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Abstract #2745

A Deep Learning Approach for Automated Volume Delineation on Daily MRI Scans in Glioblastoma Patients

Adrian Lazaro Breto1, Kaylie Cullison2, Kolton Jones3, Olmo Zavala-Romero4, John C Ford1, Eric A Mellon1, and Radka Stoyanova1
1Radiation Oncology, University of Miami Sylvester Comprehensive Cancer Center, Miami, FL, United States, 2University of Miami Miller School of Medicine, Miami, FL, United States, 3West Physics, Atlanta, GA, United States, 4Department of Earth, Ocean, and Atmospheric Sciences, Florida State University, Tallahassee, FL, United States


Identifying early progressors following treatment for Glioblastoma (GBM) is paramount in GBM management. MRI-RT platforms provide opportunity for daily MRI of patients. We identified early changes in tumor volume typically starting week 3 or 4 of treatment. We hypothesize tumor volume kinetics are associated with outcome and allow for adapting treatment. We develop a deep learning solution for automatic volume delineation on daily scans, allowing real time monitoring of tumor changes and reducing time burden of segmentation. We obtained DSC for tumor lesion and resection cavity on training and test datasets (mean±standard deviation) 0.87±0.128 and 0.9±0.122; 0.74±0.233 and 0.8±0.277, respectively.

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