One-click Automated 3D Treatment Planning for Radiopharmaceutical Therapy - PROJECT SUMMARY/ABSTRACT The Radiopharmaceutical Therapy (RPT) market is projected to surpass the Technitium-99m market by 2025, with an emphasis on metastatic prostate cancer, neuroendocrine tumors, and lymphoma. Driven by well- tolerated treatments and fewer side-effects, experts have estimated 150 new theranostic centers will be needed in the U.S. to deliver an estimated 50,000–200,000 treatment cycles/year. Currently, all RPT treatments administer a standard therapeutic dose despite unique patient physiology and pharmacokinetics. From experience with external beam radiation therapy (EBRT), we know that patient-specific prescriptions based on absorbed dose leads to better patient outcomes. A key technology required to enable this personalization is fast, accurate, whole-body patient-specific dosimetry. In a prior Phase II SBIR, Voximetry was able to integrate our fully benchmarked and IP-protected Monte Carlo dosimetry algorithm into a cost-effective RPT treatment planning solution (TorchTM) by adding additional features such as image registration, contour propagation, and voxel-based pharmacokinetic (PK) modeling. Torch was designed to model uptake and clearance routes of any drug class (e.g., small molecules, peptides, antibodies) and any radionuclide, effectively adding a ‘swiss army knife’ tool into the clinician’s RPT toolkit. Leveraging the gamma rays emitted by the RPT agent, Torch can calculate how much radiation energy is deposited within each voxel of organs and tumors throughout the body. Estimates of voxel-level whole-body patient-specific dosimetry are better correlated with response than the standard uptake value (SUV) information that is clinically available today. Using this approach, Voximetry has developed a fast, accurate, RPT treatment planning solution aimed to inform clinicians with extremely accurate voxel-based Monte Carlo (MC) dosimetry in a matter of minutes. Additionally, Vox has developed a GPU accelerated Monte Carlo SPECT reconstruction algorithm that leads to dosimetry estimates which differ from conventional reconstruction algorithms by at least 25%, which is seen as clinically significant. This information will support clinical decisions to personalize safe therapeutic doses. Automation of this RPT dosimetry workflow is especially important for healthcare systems that would like to implement dosimetry-guided therapy in clinical practice but are currently ill-equipped in terms of expertise and resources to perform advanced dosimetry for RPT. By enabling automation, Vox will ultimately benefit cancer patients by making available a personalized treatment that targets metastatic cancer that in many cases is more efficacious and has fewer side effects than chemotherapy. The specific aims that will be accomplished in the proposal are to (1) Develop automated segmentation tools for organs and risk and tumors using artificial intelligence, (2) develop scanner calibration, partial volume correction, and Monte Carlo reconstruction to ensure accurate SPECT imaging, and (3) automate, implement, and validate end-to-end clinical Torch workflow. The successful completion of these aims will help break down barriers to commercialization and accelerate market adoption of Torch.
