Abstract #0527

Predicting Post-Stereotactic Radiotherapy Magnetic Resonance Images: A Proof-of-Concept Study in Breast Cancer Metastases to the Brain

Shraddha Pandey^1,2, Tugce Kutuk³, Matthew N Mills⁴, Mahmoud Abdalah⁵, Olya Stringfield⁵, Kujtim Latifi⁴, Wilfrido Moreno¹, Kamran Ahmed⁴, and Natarajan Raghunand²

¹Electrical Engineering, University of South Florida, Tampa, FL, United States, ²Department of Cancer Physiology, H.Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States, ³Department of Radiation Oncology, Baptist Health South Florida, Miami, FL, United States, ⁴Department of Radiation Oncology, H.Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States, ⁵Quantitative Imaging Shared Service, H.Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States

Synopsis

Keywords: Tumors, Radiotherapy, Image PredictionStereotactic radiosurgery (SRS) can provide effective local control of breast cancer metastases to the brain while limiting damage to surrounding healthy tissues. Knowledge-based algorithms have been reported that can alleviate the manual aspects of radiation dose planning, but these do not currently provide voxel-level dose prescriptions that are optimized for tumor control and avoidance of radionecrosis and associated toxicity. On the assumption that a voxelwise relationship exists between pre-SRS MR images, the RT dose map, and the resulting post-SRS MR images, we have investigated a deep learning framework to predict the latter from the former two.

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