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About
Brachytherapy plays a critical role in the management of locally advanced cancer, and recent advances in MRI-guided brachytherapy have led to improvements in survival compared to historical controls, especially for bulkier tumours. The increasing complexity and number of fractions in adaptive brachytherapy treatments lead to the development of automated tools for extracting doses for both quality assurance and research to improve clinical care.
Gene-expression analyses can diagnose many diseases, but its implementation currently involves expensive instrumentation. In molecular computing, wires and transistors of a silicon-based computer are replaced with engineered bio-molecules, which relay and integrate signals via programmed self-assembly. This project aims to build a low-cost and accessible molecular computer capable of executing the functions of a deep-learning multilayer perceptron for point-of-care viral classification.
The CAM PDX model, a novel patient xenograft tumour model developed by engrafting and growing diagnostic biopsy samples in avian chorioallantoic membranes (CAMs), could be used to evaluate the effectiveness of therapeutics with the objective of personalizing treatment.
One of the most common diabetic complications is diabetic retinopathy (DR), a condition that affects the blood vessels in the retina and is the leading cause of vision impairment in working-age adults worldwide. There is a need for a suitable preclinical animal model that allows researchers to quickly and easily study DR such that new therapies may be developed and tested. Here we propose a near real-time platform that uses the duck (Anas platyrhynchos domesticus) embryo retina and basic commercially available medical ultrasound to observe and study advanced PDR ex ovo.
To facilitate the use of the CAM PDX model for hyperpersonalized treatment planning, a rapid evaluation of the response of 50-100 CAM PDXs would be required per patient over time. Micro ultrasound (µUS) could play an important role in this evaluation by providing high resolution tumor volume and flow-based measurements without contrast agent injection.
Standard ontologies are critical for interoperability and multi-site analyses of health data. Nevertheless, mapping concepts to ontologies is often done with generic tools and is labor-intensive. Contextualizing candidate concepts within source data is also done in an ad-hoc manner. We present AnnoDash (formerly called MIMIC-Dash), a flexible dashboard to support annotation of concepts with terms from a given ontology.
Recent work has shown that μUS power Doppler poorly detects CAM tumor microvessels, although improvements are achieved using singular value decomposition (SVD) clutter filtering. However, SVD remains affected by tissue motion and complexities with respect to cut-off order selection. Here we investigate a more robust approach for flow imaging processing, involving motion compensation prior to clutter filtering with SVD or interframe subtraction (IS).
Mar, S., Xu, J., Chérin, E., Foster, FS, Démoré, CEM, Goertz, D., Leong, HS. "Evaluation of Treatment Response in Chorioallantoic Membrane Patient Derived Tumor Model Using Micro Ultrasound and Speckle Variance." Proceedings for 2022 IEEE International Ultrasonics Symposium (IUS). October 2022.
*Xu, J., *Mar, S., Chérin, E., Démoré, CEM, Leong, HS, Goertz, D. "Methods for Micro Ultrasound Flow Imaging of the Chorioallantoic Membrane (CAM) Patient Derived Tumor Model." Proceedings for 2022 IEEE International Ultrasonics Symposium (IUS). October 2022.
Xu, J., Khalvati, F. "Radiomics and Residual Neural Networks for Pretherapeutic MRI Differentiation of BRAF Status in Pediatric Low-Grade Gliomas." University of Toronto. doi:10.13140/RG.2.2.11399.88485/1
Xu, J., Mazwi, M., Johnson, AEW. "AnnoDash, A Clinical Terminology Annotation Dashboard." Journal of the American Medical Informatics Association (JAMIA) Open. 2023;6(3). doi:10.1093/jamiaopen/ooad046