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Taxonomy: TH- Small Animal RT: Response assessment
| MO-AB-SAN2-0 | AI for Predicting Response J Kalpathy-Cramer1*, M Giger2*, E Koay3*, J Wu4*, (1) ,Boston, MA, (2) University of Chicago, Chicago, IL, (3) MD Anderson, Houston, TX, (4) Stanford University, Palo Alto, CA |
| MO-AB-SAN2-1 | AI for Predicting Treatment Outcomes J Kalpathy-Cramer1*, M Giger2*, E Koay3*, J Wu4*, (1) ,Boston, MA, (2) University of Chicago, Chicago, IL, (3) MD Anderson, Houston, TX, (4) Stanford University, Palo Alto, CA |
| MO-AB-SAN2-2 | Radiomics and Machine Learning in Predicting Response From Medical Imaging J Kalpathy-Cramer1*, M Giger2*, E Koay3*, J Wu4*, (1) ,Boston, MA, (2) University of Chicago, Chicago, IL, (3) MD Anderson, Houston, TX, (4) Stanford University, Palo Alto, CA |
| MO-AB-SAN2-3 | Quantitative Imaging Response Metrics for Hepatobiliary and Pancreatic Cancers J Kalpathy-Cramer1*, M Giger2*, E Koay3*, J Wu4*, (1) ,Boston, MA, (2) University of Chicago, Chicago, IL, (3) MD Anderson, Houston, TX, (4) Stanford University, Palo Alto, CA |
| MO-AB-SAN2-4 | Spearhead Clinically Relevant Radiologic Biomarker Discovery in Precision Oncology with Habitat Imaging J Kalpathy-Cramer1*, M Giger2*, E Koay3*, J Wu4*, (1) ,Boston, MA, (2) University of Chicago, Chicago, IL, (3) MD Anderson, Houston, TX, (4) Stanford University, Palo Alto, CA |
| MO-C930-GePD-F2-1 | Assessment of the Treatment Response of Radiotherapy for Brain Metastases Using Arterial Spin Labeling Imaging c Hou*, G Gong , Y Yin , ShanDong Cancer Hospital, Jinan, 37 |
| MO-E115-GePD-F2-1 | Delta Radiomics: Assessment of Tumor Response for Lung SBRT Patients Using Daily CBCT S Meade1*, G Ge2 , J Molloy3 , D Pokhrel4 , (1) ,Lexington, KY, (2) ,Lexington, KY, (3) Univ Kentucky, Lexington, KY, (4) University of Kentucky, Lexington, KY |
| 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-F2-5 | Role of Mid-Treatment Imaging Biomarkers in Phase II: Adaptive De-Escalation of Radiation Therapy Dose in HPV-Positive Oropharyngeal Carcinoma (ART) P Galavis*, M Tam , S Kim , E Zan , W Wang , K Hu , NYU Langone Health, New York, NY |
| MO-J430-CAMPUS-F3-4 | Impact of Cardiac Substructure Dose for Modeling Radiation Toxicity in the Heart J Niedzielski*, X Wei , T Xu , D Gomez , Z Liao , J Bankson , S Lai , L Court , J Yang , University of Texas-MD Anderson Cancer Center, Houston, TX |
| 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 |
| PO-GePV-T-44 | Physics of Cancer: Measurement and Modelling of Post-Radiotherapy Cell Migration to Assess Metastasis A Walther*, M Merrick , C Weeder , H Akhter , M Mimlitz , A Ekpenyong , Creighton University, Omaha, NE, USA. |
| PO-GePV-T-50 | Intra-Target Dose Surrogates and Treatment Histories as Local Control Predictors for Gamma Knife Stereotactic Radiosurgery of Large Meningioma Y Natsuaki*, BP Ziemer , M Susko , PK Sneed , SE Braunstein , DR Raleigh , L Ma. Dept. of Radiation Oncology, University of California at San Francisco, San Francisco, CA |
| PO-GePV-T-54 | Voxel-Level Analysis of Brain Imaging Changes in Nasopharynx Patients Treated with Proton Therapy D Mirkovic1*, GM Engeseth1,3 , P Yepes2 , R Mohan1 , S Frank1 , A Adair1,2 , Q Wang2 , B Gunn1 , (1) U.T M.D. Anderson Cancer Center, Houston, TX, (2) Rice University, Houston, TX, (3) Haukeland University Hospital, Bergen, Norway |
| PO-GePV-T-303 | Predicting Post-SBRT Pulmonary Function Using 4DCT-Derived Ventilation Imaging N Myziuk1*, G Sakthivel2 , L Foster2 , D Solis3 , I Sala 1, E Castillo4 , T Guerrero1,2,4 , (1) William Beaumont Hospital, Dept. of Radiation Oncology, Royal Oak, MI (2) Oakland University William Beaumont School of Medicine, Rochester, MI, (3) Mary Bird Perkins Cancer Center, Baton Rouge, LA (4) Beaumont Health Research Institute, Royal Oak, MI (6) |
| PO-GePV-T-397 | Minimal Difference Between Fractionated and Single-Fraction Exposure in a Murine Model of Radiation Necrosis A Boria*, C Perez-Torres, Purdue University, West Lafayette, IN |
