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Taxonomy: TH- response assessment : PET imaging-based
| MO-K-SAN2-2 | BEST IN PHYSICS (MULTI-DISCIPLINARY): Predictive Capability of 18F-FDG PET/CT Imaging On Tumor Voxel Dose Response S Chen*, D Krauss , D Yan , William Beaumont Hospital, Royal Oak, MI |
| MO-K-SAN2-5 | An Encoder-Decoder Based Convolutional Neural Network (ED-CNN) for PET Image Response Prediction Using Pre-RT Information: A Feasibility of Oropharynx Cancer IMRT Y Chang1*, K Lafata2 , C Liu3 , C Wang4 , Y Cui5 , L Ren6 , X Li7 , Y Mowery8 , D Brizel9 , F Yin10 , (1) Duke University Medical Center, Durham, NC, (2) Duke University Medical Center, Durham, NC, (3) Duke Kunshan University, Suzhou, Jiangsu, (4) Duke University Medical Center, Durham, NC, (5) Duke University Medical Center, Durham, NC, (6) Duke University Medical Center, Cary, NC, (7) Duke University Medical Center, Durham, NC, (8) Duke University Medical Center, Durham, ,(9) Duke University Medical Center, Durham, ,(10) Duke University Medical Center, Durham, NC |
| SU-L-221AB-1 | Effect of Uncertainties in 18F-FDG PET/CT Imaging Feedback On Treatment Response Assessment and Dose Painting S Chen*, J Liang , A Qin , D Krauss , D Yan , William Beaumont Hospital, Royal Oak, MI |
| SU-L-221AB-6 | Deep Learning to Predict Dosimetric Metabolic Response Map Using Longitudinal 18F-FDG PET/CT Images for Pancreatic Cancer Patients Y Yue1*, K Huang1 , P Maxim1 , S Ellsworth1 , R Tuli2 , (1) Indiana University- School of Medicine, Indianapolis, IN(2) Memorial Sloan-Kettering Cancer Center, New York, NY |
| TH-C-SAN2-3 | Dose-Specific PET Image-Based Outcome Prediction: A Deep Learning Study for Oropharyngeal Cancer IMRT Application C Liu*, C Wang , K Lafata , Y Chang , Y Cui , F Yin , Duke University Medical Center, Durham, NC |
