Although there is a rich resource of established procedures to ensure the delivery of safe radiation therapy, urgent work remains to be done to improve quality in radiation therapy. ROI is funding research to address the lack of quality metrics that capture the quality of the planning and delivery process for individual patients.
Predictive Modeling for Toxicities in Head and Neck Cancer from Oncospace
Todd McNutt, PhD
The Johns Hopkins University School of Medicine
ROI awarded Dr. McNutt a grant to use big data and machine learning approaches to develop better predictive models for toxicities associated with head and neck cancer. The ultimate goal is to integrate these predictions into a clinical decision support tool to assist with more personalized treatment planning and clinical interventions. Read more about Dr. McNutt’s project and Oncospace, a complex system that assembles and analyzes data from the treatment planning system and patients’ clinical records.
Ensuring Safe Delivery of IMRT Using EPID-Based Real-Time Verification
Peter Greer, PhD
Calvary Mater Newcastle Hospital and University of Newcastle, Australia
For its inaugural research award, ROI selected Dr. Greer and his team to conduct the the first clinical demonstration of their WatchDog system that determines treatment delivery accuracy for radiation therapy patients in real-time and can prevent mistreatment. The system uses imaging equipment present on all linear accelerators and could have a major impact on radiation oncology safety and accuracy. Learn more about current trials of the system, publications and presentations.