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

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Radiation therapy (RT) is one of the main methods of treating cancer. The delivery of RT is acomplicated process that requires both clinical and technical expertise. The process ofgenerating an RT treatment plan, which specifies how the radiation dose for treatment is to bedelivered to the patient, relies on complex technology and considerable manual effort oftenrequiring hours to days of dedicated time to plan each patient. We are proposing a method toautomatically generate an RT plan that can readily be integrated into the existing clinical RTprocess.The goal of the research is to develop and make available to other institutions an automatedRT treatment planning method that will i) rapidly generate RT plans in minutes, expediting theRT process, ii) allow patients greater access to RT based on the expertise of establishedcancer institutions in Canada, and iii) produce high quality RT plans that are tailored to eachspecific patient.The automated planning method applies state-of-the-art machine learning, image registration,and optimization algorithms to learn which relationships and patterns in RT image and RTplan data are best for deciding where dose should be placed and how dose should bedelivered in an RT plan. The method automatically learns based on RT plan data frompreviously treated patients and generates a personalized RT plan without requiring anymanual intervention.We are engaging multiple cancer institutions in Canada to evaluate and contribute to thedevelopment of the automated planning method in order to ensure the research has wideclinical applicability and has consensus expert review.The proposed research is applicable to all patients receiving RT as part of their cancermanagement. The research will transform the existing RT process to make better use oflimited resources, while still ensuring high quality, personalized, and cost-effective healthcareis accessible for all RT patients.