Reference number : GC-2017-Q1-00257, amendment number : , agreement type : G, recipient legal name : Purdie, Thomas (University of Toronto), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Collaborative Health Research Projects, program name french : Projets de recherche concertée sur la santé, program purpose english : Radiation therapy (RT) is one of the main methods of treating cancer. The delivery of RT is a_x000D_
complicated process that requires both clinical and technical expertise. The process of_x000D_
generating an RT treatment plan, which specifies how the radiation dose for treatment is to be_x000D_
delivered to the patient, relies on complex technology and considerable manual effort often_x000D_
requiring hours to days of dedicated time to plan each patient. We are proposing a method to_x000D_
automatically generate an RT plan that can readily be integrated into the existing clinical RT_x000D_
process._x000D_
The goal of the research is to develop and make available to other institutions an automated_x000D_
RT treatment planning method that will i) rapidly generate RT plans in minutes, expediting the_x000D_
RT process, ii) allow patients greater access to RT based on the expertise of established_x000D_
cancer institutions in Canada, and iii) produce high quality RT plans that are tailored to each_x000D_
specific patient._x000D_
The automated planning method applies state-of-the-art machine learning, image registration,_x000D_
and optimization algorithms to learn which relationships and patterns in RT image and RT_x000D_
plan data are best for deciding where dose should be placed and how dose should be_x000D_
delivered in an RT plan. The method automatically learns based on RT plan data from_x000D_
previously treated patients and generates a personalized RT plan without requiring any_x000D_
manual intervention._x000D_
We are engaging multiple cancer institutions in Canada to evaluate and contribute to the_x000D_
development of the automated planning method in order to ensure the research has wide_x000D_
clinical applicability and has consensus expert review._x000D_
The proposed research is applicable to all patients receiving RT as part of their cancer_x000D_
management. The research will transform the existing RT process to make better use of_x000D_
limited resources, while still ensuring high quality, personalized, and cost-effective healthcare_x000D_
is accessible for all RT patients., program purpose french : Radiation therapy (RT) is one of the main methods of treating cancer. The delivery of RT is a_x000D_
complicated process that requires both clinical and technical expertise. The process of_x000D_
generating an RT treatment plan, which specifies how the radiation dose for treatment is to be_x000D_
delivered to the patient, relies on complex technology and considerable manual effort often_x000D_
requiring hours to days of dedicated time to plan each patient. We are proposing a method to_x000D_
automatically generate an RT plan that can readily be integrated into the existing clinical RT_x000D_
process._x000D_
The goal of the research is to develop and make available to other institutions an automated_x000D_
RT treatment planning method that will i) rapidly generate RT plans in minutes, expediting the_x000D_
RT process, ii) allow patients greater access to RT based on the expertise of established_x000D_
cancer institutions in Canada, and iii) produce high quality RT plans that are tailored to each_x000D_
specific patient._x000D_
The automated planning method applies state-of-the-art machine learning, image registration,_x000D_
and optimization algorithms to learn which relationships and patterns in RT image and RT_x000D_
plan data are best for deciding where dose should be placed and how dose should be_x000D_
delivered in an RT plan. The method automatically learns based on RT plan data from_x000D_
previously treated patients and generates a personalized RT plan without requiring any_x000D_
manual intervention._x000D_
We are engaging multiple cancer institutions in Canada to evaluate and contribute to the_x000D_
development of the automated planning method in order to ensure the research has wide_x000D_
clinical applicability and has consensus expert review._x000D_
The proposed research is applicable to all patients receiving RT as part of their cancer_x000D_
management. The research will transform the existing RT process to make better use of_x000D_
limited resources, while still ensuring high quality, personalized, and cost-effective healthcare_x000D_
is accessible for all RT patients., agreement title english : Advancing Personalized Cancer Care with an Automated Radiomics-Based Radiation Therapy Method, agreement title french : Advancing Personalized Cancer Care with an Automated Radiomics-Based Radiation Therapy Method, agreement number : CHRPJ, agreement value : 276282.0, agreement start date : 2017-04-25, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year. (2017-2018 to 2020-2021), additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2020-2021), owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada