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Keywords: modeling
| MO-B-TRACK 6-1 | A Quantitative Analysis of Lung Elastography Performance Using Large-Deformation CT Scans Acquired at Residual Volume (RV) and Total Lung Capacity (TLC) B Stiehl1*, M Lauria1, L Naumann1, K Singhrao1, I Barjaktarevic2, J Goldin2, M McNitt-Gray3, D Low1, A Santhanam1, (1) Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, (2) Division of Pulmonary and Critical Care, University of California, Los Angeles, Los Angeles, CA, (3) Department of Radiology, University of California, Los Angeles, Los Angeles, CA |
| MO-B-TRACK 6-7 | Use of Short Duration Dynamic [18F]DCFPyL PET and CT Perfusion Imaging to Localize Dominant Intraprostatic Lesions in Prostate Cancer: Validation Against Digital Histopathology D Yang1,2,3*, R Alfano4, G Bauman3,4, J Chin3,4, S Pautler3,4, K Chung2,3, A Ward4, T Lee2,3, (1) Fox Chase Cancer Center, Philadelphia, PA, (2) Robarts Research Institute, University of Western Ontario, London, ON, CA, (3) Lawson Imaging Research Program, Lawson Health Research Institute, London Health Science Centre, London, ON, CA, (4) London Regional Cancer Program, London Health Science Centre, London, ON, CA |
| MO-IePD-TRACK 1-5 | Theoretical Investigation of X-Ray Spectral Shape Impact On Dual-Energy Image Quality: Effects of Dose Allocation, Total Dose, and Energy Ratio I Romadanov, M Sattarivand*, Nova Scotia Health Authority, Halifax, NSCA, |
| MO-IePD-TRACK 2-3 | Fetal Dose in Body CT: Z-Axis Dose Profile Based Calculation Compared to Monte Carlo Simulation X Li*, K Yang, B Liu, Massachusetts General Hospital, Boston, MA |
| MO-IePD-TRACK 4-5 | Exploratory Unsupervised Structure-Learning Based Radiomics Approach for Brain Metastases Treatment Response Modeling of Stereotactic Radiosurgery Z Yang1*, L Wang2, M Chen1, R Timmerman1, T Dan1, Z Wardak1, W Lu1, X Gu1, (1) UT Southwestern Medical Center, Dallas, TX, (2) University Of Texas At Arlington, Arlington, TX |
| MO-IePD-TRACK 5-2 | Capturing Head-And-Neck Planning Trends with a Knowledge-Based Tradeoff Model J Zhang1*, Y Sheng2, Y Ge3, Z Tian1, X Yang1, T Liu1, J Wu2, (1) Emory University, Atlanta, GA, (2) Duke University, Durham, NC, (3) University of North Carolina at Charlotte, Charlotte, NC |
| MO-IePD-TRACK 5-2 | Prediction of Glioblastoma Patient’s Survival After Radiation Therapy with Random Survival Forest Model Y Kim1*, KW Kim2, H Yoon2, W Sung1, (1) The Catholic University of Korea, Seoul, ,KR (2) Yonsei University College of Medicine, Seoul, ,KR |
| MO-IePD-TRACK 6-2 | Using in Vivo EPID Measurements to Model Acute Gastrointestinal Toxicity C Bojechko*, D Simpson, T Nelson, V Moiseenko, University of California San Diego, La Jolla, CA |
| MO-IePD-TRACK 6-6 | Prediction of Therapeutic Outcomes of Patients with Non-Small Cell Lung Cancer for Stereotactic Ablative Radiotherapy Based On a Differential Model E Mai*1, A Hidetada2,T Huy1,Y Shirakawa3, N Kenta1, Y Tadamasa4,S Yoshiyuki5,(1) Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Science, Kyushu University, Fukuoka,JP(2)Division of Medical Quantum Science, Department of Health Sciences, Faculty of Medical Sciences, Kyushu University,Fukuoka,JP(3)Department of Radiation Oncology, National Hospital Organization Kyushu Cancer Center,Fukuoka,JP(4)Department of Radiology, Kyushu University Hospital,Fukuoka,JP(5)Kyushu International Heavy Ion Cancer Treatment Center,Saga,JP |
| PO-GePV-I-11 | 3D Computational Approach to Morphological Brain Abnormalities in Autism S Brindle*, C Saben, R Makkia, University of Mary Washington, Stafford, Virginia |
