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Keywords: Statistical Analysis
MO-E115-IePD-F8-5Case-Based Repeatability of AI Classification On Multi-Modality Imaging of Breast Lesions Using DCE-MRI and FFDM
H Whitney1,2*, K Drukker1, H Li1, A Edwards1, L Lan1, H Abe1, M Giger1, (1) University of Chicago, Chicago, IL, (2) Wheaton College, Wheaton, IL
PO-GePV-M-63Investigating the Expected Impact of Auto-Contouring in Clinical Practice: A Cohort Analysis
D Boukerroui1, J Baker2,3*, Y Mcquinlan1, A Riegel2,3, Y Cao2,3, M Gooding1, L Potters2,3, (1) Mirada Medical Ltd., Oxford, U.K, (2) Department of Radiation Medicine, Northwell Health, Lake Success, NY 11042, (3) Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549
PO-GePV-M-64Investigating Disease-Stage Dependence of MR-Based Radiomic Feature Reproducibility Against Image Perturbations in Nasopharyngeal Carcinoma
T Cheung, S Lam, J Zhang*, J Cai, Hong Kong Polytechnic University, Hong Kong, CN
PO-GePV-T-120A Radiomics-Based Light Gradient Boosting Machine to Predict Radiation-Induced Toxicities in Nasopharynx Cancer Patients Receiving Chemoradiotherapy
Z Jiang1*, Y Liang2, X Wang3, Z Min4, M Feng5, Y Kuang6, (1) University of Nevada, Las Vegas, Las Vegas, NV, (2) Cancer Hospital Chinese Academy of Medical Sciences, Sichuan Center, Chengdu, CN, (3) Radiation Oncology Key Laboratory Of Sichuan Province, Chengdu, CN,(4) Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, CN,(5) Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Chengdu, CN,(6) University of Nevada, Las Vegas, Las Vegas, NV
SU-H330-IePD-F9-6A Radiogenomics Study Using a Network-Based Unbalanced Optimal Mass Transport Method in Head and Neck Squamous Cell Carcinoma
J Oh1, H Veeraraghavan1, E Katsoulakis2, A Apte1, J Zhu3*, Y Yu1, N Lee1, V Hatzoglou1, A Tannenbaum3, N Riaz1, J Deasy1, (1) Memorial Sloan Kettering Cancer Center, New York, NY, (2) Department of Veterans Affairs, Tampa, FL, (3) Stony Brook University, Stony Brook, NY
SU-H430-IePD-F6-4The Medical Imaging and Data Resource Center (MIDRC) Technology Development Project (TDP) 3c: Developing Tools to Assist in Task-Specific Performance Evaluation for Machine Learning Algorithms Employing MIDRC Data
K Drukker1, B Sahiner2, T Hu2, G Kim3, H Whitney1,4, N Baughan1, K Myers5, M Giger1, M McNitt-Gray3*, (1) University of Chicago, Chicago, IL, (2) US Food and Drug Administration, Silver Spring, MD, (3) David Geffen School of Medicine at UCLA, Los Angeles, CA, (4) Wheaton College, Wheaton, IL, (5) US Food and Drug Administration (retired),Phoenix, AZ
WE-C1030-IePD-F3-2A New Independent Outcome Predictor for Lung Stereotactic Body Radiation Therapy Patients, Plan Averaged Beam Modulation
H Liu*, C Li, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Peking University Cancer Hospital & Institute, Beijing, CN