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| MO-E115-GePD-F2-3 | Predicting Acute-Phase Weight Loss Based On CT Radiomics and Dosiomics in Lung Cancer Patients Treated with Radiotherapy S Lee , P Han , R Hales , K Voong , T McNutt , J Lee*, Johns Hopkins University, Baltimore, MD |
| MO-E115-GePD-F2-4 | Prediction of Acute Xerostomia Based On Delta Radiomics From CT Images During Radiation Therapy for Nasopharyngeal Cancer Yanxia LIU1*, Hongyu SHI1, Sijuan Huang2, Xiaochuan CHEN1, Huimin ZHOU2,3, Hui CHANG2, Yunfei XIA2, Guohua WANG1, Xin Yang2. (1) School of Software Engineering, South China University of Technology, Guangzhou, Guangdong, 510006, China. (2) Sun Yat-sen University Cancer Center; State Key Laboratory of Oncology in South China; Collaborative Innovation Center for Cancer Medicine; Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, Guangdong, 510060, China. (3) Department of Oncology, the Seventy-fourth Group Army Hospital of the Chinese People's Liberation Army, Guangzhou, Guangdong, 510318, China. |
| MO-E115-GePD-F9-3 | Investigation of Origin and Clinical Impact of Different Artifacts in Thorax CT E Lavdas1*, M Papaioannou2 , A Tsikrika3 , E Pappas4 , G Sakkas5 , V Roka6 , S Kostopoulos7 , S Stathakis8 , N Papanikolaou9 , P Mavroidis10 , (1) University of West Attica, Athens, ,(2) Animus Kyanoys Stavros, Larissa, ,(3) General University Hospital of Larissa, Larissa, ,(4) Animus Kyanoys Stavros, Larissa, ,(5) University of Thessaly, Trikala, ,(6) Health Center of Farkadona, Trikala, ,(7) University of West Attica, Athens, ,(8) University Of Texas Health, San Antonio, TX, (9) University of Texas HSC SA, San Antonio, TX, (10) Univ North Carolina, Chapel Hill, NC |
| 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 |
| 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 |
| PO-GePV-I-11 | Transfer Learning Based Deformable Image Registration in Pancreatic SBRT C Wessels*, C Rao , N Givehchi , S Scheib , Varian Medical Systems, Baden - Daettwil |
| PO-GePV-M-22 | Brain Tumor Segmentation Basedon Features Extracted From MRI Multimodal Images Using Deep Convolution NeuralNetworks B Zhang*, H Lin , Z Xue , j xu , B Liu , Z Wei , School of Electronic Science Application Physics,Hefei University of Technology, Hefei, China |
| SU-E-221AB-6 | Normalizing the Response of a Fixed Geometry EPID Using a Flattening Phantom On a Ring Gantry Linear Accelerator J Chapman*, E Laugeman , B Sun , N Knutson , S Goddu , G Hugo , S Mutic , B Cai , Washington University School of Medicine, St. Louis, MO |
| SU-E-221CD-2 | Analysis of T2-Relaxation-Diffusion Correlation in Glioblastoma Using Classified Clusters Y Li*, T Lawrence , Y Cao , Department of Radiation Oncology, The University of Michigan, Ann Arbor, MI |
| SU-E-SAN2-2 | Comparison in Classification Performance of Radiation Pneumonitis Between Two Delta Radiomics Logistic Regression Models J Foy*, H Al-Hallaq , S Armato , The University of Chicago, Chicago, IL |
| SU-G300-SPS-F4-7 | Prediction of Acute Xerostomia in Nasopharyngeal Cancer for Radiotherapy Using 3D Convolutional Neural Network Y LIU1*, X CHEN2 , s Huang3 , H SHI4 , H ZHOU5 , H CHANG6 , Y XIA7 , X Yang8 , (1) School of Software Engineering, South China University of Technology, Guangzhou, ,(2) School of Software Engineering, South China University of Technology, Guangzhou,(3) State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, (4) School of Software Engineering, South China University of Technology, Guangzhou, ,(5) the 74th Group Army Hospital of the Chinese People's Liberation Army, Guangzhou, ,(6) SYSUCC, Guangzhou, ,(7) SYSUCC, Guangzhou, ,(8) Sun Yat-Sen University Cancer Center (SYSUCC), Guangzhou City |
| SU-I300-GePD-F8-1 | A Deep Learning Approach On Non Alcoholic Fatty Liver Disease Diagnosis Utilizing Ultrasound B-Mode Images and Liver Biopsy as Gold Standard I Gatos1 , S Tsantis1 , P Drazinos2 , P Zoumpoulis2 , I Theotokas2 , P Katsakiori1 , D Mihailidis3 , J Hazle4 , G C Kagadis1, 3*, (1) University of Patras, Rion, Greece, (2) Diagnostic Echotomography, Athens, Greece, (3) University of Pennsylvania, Philadelphia, PA, USA, (4) UT MD Anderson Cancer Center, Houston, TX, USA |
