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Taxonomy: TH- Response Assessment: Modeling: Machine Learning

PO-GePV-M-351Three-Dimensional Dose-Function Data-Based Deep Convolutional Neural Network for Predicting Pulmonary Toxicity After Radiotherapy for Lung Cancer
Y Fujita1*, Y Nakajima1, D Kawahara2, T Kimura3, T Yamamoto4, (1) Komazawa University, Tokyo, JP, (2) Hiroshima University, Hiroshima, JP, (3) Kochi University, Kochi, JP, (4) UC Davis School of Medicine, Sacramento, CA
PO-GePV-T-112An Interpretable Machine Learning Model for Predicting Overall Survival for Malignant Mesothelioma Patients Following Radiotherapy
Z Wang1*, V Li2, X Qi3, (1) UCLA School of Medicine, Los Angeles, CA, (2) USC Dornsife College of Letters, Arts and Sciences, Los Angeles, CA, (3) UCLA School of Medicine, Los Angeles, CA
SU-F-BRC-2Using a Residual Neural Network to Predict Radiation-Induced Pulmonary Function Damage
E Wallat1*, M Flakus1, A Wuschner1, J Reinhardt2, G Christensen2, J Bayouth1, (1) University of Wisconsin, Madison, WI, (2) University of Iowa, Iowa City, IA
SU-H400-IePD-F5-3A Comparative Study of Radiomics and Deep-Learning Approaches for Predicting Surgery Outcomes in Early-Stage Non-Small Cell Lung Cancer (NSCLC)
H Zhang1, Z Yang2, B Ackerson2, Z Hu1, M Khazaieli2, C Kelsey2, F Yin2, C Wang2*, (1) Duke Kunshan University, Kunshan, Jiangsu, China, (2) Duke University, Durham, NC
SU-H430-IePD-F7-3Do Machine Learning-Based Models Perform Better Than Clinical Models in Predicting Biochemical Outcome for Prostate Cancer Patients?
L Sun1,2*, H Quon1,2, W Smith1,2, and C Kirkby1,3 (1) University of Calgary, Calgary, AB, CA (2) Tom Baker Cancer Centre, Calgary, AB, CA (3) Jack Ady Cancer Centre, Lethbridge, AB, CA
SU-J-202-6Machine-Learning Modeling of Patient-Reported Swallowing Dysfunction Using Dose to the Epiglottis and Pharyngeal Constrictors
MD Pepin*, S Anaya, RL Foote, YI Garces, H Emily, DW Mundy, MA Neben-wittich, DM Routman, S Catherine, SC Lester, DJ Ma, S Shiraishi, Mayo Clinic Rochester, Rochester, MN
TH-D-BRC-6Outcome Prediction for Metastatic Lung Cancer Patients After Radiotherapy and Immunotherapy Using Patient, Tumor, Treatment, and Immunologic Factors
Y KIM1*, I Chamseddine2, C Grassberger2, Y Cho3, W Sung1, (1) Department of Biomedical Engineering and Department of Biomedicine & Health Science, College of Medicine, The Catholic University of Korea, Seoul, South Korea (2) Department of Radiation Oncology, Massachusetts General Hospital/Harvard Medical School, United States (3) Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Seoul, Republic of Korea