Machine learning-based radiation toxicity mitigation in pediatric brain cancer

Project Summary Radiation therapyRThas a proven record of efficacy in treating many forms of pediatric brain tumorsHoweverit is associated with long term side effects due to damage to healthy tissueThis is especially important in the developing brainwhere long term deficits can be seen in the areas of intelligenceattentionmemory and psychomotor processingTo mediate these deficitsthere has been a push away from whole brain irradiation to more targeted treatment by using dose painting intensity modulated radiation therapyDP IMRTHoweverin order to use these techniquesmore information about how dosing to organs at riskOARsaffects outcomesincluding volumetric changes in the brainVoxel Healthcare LLCformerly Advanced Medical Systems LLCis the developer of ClickBrainan automatic pediatric MR brain segmentation tool that uses cloud based deep learningGoogle TensorFlowtechnology for radiology clinical decision supportIn Aimawe extend ClickBrain to ClickBrain RTa system that will combine ClickBrainandapos s pre treatment brain structure segmentation outputs with radiation planning CTs and MRs to calculate dosing to OARsClickBrain RT will also segment longitudinal MRIsmonthmonthsyearyearsto track outcomes via volumetric changesWe will use OAR dosingdemographicstumor type and gradechemotherapy informationOAR and tumor volumetric measurements to predict tumor and OAR volumetric outcomesWe will adapt our existing version of a multi time point machine learning technique to do this prediction taskIn Aimba user interface for this cloud computing based proof of concept system will be built to allow the RT planner to import patient information and see changes in predicted longitudinal post RT OAR and tumor volumesbased on adjusting OAR dosages for a particular patientOur initial validationAimwill focus on an existing database ofgerm cell tumor patients acquired as part of standard of care and previous studies at Childrenandapos s Hospital Los AngelesGerm cell tumors have relative uniform size and location and provide an ideal dataset to validate our proof of concept systemOur long term goal for ClickBrain RT is to train the machine learning algorithm to provide optimized recommended OAR dosage ranges based on patient history and tumor informationOur software will allow radiation oncologists to optimize treatment and vastly improve long term quality of life in pediatric brain tumor survivors Narrative Mitigating radiation toxicity due to radiation therapy for the treatment of pediatric brain tumors is important to avoid long term developmental side effectsOur ClickBrain RT software will use cloud computing based deep learning technologies to automatically delineate key structures in the brain and to train a machine learning algorithm to predict volumetric changes due to treatment related radiation dosing to these key structuresThis will enable physicians to better avoid unnecessary radiation doses that may cause long term deficits in children with brain tumors