Speaker
Description
Lung cancer remains the leading cause of cancer death in Canada. More accurate risk stratification tools are needed to determine patient prognosis and aid in determining optimal treatment plans. Computed tomography (CT) and positron emission tomography (PET) images are widely used for cancer staging. Artificial intelligence (AI) models integrating quantitative imaging biomarkers have the potential to provide additional information on disease prognosis that is not visible to the radiologists’ eye. This talk will describe the development, validation, and evaluation of a clinical decision-support system integrating AI models utilizing multi-modality imaging and clinical information for the risk stratification of lung cancer patients following surgery. These computational imaging-based models can assist clinicians in decision-making and allow for personalized medicine, with the goal of improving outcomes for cancer patients.
| Keyword-1 | Artificial Intelligence |
|---|---|
| Keyword-2 | Medical Imaging |