| SU-E-SAN2-6 | Variations in Feature Combinations Correlated with Radiation Pneumonitis Among Radiomics Software Packages J Foy*, S Armato , H Al-Hallaq , 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-I400-GePD-F4-1 | Combining Radiotherapy and Nano-Immunotherapy to Boost the Abscopal Effect in Cervical Cancer J Wood1,2*, R Mueller1,3,4 , S Yasmin-Karim1,5 , W Ngwa1,5,6 , (1) Dana-Farber Cancer Institute, Brigham and Women's Hospital, Boston, MA (2) University of Veterinary Medicine and Pharmacy in Kosice, Kosice, Slovakia (3) University Medical Center Mannheim, Heidelberg University, Mannheim, Germany (4) Heidelberg University, Heidelberg, Germany (5) Harvard Medical School, Boston, MA (6) University of Massachusetts Lowell, Lowell, MA |
| SU-I430-GePD-F8-1 | Functional Networks in Health and Cancerous Brains Related to Language FMRI Q Li1*, G Ferraro1 , L Pasquini2 , K Peck2 , H Makse1 , A Holodny2 , (1) City College of New York, New York, NY, (2) Memorial Sloan Kettering Cancer Center, New York, NY. |
| SU-I430-GePD-F9-3 | Initial Evaluation of the Use of a Convolutional Neural Network to Determine Coronary Artery Disease Severity Using Computed Tomography Angiography A Podgorsak1, 2*, K Sommer1, 2 , V Iyer3 , M Wilson1 , U Sharma1 , K Kumamaru3 , F Rybicki4 , D Mitsouras4 , E Angel5 , C Ionita1, 2 , (1) SUNY Buffalo, Buffalo, NY, (2) Canon Stroke and Vascular Research Center, Buffalo, NY, (3) Juntendo University, Tokyo, (4) University of Ottawa, Ottawa, ON, (5) Canon Medical Systems, Tustin, CA |
| SU-J400-CAMPUS-F2-2 | Observer-Independent, Hand-Crafted Radiomic Features Predict GBM Patient-Specific Survival E Carver1*, N Wen2 , E Liang3 , J Snyder4 , (1) Wayne State University, Troy, MI, (2) Henry Ford Hospital, Detroit, MI, (3) HFHS, Detroit, ,(4) HFHS, Detroit, |
| SU-J400-CAMPUS-F2-3 | Pre-Treatment Prediction by Hormone Receptor Subtype of Response to Neoadjuvant Chemotherapy in Node-Positive Breast Cancer Patients; a Radiomics Study K Drukker*, A Edwards , C Doyle , J Papaioannou , K Kulkarni , M Giger , University of Chicago, Chicago, IL |
| SU-K-225BCD-3 | Intravoxel Incoherent Motion Imaging and MR Texture Analysis of Healthy and Diseased Tissue During Stereotactic Body Radiotherapy of Liver Cancer B Lewis1*, E Fields1 , S Kim1 , T Kim2 , (1) Virginia Commonwealth University, Richmond, VA, (2) Washington University in St. Louis, Saint Louis, MO |
| 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 |
| SU-L-221AB-7 | Assessment of the Liver Functions in Different Dose Region After SBRT Using HIDA SPECT Scans K C Huang1*, D Long1 , Y Yue1 ,M Tann2 , R Rhome1 , P Maxim1 , S Ellsworth1 , (1) Department of Radiation Oncology, Indiana University - School of Medicine, Indianapolis, IN, (2) Department of Radiology, Indiana University - School of Medicine, Indianapolis, IN |
| TH-A-SAN2-9 | Mid-Treatment Gd-EOB-DTPA-Enhanced MRI and Interleukin 6 Cytokine as Biomarkers of Radiation-Induced Liver Toxicity in Metastatic Liver Patients A Ajdari1*, Y Xie1 , C Richter2 , T Hong1 , T Bortfeld1 , (1) Massachusetts General Hospital, Boston, MA, (2) OncoRay, Dresden, Germany |
| TH-BC-225BCD-4 | Automatic Fiducial Marker Detection in Prostate Cancer MR Images Using Generative Adversarial Networks (GANs) K Singhrao1*, J Fu1 , A Kishan1 , J Lewis1 , (1) Dept. of Radiation Oncology, UCLA, Los Angeles, CA, |
| TH-BC-225BCD-9 | BEST IN PHYSICS (MULTI-DISCIPLINARY): Rapid Multi-Contrast Brain Imaging at 0.35T to Facilitate Tumor Response Assessment and Functional Adaptive Radiation Therapy N. Zakariaei1*, Y. Chen2, S Nejad-Davarani 3, E.M. Haacke1,4, C Glide-Hurst1,3 (1) Wayne State University, Detroit, MI, (2) Wayne State University School of Medicine, Detroit, (3) Henry Ford Cancer Institute, Detroit, MI (4) The MRI Inst. for Biomedical Research, Detroit, MI |
