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DOI | Resolve DOI: https://doi.org/10.1038/s41598-022-07000-1 |
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Author | Search for: Zabel, W. Jeffrey; Search for: Allam, Nader; Search for: Foltz, Warren D.; Search for: Flueraru, Costel1; Search for: Taylor, Edward; Search for: Vitkin, I. Alex |
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Affiliation | - National Research Council of Canada. Advanced Electronics and Photonics
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Funder | Search for: Princess Margaret Cancer Foundation; Search for: Terry Fox Foundation; Search for: Natural Sciences and Engineering Research Council of Canada; Search for: Temerty Faculty of Medicine, University of Toronto; Search for: Canadian Institutes of Health Research |
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Format | Text, Article |
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Abstract | Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) is emerging as a valuable tool for non-invasive volumetric monitoring of the tumor vascular status and its therapeutic response. However, clinical utility of DCE-MRI is challenged by uncertainty in its ability to quantify the tumor microvasculature (μm scale) given its relatively poor spatial resolution (mm scale at best). To address this challenge, we directly compared DCE-MRI parameter maps with co-registered micron-scale-resolution speckle variance optical coherence tomography (svOCT) microvascular images in a window chamber tumor mouse model. Both semi and fully quantitative (Toft’s model) DCE-MRI metrics were tested for correlation with microvascular svOCT biomarkers. svOCT’s derived vascular volume fraction (VVF) and the mean distance to nearest vessel (DNV) metrics were correlated with DCE-MRI vascular biomarkers such as time to peak contrast enhancement (r=−0.81 and 0.83 respectively, P<0.0001 for both), the area under the gadolinium-time concentration curve (r=0.50 and −0.48 respectively, P<0.0001 for both) and ktrans (r=0.64 and −0.61 respectively, P<0.0001 for both). Several other correlated micro–macro vascular metric pairs were also noted. The microvascular insights afforded by svOCT may help improve the clinical utility of DCE-MRI for tissue functional status assessment and therapeutic response monitoring applications. |
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Publication date | 2022-02-24 |
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Publisher | Springer Nature Limited |
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Licence | |
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In | |
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Language | English |
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Peer reviewed | Yes |
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Export citation | Export as RIS |
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Report a correction | Report a correction (opens in a new tab) |
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Record identifier | d43ff0c2-97bb-4453-ab02-286e572249c9 |
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Record created | 2022-03-16 |
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Record modified | 2022-03-16 |
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