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|PO-GePV-M-52||The Dataset Heterogeneity Matters: A Machine Learning Study of Dataset Conformation Effects On Model Performance for Dose Deliverability Prediction|
P Quintero1,2*, D Benoit1, Y Cheng1, C Moore2, A Beavis2, (1) University Of Hull, ,UK, (2) Queens Centre for Oncology & Haematology, Cottingham, ,UK
|PO-GePV-M-196||Effect of Scattered X -Rays From Megavoltage Photon Beam On Markerless Tumor Tracking in Dual Energy Imaging|
M Kaur1*, J Luce1, H Mostafavi2, M Lehmann2, D Morf2, L Zhu2, H Kang1, M Walczak2, M Harkenrider1, J Roeske1, (1) Department of Radiation Oncology, Stritch School of Medicine, Cardinal Bernadin Cancer Center, Loyola University Chicago, Maywood, IL,(2) Varian Medical Systems, Palo Alto, CA
|PO-GePV-T-242||Identifying Poor SBRT Plan Performance Using IMRT QA and Complexity Metrics|
M Glenn*, E Ford, M Kim, University of Washington, Seattle, WA
|SU-E-207-5||Cross-Site Generalizability of the Optimal Machine Learning Method for Prostate Cancer Diagnosis|
S Hu1, J Zhang2, X Teng2*, J Cai2, (1) Basis Scottsdale, Scottsdale, AZ, USA, (2) The Hong Kong Polytechnic University, Hong Kong, CN
|TH-A-206-2||Effect of Various DCE-MRI Image Parameters On AI Assessment of Breast Parenchymal Enhancement|
L Douglas*, A Edwards, H Abe, M Giger, University of Chicago, Chicago, IL
|TU-D1030-IePD-F1-1||Automated Patient Positioning Error Detection with Orthogonal Setup DRR and Treatment KV Radiograph Image Pairs|
J Charters*, R Petragallo, D Luximon, J Neylon, D Low, J Lamb, University of California, Los Angeles, Los Angeles, CA