| PO-GePV-M-10 | Development of Gell: A GPU-Powered Ultra-Large-Scale Cell-Based Simulator J Du1*, Y Zhou2, L Jin2, K Sheng1, (1) UCLA School of Medicine, Los Angeles, CA, (2) UCLA School of Engineering, Los Angeles, CA |
| PO-GePV-M-27 | Group-Lasso Regularized Artificial Neural Network for Embedded Feature Selection in Radiomics Study On Pediatric Patients with Craniopharyngioma Treated with Proton Therapy W Yang*, C Hua, T Davis, J Uh, T Merchant, St. Jude Children's Research Hospital, Memphis, TN |
| PO-GePV-M-31 | Can Clinical Data Uncover Physician’s Decision Making Criteria On Patient’s Prescription in Radiotherapy? Q Li1*, J Wright1, T McNutt1, (1) SOM Rad Onc Physics of Johns Hopkins University Hospital, Baltimore, MD |
| PO-GePV-M-39 | Outcome Prediction of Prostate Cancer Patients After Radiotherapy Using Machine Learning Models Developed with Extrapolation Data K Oguma1*, T Magome2, M Someya3, T Hasegawa4, K Sakata5, (1) Graduate Division of Health Sciences, Komazawa University, Setagaya, Tokyo, JP, (2) Komazawa University, Tokyo, JP, (3) Department Of Radiology, Sapporo Medical University School Of Medicine, (4) Department Of Radiology, Sapporo Medical University School Of Medicine, (5) Department of Radiology, Sapporo Medical University School Of Medicine |
| PO-GePV-M-49 | The Impact of the COVID-19 Pandemic On Cancer Care in Radiation Oncology S Utkarsh1,2*, S Andrea3,4,5, M Gray6, D Wazer1,2, K Leonard1,2, R Munbodh1,2, (1) Department of Radiation Oncology, Warren Alpert Medical School Of Brown University, Providence, RI, (2) Department of Radiation Oncology, Rhode Island Hospital, Providence, RI, (3) Lifespan Biostatistics Core, Rhode Island Hospital, Providence, RI, (4) VA Portland Health Care System, Portland, OR, (5) OHSU-PSU School of Public Health, Portland, OR, (6) Department of Molecular Pharmacology, Physiology, and Biotechnology, Brown University, Providence, RI |
| PO-GePV-M-55 | Assessment of Magneto-Plasmonic Nanoparticles as a Theragnostic for MRI-Based Photothermal Therapy: A Numerical 3-D Modeling Study in Mice zahed tavangari - Iran university of medical sciences |
| PO-GePV-T-57 | Clinical Implementation of An Abdominal Compression Belt On a Double Scattering Proton Machine X Wang1*, Y Zhang1, J Zhu1, K Qing2, K Nie1, T Cui1, N Yue1, B Liu1, (1) Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, (2) City of Hope National Medical Center, Duarte, CA |
| PO-GePV-T-118 | Assess the Interplay Effect Based On the Cyclotron Accelerator Proton Therapy System Machine-Specific Delivery Sequence Model L Zhao1*, G Liu1, J Shen2, A Lee3, D Yan1, R Deraniyagala1, C Stevens1, W Zheng1, X Li1, S Tang3, X Ding1, (1) Beaumont Health System, Royal Oak, MI, (2) Mayo Clinic, Phoenix, AZ, (3) US Oncology, Coppell, TX |
| PO-GePV-T-174 | Machine Learning Based Prediction for Patient-Specific Quality Assurance: Understand the Plan Complexity for VMAT X Zhang*, Y Zhang, B Liu, N Yue, K Nie, Rutgers Cancer Institute of New Jersey, New Brunswick, NJ |
| PO-GePV-T-221 | Validation of the Monte Carlo Model of a Novel Biology-Guided Radiotherapy (BgRT) Linac M Shi1*, K Bush1, C Chuang1, D Zaks2, L Xing1, N Kovalchuk3, M Surucu4, B Han4, (1) Stanford University, Stanford, CA, (2) RefleXion Medical, Hayward, CA, (3) Stanford University Cancer Center, Palo Alto, CA, (4) Stanford University School of Medicine, Stanford, CA |
