On Wednesday, December 11, Oncopole, the MEDTEQ consortium, the Institut TransMedTech and the Cancer Research Society, as part of the joint Onco-Tech competition, announced an investment of $2.6 million to support innovative projects in oncology and medical technologies.
The Onco-Tech competition was set up by a group of leading funders keen to combine academic research with industrial expertise, and is an opportunity for Quebec researchers to accelerate the development, validation and marketing of new medical technologies in oncology, for the benefit of patients.
Five projects benefiting from substantial support
Selected for their ability to meet a clinical need through the integration of innovative technologies, in collaboration with industrial partners such as Imagia, Siemens, Elekta, Photo Etc and ORS, five projects will benefit from major financial support.
Better classification of mammographic abnormalities thanks to artificial intelligence [L’équipe de Julie Lemieux et de Louise Provencher (CHU de Québec – Université Laval)]
Using Imagia’s EVIDENS platform, the research project aims to classify mammographic abnormalities more accurately as “malignant” or “benign”. This would reduce the number of additional investigations (other breast imaging or biopsy) for abnormal mammography images. The project also holds out the hope of major advances in breast cancer screening: it seeks to obtain an objective measurement of breast density through artificial intelligence.
Towards a better quantitative and multiplexed diagnosis of lung cancer through the use of metallic nanoparticles [Michel Meunier and Dominique Trudel’s team (Polytechnique Montréal, CHUM)]
The project focuses on the innovative use of multiple metallic nanoparticles of different colors targeting cell surface proteins to ensure more accurate quantitative and multiplexed diagnosis and optimal immunological treatment selection for lung cancer patients.
Predicting clinical response to immunotherapy in lung cancer patients using artificial intelligence [The team of Drs Philippe Joubert and Bertrand Routy (Institut universitaire de cardiologie et de pneumologie de Québec, CHUM Research Centre)]
Using artificial intelligence, the project aims to develop an algorithm integrating clinical, radiological and molecular tumor characteristics in patients with advanced lung cancer, to predict their potential response to treatments aimed at reactivating the immune system to destroy tumor cells (immunotherapy).
Better detection of hepatocellular carcinoma to improve the efficacy of liver cancer treatments, thanks to innovative algorithms [The team of Guy Cloutier and Dr. An Tang, Centre de recherche du CHUM, Université de Montréal]
The team is investigating the mechanical and structural properties of the liver using innovative algorithms applied to experimental ultrasound images to increase detection of hepatocellular carcinoma (HCC) and thus improve surveillance, treatment efficacy and patient survival.
A predictive platform for radiotherapy treatments thanks to breakthroughs in artificial intelligence [The team of Samuel Kadoury and Dr. David Roberge (Polytechnique Montréal, CHUM)]
The aim of the project is to develop a prediction platform based on deep learning for digital planning and radiotherapy treatments – adaptable on a case-by-case basis – from medical images, for cancer patients.
This major funding will enable the selected projects to benefit from targeted, complementary expertise to accelerate the development of new medical technologies that offer hope in the fight against cancer.
