Grants and Contributions:

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Title:

Advancing Personalized Cancer Care with an Automated Radiomics-Based Radiation Therapy Method

Agreement Number:

CHRPJ

Agreement Value:

$276,282.00

Agreement Date:

Apr 25, 2017 -

Organization:

Natural Sciences and Engineering Research Council of Canada

Location:

Ontario, CA

Reference Number:

GC-2017-Q1-00257

Agreement Type:

Grant

Report Type:

Grants and Contributions

Additional Information:

Grant or Award spanning more than one fiscal year. (2017-2018 to 2020-2021)

Recipient's Legal Name:

Purdie, Thomas (University of Toronto)

Program:

Collaborative Health Research Projects

Program Purpose:

Radiation therapy (RT) is one of the main methods of treating cancer. The delivery of RT is ax000D
complicated process that requires both clinical and technical expertise. The process ofx000D
generating an RT treatment plan, which specifies how the radiation dose for treatment is to bex000D
delivered to the patient, relies on complex technology and considerable manual effort oftenx000D
requiring hours to days of dedicated time to plan each patient. We are proposing a method tox000D
automatically generate an RT plan that can readily be integrated into the existing clinical RTx000D
process.x000D
The goal of the research is to develop and make available to other institutions an automatedx000D
RT treatment planning method that will i) rapidly generate RT plans in minutes, expediting thex000D
RT process, ii) allow patients greater access to RT based on the expertise of establishedx000D
cancer institutions in Canada, and iii) produce high quality RT plans that are tailored to eachx000D
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 RTx000D
plan data are best for deciding where dose should be placed and how dose should bex000D
delivered in an RT plan. The method automatically learns based on RT plan data fromx000D
previously treated patients and generates a personalized RT plan without requiring anyx000D
manual intervention.x000D
We are engaging multiple cancer institutions in Canada to evaluate and contribute to thex000D
development of the automated planning method in order to ensure the research has widex000D
clinical applicability and has consensus expert review.x000D
The proposed research is applicable to all patients receiving RT as part of their cancerx000D
management. The research will transform the existing RT process to make better use ofx000D
limited resources, while still ensuring high quality, personalized, and cost-effective healthcarex000D
is accessible for all RT patients.