| 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 |
| 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-SAN2-6 | A General Framework of Delta-Radiomics for Treatment Response Prediction H Nasief*, X Li , Medical College of Wisconsin, Milwaukee, WI |
| 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-C1000-GePD-F6-3 | Decision Trees Identifying Factors Affecting Tumor Response to Chemo-Radiotherapy in Head and Neck Cancer Evaluated for Tumor Burden M Surucu1*, I Mescioglu2 , A Block1 , B Emami1 , J Roeske1 , (1) Loyola University Medical Center, Maywood, IL, (2) Lewis University, Romeoville |
| TU-C930-GePD-F6-5 | A Comparison of Different Data Augmentation Methods in Isocitrate Dehydrogenase 1 (IDH1) Mutation Prediction H Xiao1*, Z Chang2 , (1) Duke Kunshan University, Kunshan, Jiangsu,(2) Duke University Medical Center, Durham, NC |
| TU-E-SAN1-2 | Radiation Related Lymphopenia for the Treatment of Pancreatic Cancer: Measurement Derived Data for the Development of a Robust Simulation J Sanders1 , T Jones1 , D Liyanage2 , M Conaway1 , M Peach1 , E Janowski1 , K Wijesooriya1*, (1) University of Virginia, Charlottesville, VA,(2) Albemarle High School, Charlottesville, VA |
| TU-F115-GePD-F2-5 | Radiation Dose Response Model for Ventilation Change Using All Phases of 4DCT E Wallat1*, M Flakus1 , A Wuschner1 , W Shao2 , S Gerard2 , T Patton3 , G Christensen2 , J Reinhardt2 , A Baschnagel1 , J Bayouth1 , (1) University of Wisconsin-Madison, Madison, WI, (2) University of Iowa, Iowa City, IA,(3) University of Denver, Denver, CO |
| TU-HI-221AB-0 | Biomedical Modeling Using Imaging Data P Boyle1*, D Tward2*, D Holdsworth3*, C Grassberger4*, (1) University of Washington, Seattle, WA, (2) The Johns Hopkins University, Baltimore, MD, (3) John P. Robarts Research Instit., London, ON, (4) Massachusetts General Hospital, Boston, MA |
| WE-AB-225BCD-3 | A Composite Deep Learning Architecture for the Joint Prediction of Local Control and Radiation Pneumonitis in Radiotherapy for Non-Small Cell Lung Cancer Patients S Cui*, Y Luo , H Tseng , R Ten Haken , I El Naqa , University of Michigan, Ann Arbor, MI |
| WE-AB-225BCD-6 | Intra-Treatment 18F-FDG PET/CT Radiomic Signature Predicts In-Field Recurrence Following Definitive Chemo-Radiation Therapy for Oropharyngeal Cancer K Lafata*, Y Chang , C Wang , Y Mowery , D Brizel , F Yin , Duke University Medical Center, Durham, NC |
| WE-AB-225BCD-10 | The Influence of SABR Fractionation and Imaging Delay Time On Post-Radiotherapy DCE-MRI Response Assessment in Early Stage Breast Cancer Patients M Mouawad1,2,3*, H Biernaski2 , M Brackstone3,4 , M Lock3,4 , B Yaremko3,4 , O Shmuilovich2 , A Kornecki2 , I Nachum2 , F S. Prato1,2 , R. T. Thompson1,2 , S Gaede1,2,3,4 , N Gelman1,2,4 , (1) Western University, London, ON, (2) Lawson Health Research Institute, London, ON, (3) London Regional Cancer Program, London, ON, (4) London Health Sciences Center, London, ON |
| WE-FG-301-7 | Nanoparticle-Aided Radiotherapy Converts the Tumor Into An In-Situ Vaccine Treating Cancer Metastasis with Major Increase in Survival B Ziberi1, 6*, U Ibeh2 , N Bih3 , M Moreau4 , S Yasmin-Karim5 , W Ngwa6 , (1) Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, (2) Dana Farber Cancer Institute, Boston, MA, (3) Dana Farber Cancer Institute, Boston, MA, (4) Dana Farber Cancer Institute/University of Massachusetts Lowell, Everett, MA, (5) Harvard Medical School, Boston, MA, (6) Harvard Medical School, Boston, MA, (6) University of Tetova, Tetova, North Macedonia |
| WE-FG-304-7 | Automated Quantification of Lymphoma On FDG PET/CT Images Using Cascaded Convolutional Neural Networks A Weisman1*, M Kieler1 , S Perlman1 , R Jeraj1,2 , M Hutchings3 , L Kostakoglu4 , T Bradshaw1 , (1) University of Wisconsin-Madison, Madison, WI, (2) Faculty of Mathematics and Physics, Ljubljana, Slovenia, (3) Rigshospitalet, Copenhagen, Denmark, (4) Mount Sinai Medical Center, New York, NY |
