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Functional parameters derived from magnetic resonance imaging reflect vascular morphology in preclinical tumors and in human liver metastases.
Clin. Cancer Res. 24, 4694-4704 (2018)
Publ. Version/Full Text
Research data
DOI
Purpose: Tumor vessels influence the growth and response of tumors to therapy. Imaging vascular changes in vivo using dynamic contrast-enhanced MRI (DCE-MRI) has shown potential to guide clinical decision making for treatment. However, quantitative MR imaging biomarkers of vascular function have not been widely adopted, partly because their relationship to structural changes in vessels remains unclear. We aimed to elucidate the relationships between vessel function and morphology in vivo. Experimental Design: Untreated preclinical tumors with different levels of vascularization were imaged sequentially using DCE-MRI and CT. Relationships between functional parameters from MR (iAUC, Ktrans, and BATfrac) and structural parameters from CT (vessel volume, radius, and tortuosity) were assessed using linear models. Tumors treated with anti-VEGFR2 antibody were then imaged to determine whether antiangiogenic therapy altered these relationships. Finally, functional-structural relationships were measured in 10 patients with liver metastases from colorectal cancer. Results: Functional parameters iAUC and Ktrans primarily reflected vessel volume in untreated preclinical tumors. The relationships varied spatially and with tumor vascularity, and were altered by antiangiogenic treatment. In human liver metastases, all three structural parameters were linearly correlated with iAUC and Ktrans. For iAUC, structural parameters also modified each other's effect. Conclusions: Our findings suggest that MR imaging biomarkers of vascular function are linked to structural changes in tumor vessels and that antiangiogenic therapy can affect this link. Our work also demonstrates the feasibility of three-dimensional functional-structural validation of MR biomarkers in vivo to improve their biological interpretation and clinical utility.
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Publication type
Article: Journal article
Document type
Scientific Article
ISSN (print) / ISBN
1078-0432
e-ISSN
1557-3265
Journal
Clinical Cancer Research
Quellenangaben
Volume: 24,
Issue: 19,
Pages: 4694-4704
Publisher
American Association for Cancer Research (AACR)
Non-patent literature
Publications
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)