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Taxonomy: IM/TH- Cone Beam CT: Radiomics
| MO-GH-SAN2-9 | Improved Glioblastoma Survival Prediction Using Deep Learning-Based Radiomic Features From Preoperative Multimodal MR Images J Fu*, K Singhrao , X Zhong , X Qi , Y Yang , D Ruan , J Lewis , UCLA School of Medicine, Los Angeles, CA |
| PO-GePV-I-2 | Developing in Vivo Diffusion and Functional MR Imaging Biomarkers in a Knock-in Mouse Model of DYT1 Dystonia H Liu*, D Vaillancourt , University of Florida, Gainesville, FL |
| SU-E-SAN2-4 | Evaluating the Stability of Radiomics Features Using 4D-CT X Wang1*, C Ma1 , H Wang2 , Y Zhang1 , N Yue1 , K Nie1 , (1) Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, (2) Shanghai Chest Hospital, Shanghai Jiaotong University, Shanghai, China |
| TH-A-SAN2-2 | Prediction of MGMT Status for Newly Diagnosed Glioblastoma Patients Using Radiomics Feature Extraction From 18F-DOPA PET Imaging J Qian*, M Herman , D Brinkmann , N Laack , P Korfiatis , B Kemp , C Hunt , V Lowe , D Pafundi , Mayo Clinic, Rochester, MN |
| TH-C-SAN2-1 | Harmonizing Imaging Protocols: Impact On Radiomics Survival Prediction in Large Patient Cohorts R Ger*, S Zhou , D Mackin , H Elhalawani , B Elgohari , J Meier , C Fuller , R Howell , R Layman , H Li , O Mawlawi , R Stafford , L Court , UT MD Anderson Cancer Center, Houston, TX |
| TU-AB-SAN2-9 | Investigating Radiomics to Predict Positive Lymph Nodes in Oral Cavity Squamous Cell Carcinoma (OSCC) A Traverso12*, A Hosni-Abdalaty2 , M Hasan2 , J Kim2 , J Ringash2 , J Cho2 , S Bratman2 , A Bailey2 , J Waldron9 , M Welch2 , J Irish3 , B O'Sullivan2 , J De Almeida3 , M Giuliani2 , D Chepeha2 , D Goldstein2 , D Jaffray2 , L Wee1 , A Dekker1 , A Hope2 , (1) MAASTRO Clinic, Maastricht, The Netherlands ,(2) Princess Margaret Cancer Centre, Toronto, Canada (3) University Health Network, Toronto, Canada |
| TU-AB-SAN2-11 | ComBat Harmonization for Radiomcs Studies with CT Images R N Mahon1*, M Ghita1 , G D Hugo2 , E Weiss1 , (1) Virginia Commonwealth University, Richmond, VA, (2) Washington University School of Medicine, St. Louis, MO |
| TU-C1030-GePD-F2-5 | Synthetic CTs Generated by Deep Learning Approaches: How Good Are They for Radiomics Analysis? F Tixier*, P Klages , S Riyahi , J Jiang , H Um , N Tyagi , R Young , H Veeraraghavan , Memorial Sloan-Kettering Cancer Center, New York, NY |
| TU-C930-GePD-F6-3 | Standardizing Patient Orientation to Improve Generalization of Radiomics Models A Iyer*, J Oh , M Thor , J Deasy , A Apte , Memorial Sloan Kettering Cancer Center, New York, NY |
| TU-C930-GePD-F6-4 | The Stability of CT Radiomics Feature Using Repeated Scans C Ma1*, (1) Rutgers Cancer Institute of New Jersey |
| WE-AB-221AB-5 | Dual-Energy CT Ventilation & Perfusion Imaging as An Accessible Alternative to Nuclear Medicine Techniques J Korte* , N Bucknell , S Siva , B Woon , P Jackson , T Mulcahy , J Callahan , T Kron , N Hardcastle , Peter MacCallum Cancer Centre, Melbourne, Australia |
