On Wednesday, December 11th, a new joint initiative between the Oncopole, the Cancer Research Society, MEDTEQ, and the TransMedTech Institute held its first MEET&TEQ where it announced the winners of its new competition.
For the first time in Quebec history, the Onco-Tech competition brings together industry leaders and academic cancer researchers, with the goal of providing the oncology world with new medical technologies that can bring radical beneficial change to the lives of patients. This major funding will enable the projects selected to benefit from targeted and complementary expertise to accelerate the development of new medical technologies that can be used to outsmart cancer.
5 projects will be funded for a total of $2.6 M dollars:
Julie Lemieux and Louise Provencher (CHU de Québec – Université Laval)
This research project aims to more accurately classify mammographic abnormalities as either “malignant” or “benign”, as well as objectively measure breast density with the use of artificial intelligence. These provide major advances in breast cancer screening and decrease the need for additional investigations (biopsies, etc.) when an abnormality is detected.
Michel Meunier and Dominique Trudel (Polytechnique Montréal, CHUM)
The project focuses on the innovative use of metallic nanoparticles targeting cell surface proteins to ensure a more thorough diagnosis and a larger selection of immune treatments for lung cancer patients.
Drs. Philippe Joubert and Bertrand Routy (Quebec Heart and Lung Institute, CHUM Research Centre)
This project aims to use artificial intelligence to develop an algorithm integrating the clinical, radiological and molecular characteristics of tumours in advanced lung cancer patients. The use of this information could help predict patients’ potential responses to immunotherapy treatments used to destroy tumour cells.
Guy Cloutier and Dr. An Tang (CHUM Research Centre, Université de Montréal)
This project studies the mechanical and structural properties of the liver using novel algorithms applied to experimental ultrasound images. This, in turn, can increase the detection of hepatocellular carcinomas and improve monitoring, treatment effectiveness and patient survival.
Samuel Kadoury and Dr. David Roberge (Polytechnique Montréal, CHUM)
The project’s objective is to develop a prediction platform based on deep learning for digital planning and radiation treatments using medical images, for cancer patients.