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A Dorsal Skinfold Window Chamber Tumor Mouse Model for Combined Intravital Microscopy and Magnetic Resonance Imaging in Translational Cancer Research

Preclinical intravital imaging such as microscopy and optical coherence tomography have proven to be valuable tools in cancer research for visualizing the ...

University of Toronto

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 ([Formula: see text] 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 ([Formula: see text]) metrics were correlated with DCE-MRI vascular biomarkers such as time to peak contrast enhancement ([Formula: see text] and [Formula: see text] respectively, [Formula: see text] for both), the area under the gadolinium-time concentration curve ([Formula: see text] and [Formula: see text] respectively, [Formula: see text] for both) and [Formula: see text] ([Formula: see text] and [Formula: see text] respectively, [Formula: see text] 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.

Bridging the macro to micro resolution gap with angiographic optical coherence tomography and dynamic contrast enhanced MRI.

Stereotactic body radiotherapy (SBRT) is an emerging cancer treatment due to its logistical and potential therapeutic benefits as compared to conventional radiotherapy. However, its mechanism of action is yet to be fully understood, likely involving the ablation of tumour microvasculature by higher doses per fraction used in SBRT. In this study, we hypothesized that longitudinal imaging and quantification of the vascular architecture may elucidate the relationship between the microvasculature and tumour response kinetics. Pancreatic human tumour xenografts were thus irradiated with single doses of [Formula: see text], [Formula: see text] and [Formula: see text] Gy to simulate the first fraction of a SBRT protocol. Tumour microvascular changes were monitored with optical coherence angiography for up to [Formula: see text] weeks following irradiation. The temporal kinetics of two microvascular architectural metrics were studied as a function of time and dose: the diffusion-limited fraction, representing poorly vascularized tissue [Formula: see text] μm from the nearest detected vessel, and the vascular distribution convexity index, a measure of vessel aggregation at short distances. These biological metrics allowed for dose dependent temporal evaluation of tissue (re)vascularization and vessel aggregation after radiotherapy, showing promise for determining the SBRT dose-response relationship.

Longitudinal in-vivo quantification of tumour microvascular heterogeneity by optical coherence angiography in pre-clinical radiation therapy.

The dominant consequence of irradiating biological systems is cellular damage, yet microvascular damage begins to assume an increasingly important role as the radiation dose levels increase. This is currently becoming more relevant in radiation medicine with its pivot towards higher-dose-per-fraction/fewer fractions treatment paradigm (e.g., stereotactic body radiotherapy (SBRT)). We have thus developed a 3D preclinical imaging platform based on speckle-variance optical coherence tomography (svOCT) for longitudinal monitoring of tumour microvascular radiation responses in vivo. Here we present an artificial intelligence (AI) approach to analyze the resultant microvascular data. In this initial study, we show that AI can successfully classify SBRT-relevant clinical radiation dose levels at multiple timepoints (t = 2-4 weeks) following irradiation (10 Gy and 30 Gy cohorts) based on induced changes in the detected microvascular networks. Practicality of the obtained results, challenges associated with modest number of animals, their successful mitigation via augmented data approaches, and advantages of using 3D deep learning methodologies, are discussed. Extension of this encouraging initial study to longitudinal AI-based time-series analysis for treatment outcome predictions at finer dose level gradations is envisioned.

Binary dose level classification of tumour microvascular response to radiotherapy using artificial intelligence analysis of optical coherence tomography images.

Lymphatic and peripheral nervous system imaging is of prime importance for monitoring various important pathologic processes including cancer development and metastasis, and response to therapy.

Detection and differentiation of semi-transparent materials simulating biological structures using optical coherence tomography: a phantom study.

Laser speckle imaging (LSI) techniques have emerged as a promising method for visualizing functional blood vessels and tissue perfusion by analyzing the speckle patterns generated by coherent light interacting with living biological tissue. These patterns carry important biophysical tissue information including blood flow dynamics. The noninvasive, label-free, and wide-field attributes along with relatively simple instrumental schematics make it an appealing imaging modality in preclinical and clinical applications. The review outlines the fundamentals of speckle physics and the three categories of LSI techniques based on their degree of quantification: qualitative, semi-quantitative and quantitative. Qualitative LSI produces microvascular maps by capturing speckle contrast variations between blood vessels containing moving red blood cells and the surrounding static tissue. Semi-quantitative techniques provide a more accurate analysis of blood flow dynamics by accounting for the effect of static scattering on spatiotemporal parameters. Quantitative LSI such as optical speckle image velocimetry provides quantitative flow velocity measurements, which is inspired by the particle image velocimetry in fluid mechanics. Additionally, discussions regarding the prospects of future innovations in LSI techniques for optimizing the vascular flow quantification with associated clinical outlook are presented.

Nader Allam

Department of Medical Biophysics

University of Toronto

A Dorsal Skinfold Window Chamber Tumor Mouse Model for Combined Intravital Microscopy and Magnetic Resonance Imaging in Translational Cancer Research

Nader Allam

.css-o250i3{font-size:18px;font-family:Open Sans,sans-serif;line-height:21.6px;}Department of Medical Biophysics

University of Toronto

A Dorsal Skinfold Window Chamber Tumor Mouse Model for Combined Intravital Microscopy and Magnetic Resonance Imaging in Translational Cancer Research

Department of Medical Biophysics