| SU-I300-GePD-F8-5 | The Feasibility of MVCT-Based Radiomics for Delta-Radiomics in Head and Neck Cancer K Abe1,2*, N Kadoya2 , S Tanaka2 , Y Nakajima1,2 , S Hashimoto1 , T Kajikawa2 , K Karasawa1 , K Jingu2 , (1) Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital, Tokyo, Japan,(2) Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Sendal, Japan |
| SU-I330-GePD-F6-4 | On-Board Imaging with High Energy MeV Proton Radiographic Imaging Using a Proton Therapy System G Wright1 , N Alsbou2, S Ahmad1 ,I Ali1 , (1) University of Oklahoma Health Sciences, Oklahoma City, OK, (2) Department of Engineering and Physics, University of Central Oklahoma, Edmond, OK |
| SU-I330-GePD-F9-2 | An Analysis of the Commercially Available CBCT Based On Iterative Reconstruction Algorithm for IGRT S Kang1,3*, J Chung1 , S Lee2 , S Kang3 , T Suh3 , J Kim1 , (1) Seoul National University Bundang Hospital, Seongnam Si, Korea, (2) Konyang University of Korea, Daejeon, Korea,(3) The Catholic University of Korea, College of Medicine, Seoul, Korea |
| SU-I400-GePD-F5-6 | 3D Dose Prediction Model for Head and Neck Cancer Patients by Combining Field Geometry Information with Patient Images E Czeizler*, M Hakala , S Basiri , E Kuusela , Varian Medical Systems Finland, Helsinki, 18 |
| SU-J400-CAMPUS-F1-2 | Combined Use of Gray Matter Volume and Quantitative Susceptibility Mapping to Predict Early Alzheimers Disease Using a Machine Learning-Based Optimized Combination-Feature Set HK Kim1 , HY Rhee2 , CW Ryu3 ,GH Jahng3*, (1) Radiology, Kyung Hee University Hospital, Seoul,Korea ,(2) Neurology, Kyung Hee University Hospital at Gangdong, Seoul,Korea ,(3) Radiology, Kyung Hee University Hospital at Gangdong, Seoul,Korea |
| SU-K-303-2 | Development of a Microwave Imaging System with a Holographic Back-Projection Reconstruction Algorithm J Branscum 1, I Ali2, S Ahmad2 , N Alsbou1, (1) Department of Engineering and Physics, University of Central Oklahoma, Edmond, OK, (2) University of Oklahoma Health Sciences Center, Oklahoma City, OK, |
| 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 |
| SU-L-221CD-4 | Feasibility of Different Prognostic Prediction Models for Lung Cancer Stages I-IIIB Based On Radiomic Signatures K Ninomiya*, H Arimura , Kyushu UniversityFukuoka |
| TH-A-225BCD-1 | Integrating Computer Vision and Non-Linear Optimization for Automated Deformable Registration of Three-Dimensional Medical Images KT Huang*, Pymedix, Inc., Northfield, IL |
| 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 |
| TU-AB-SAN2-3 | A Deep Learning Method for Xerostomia Prediction in Head-And-Neck Radiotherapy K Men*, H Geng , H Zhong , Y Fan , A Lin , Y Xiao , University of Pennsylvania, Philadelphia, PA 19104, USA |
| 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-C1000-GePD-F6-1 | A Deep Sequential Learning Architecture for Xerostomia Prediction in Parotid Glands Using CBCT and Rigid-Registered Dose Images H Tseng1*, B Rosen2, JT Chien3, M Mierzwa4, R Ten Haken5, I El Naqa6 , (1) University of Michigan, Ann Arbor, Ann Arbor, MI, (2) University of Michigan, Ann Arbor, MI, (3)National Chiao Tung University, Hsinchu, Taiwan (4) University of Michigan, Ann Arbor, MI, (5)University of Michigan, Ann Arbor, MI, (6)University of Michigan, Ann Arbor, MI |
| 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-E-SAN2-5 | Fully Automated Segmentation of 33 Abdominal Structures Using Deep Learning - Implications for Radiotherapy Dose Estimation A Weston*, P Korfiatis , K Philbrick , P Kostandy , A Zeinoddini , A Boonrod , N Takahashi, M Moynagh , B Erickson , Mayo Clinic, Rochester, MN |
| TU-L-304-3 | A Novel Contrast CT Based Quantitative Characterization of Surgical Resectability in Pancreatic Cancer Y Lao1*, J David2 , Z Fan2 , K Sheng1 , A Shiu3 , E Chang3 , R Tuli4 , W Yang3 , (1) UCLA School of Medicine, Los Angeles, CA, (2) Cedars Sinai Medical Center, Los Angeles, CA, (3) University of Southern California, Los Angeles, CA, (4) MSKCC,New York, NY |
