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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

Auto-contouring may reduce workload, interobserver variation, and time associated with manual contouring of organs at risk. Manual contouring remains the standard due in part to uncertainty around the time and workload savings after accounting for the review and editing of auto-contours. This preliminary study compares a standard manual contouring workflow with 2 auto-contouring workflows (atlas and deep learning) for contouring the bladder and rectum in patients with prostate cancer.

Clinical Evaluation of Deep Learning and Atlas-Based Auto-Contouring of Bladder and Rectum for Prostate Radiation Therapy.

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.

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.

W. Jeffrey Zabel

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

W. Jeffrey Zabel

.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