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|MO-IePD-TRACK 2-4||Exploration of the Predictive Value of Delta Radiomics Texture Features Extracted From Low-Field Magnetic Resonance Images in Pancreas Stereotactic Body Radiotherapy Patients|
G Simpson*, W Jin, B Spieler, J Ford, L Portelance, E Mellon, D Kwon, F Yang, N Dogan, University of Miami, Miami, FL
|MO-IePD-TRACK 4-4||Treatment Response Prediction for MRI-Guided Adaptive Radiation Therapy of Pancreatic Cancer Using Multiscale Wavelet-Based Delta-Radiomics|
H Nasief*, W Hall, X Chen, E Paulson, B Erickson, X Li, Medical College of Wisconsin, Milwaukee, WI
|MO-IePD-TRACK 4-6||CT-Based Deep Learning Radiomics for Predicting Chemoradiation Treatment Response in Locally Advanced Rectal Cancer|
J Fu1*, Z Wang1, K Singhrao1, J Lewis2, X Qi1, (1) Department of Radiation Oncology, UCLA, Los Angeles, CA, (2) Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA
|SU-IePD-TRACK 1-4||Recurrence Prediction for Head and Neck Squamous Cell Cancer Patients Using Local Binary Pattern-Based Dosiomics|
H Kamezawa1*, H Arimura2, (1) Teikyo University, Omuta, JP, (2) Kyushu University, Fukuoka, JP
|SU-IePD-TRACK 1-7||Super-Resolution CT Image Via Convolution Neural Network with An Observer Loss Function|
M Yu*, M Han, J Baek, Yonsei University, Incheon, 28KR,
|WE-F-TRACK 6-5||Quantitative Radiomics Approach to Assess Acute Radiation Dermatitis in Breast Cancer Patients|
S Park1*, J Park2, J Kim2, C Choi2, J Kim2, (1) Department of Radiation Oncology, Veterans Health Service Medical Center, Seoul, Republic of Korea (2) Department of Radiation Oncology, Seoul National University Hospital, Seoul, Republic of Korea,