University Health Network

This project uses a multi-modal method to train a foundational model for medical images of cervical cancer patients. Multiple imaging modalities are used over the course of treatment: multiparametric MRI, CT, cone-beam CT, and PET when indicated. This motivat...

Artificial intelligence (AI) is increasingly embedded across radiation medicine, with applications in clinical decision support, workflow efficiency, personalization of care, and quality assurance. Despite rapid technical advancement, there remains a critical...

High dose-rate brachytherapy (HDR-BT) is an essential component of cervical cancer treatment. While deep learning has shown promise to automate tasks within HDR-BT, such as segmentation of organs-at-risk (OARs) and targets, quality labelled data is limited. T...

18F-fluorodeoxygluose positron emission tomography (18F-FDG-PET) is clinically useful in cervical cancer care and has been shown to reduce interobserver variation of the residual gross tumour volume in brachytherapy. Chemical exchange saturation transfer MRI...

To investigate the diagnostic performance of perfusion and diffusion MRI in differentiating progressing tumours from radiation necrosis in brain metastases (BM) post-radiosurgery, using a radiomic-based machine learning classification approach.

Small variations in radiochromic material thickness in films leads to variability in sensitivity and uncertainty in measured dose. To achieve high accuracy, the commercial films correct for this through a dispersion of the yellow dye, which acts to correct fo...