| PO-GePV-T-410 | Predictive Model of Lung Tumor Intrafraction Motion Variability L Knybel1*, J Cvek1, M Penhaker2, (1) University Hospital Ostrava, Ostrava,CZ, (2) VSB- Technical University Of Ostrava,CZ |
| SU-A-TRACK 6-4 | Predicting Daily Record of Machine Performance Check Using Multi-Long Short-Term Memory Neural Networks M Ma*, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, China, BeijingCN, |
| SU-B-TRACK 6-4 | Managing Tumor Changes During Fractionated Radiotherapy: Evaluation On a Deep-Learning-Based Predictive Model and a Re-Planning Strategy R Li1*, A Roy2, N Kirby1, N Bice1, S Sivabhaskar1, M Fakhreddine1, N Papanikolaou1(1) UT Health San Antonio MD Anderson Cancer Center, San Antonio, Texas, (2) The University of Texas at San Antonio, San Antonio, Texas |
| SU-D-TRACK 4-2 | Automated Predictive Models for Lung ART J Kavanaugh1*, G Hugo1, Z Ji1, J Fontenot4, (1) Washington University School of Medicine, St. Louis, MO, (2) Mary Bird Perkins Cancer Center, Baton Rouge, LA |
| SU-E-TRACK 6-6 | A Radiomics-Boosted Deep Learning Model for COVID-19 and Non-COVID-19 Pneumonia Detection Using Chest X-Ray Image Z Hu1*, Z Yang2, F Yin2, K Lafata2, C Wang2, (1) Duke Kunshan University, Kunshan, Jiangsu, China, (2) Duke University Medical Center, Durham, NC |
| TH-D-TRACK 6-5 | How Fluence-Prediction Error Impact Final Plan Quality: Insights Into a Deep-Learning-Based (DL-Based) Head-And-Neck (H&N) IMRT Planning AI Agent X Li1*, QJ Wu1, Q Wu1, C Wang1, Y Sheng1, W Wang1, H Stephens1, F Yin1, Y Ge2, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC |
| TH-F-TRACK 3-1 | A Dosimetric Evaluation of 223Ra-Dichloride Using a Realistic Model of the Metastatic Castrate-Resistant Prostate Cancer Microenvironment and Whole-Body Pharmacokinetics A Strunets*, D Adam, J Jeffery, B Bednarz, University of Wisconsin, Madison, WI |
| TH-F-TRACK 4-4 | Head-And-Neck IMRT Auto-Planning Through Fluence Map Prediction Using Progressive Growing of Generative Adversarial Networks X Li1*, QJ Wu1, Q Wu1, C Wang1, Y Sheng1, W Wang1, H Stephens1, F Yin1, Y Ge2, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC |
| TH-IePD-TRACK 3-1 | Benchmark of 4DCT Derived Motion Models with a Biomechanical Lung Model for Tumor Position Estimation P Sabouri1*, M Ranjbar2, S Mossahebi3, A Sawant3, P Mohindra3, G Lasio3, L Topoleski2, (1) Miami Cancer Institute, Miami, FL, (2) University of Maryland, Baltimore County, MD, (3) University of Maryland, School of Medicine, Baltimore, MD |
| TU-A-TRACK 6-7 | Survival Prediction Models for Patients with Malignant Pleural Mesothelioma After Adjuvant 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 |
| TU-C-TRACK 3-2 | BEST IN PHYSICS (IMAGING): A GAN-Based Technique for Synthesizing Realistic Respiratory Motion in the 4D-XCAT Phantoms Y Chang*, Z Jiang, P Segars, Z Zhang, F Yin, L Ren, Duke University Medical Center, Cary, NC |
| TU-C-TRACK 6-3 | Features Of TOPAS-NBio – Release 1.0 J Schuemann1*, A McNamara1, J Ramos-Mendez2, N Dominguez-Kondo3, J Perl4, D Yoo1, N Henthron5, J Warmenhoven5, S Ingram5, M Merchant5, K Kirkby5, K Held1, J Laverne5, H Paganetti1, B Faddegon2, (1) Physics Division, Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA, Boston, MA, (2) University of California San Francisco, San Francisco, CA, (3) Benemerita Universidad Autonoma de Puebla, Puebla, Mexico, (4) Stanford Linear Accelerator Center, Menlo Park, CA, (5) Division of Cancer Sciences, Faculty of Biology, Medicine and Health, The University of Manchester and The Christie NHS Foundation Trust, Manchester, United Kingdom, GB, (6) The University of Notre Dame, