day 4, BT-474 tumors treated with trastuzumab showed a decrease in LV-HC (p = 0.03) and increase in HV-HC (p = 0.03) percent tumor volume compared to control. MDA-MB-231 tumors treated with low-dose albumin-bound paclitaxel showed a longitudinal decrease in LV-HC percent tumor volume at day 3 (p = 0.01). Positive correlations were found between histological and imaging-derived habitats: HV-HC (BT-474: p = 0.03), LV-HC (MDA-MB-231: p = 0.04), LV-LC (BT-474: p = 0.04; MDA-MB-231: p < 0.01). Physiologically distinct tumor habitats associated with therapeutic response were identified with MRI and histology data in preclinical models of breast cancer. View Full-Text
Keywords: diffusion-weighted MRI; dynamic contrast-enhanced MRI; immunohistochemistry; habitat imaging; intratumoral heterogeneity diffusion-weighted MRI; dynamic contrast-enhanced MRI; immunohistochemistry; habitat imaging; intratumoral heterogeneity
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Syed, A.K.; Whisenant, J.G.; Barnes, S.L.; Sorace, A.G.; Yankeelov, T.E. Multiparametric Analysis of Longitudinal Quantitative MRI Data to Identify Distinct Tumor Habitats in Preclinical Models of Breast Cancer. Cancers 2020, 12, 1682.

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