Lung cancer imaging biomarker development on computed tomography using artificial intelligence
The University Health Network's Quantitative Imaging for Personalized Cancer Medicine (QIPCM) Imaging core lab and Altis Labs, Inc, a Toronto-based company which is submitting its own project proposal and DTA for this research project, are collaborating to develop and validate novel lung cancer imaging biomarkers using deep learning. This project will entail labeling the data to develop detection, segmentation, and classification algorithms. We will explore the potential clinical utility when incorporating algorithms into physician workflow and real-world clinical data. Our goal is to develop robust algorithms that can be applied in clinical settings to improve patient care and outcomes.
- develop state-of-the-art nodule detection and classification algorithms
- test algorithms' utility on screening, diagnostic, and staging workflows
UHN (Quantitative Imaging for Personalized Cancer Medicine) and Altis Labs' R&D team.