| TU-L-304-7 | Standardization in Quantitative Imaging: A Comparison of Radiomics Feature Values Obtained by Different Software Packages On a Set of Digital Reference Objects M McNitt-Gray1*, S Napel2 , J Kalpathy-Cramer3 , A Jaggi2 , D Cherezov4 , D Goldgof4 , H Yang5 , E Jones6 , M Muzi7 , N Emaminejad1 , M Wahi-Anwar1 , Y Balagurunathan8 , M Abdalah8 , B Zhao5 , L Hadjiiski9 , L Pierce7 , K Farahani10 , (1) David Geffen School of Medicine at UCLA, Los Angeles, CA, (2) Stanford Univ School of Medicine, Stanford, CA, (3) Massachusetts General Hospital, Boston, MA, (4) University of South Florida, Tampa, FL, (5) Columbia University, New York, NY, (6) UCSF, San Francisco, CA, (7) University of Washington, Seattle, WA (8) Moffitt Cancer Center, Tampa, FL, (9) University of Michigan, Ann Arbor, MI, (10) National Cancer Institute, Bethesda, MD |
| WE-C1030-GePD-F2-3 | Multitask-Based Supervised Deep Learning Using Contrast-Enhanced CT (CECT) Images for Hepatocellular Carcinoma (HCC) Intrahepatic Progression Risk Analysis L Wei1*, D Owen2 , M Mendiratta-Lala3 , B Rosen2 , K Cuneo2 , T Lawrence2 , R Ten Haken2 , I El Naqa2 , (1) Applied Physics Program, University of Michigan, Ann Arbor, MI, (2) Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, (3) Department of Radiology, University of Michigan, Ann Arbor, MI |
| WE-C1030-GePD-F5-2 | Automated Detection and Segmentation of Lung Tumors Using Deep Learning C Owens1,2*, D Rhee1,2 , D Fuentes3 , C Peterson2,4 , J Li5 , M Salehpour1 , L Court1,2,3 , J Yang1,2 , (1) Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, (2) The University of Texas Graduate School of Biomedical Sciences at Houston, Houston, TX, (3) Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, (4) Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, (5) Department of Radiation Oncology, The University of Texas M.D. Anderson Cancer Center, Houston, TX |
| WE-C1030-GePD-F5-6 | How Many Sample Sizes Are Appropriate for Deep Learning Based Auto Segmentation for Head and Neck Cancer? F Yingtao , W Hu*, J Wang , S Chen , S Sun , Z Zhang , Fudan University Shanghai Cancer Center, Shanghai |
| WE-C1030-GePD-F8-1 | Automated High Contrast Resolution Test Analysis for MRI Daily QC J Jimenez*, W Stefan , J Yung , D Reeve , R Stafford , J Hazle , UT MD Anderson Cancer Center, Houston, TX |
| WE-C930-GePD-F5-4 | Detecting Significant Nodal Volume Shrinkage During Treatment in Head-And-Neck Radiotherapy Using Image Saliency Y Hu*, C Polvorosa , C Tsai , S Fontenla, G Mageras , M Hunt , Memorial Sloan Kettering Cancer Center, New York, NY |
| WE-C930-GePD-F8-3 | Interactive Deep Learning-Based Delineation of Gross Tumor Volume for Post-Operative Glioma Patients M Nordstrom1,2*, J Soderberg2 , N Shusharina3 , D Edmunds3 , F Lofman2 , H Hult1 , A Maki1 , T Bortfeld3 , (1) Royal Institute of Technology, Stockholm, (2) RaySearch Laboratories, Stockholm, (3) Massachusetts General Hospital, Boston |
| WE-FG-304-8 | Automated Registration-Based Longitudinal Lesion Matching On PET/CT V Santoro-Fernandes1*, D Huff1 , M Albertini2 , R Jeraj1,2,3 , (1) University of Wisconsin-Madison, Madison, WI, (2) University of Wisconsin Carbone Cancer Center, Madison, WI, (3) University of Ljubljana, Ljubljana, Slovenia |
| WE-FG-304-9 | Improve Deformable Imaging Registration Accuracy Using Pulmonary Vascular Extraction for Lung CT Images D Yang*, Y Fu , X Wu , H Li , Washington University School of Medicine, St. Louis, MO |
| WE-HI-303-4 | Reliability of Doppler Blood Flow Evaluation of Neurovascular Bundle Vessels in Patients Receiving Prostate Radiotherapy X He*, X Yang , A Jani , J Sohn , P Patel , W Curran , T Liu , Emory University, Atlanta, GA |
| WE-J-303-3 | A Multi-Layer Perception Based Method for Thyroid Imaging Reporting and Data System Class-4 Thyroid Nodules Diagnosis T Wang*, W Lu , L Shi , J Qiu , K Hou , H Zhao , W Lu , Taishan Medical University, Taian, Shandong |
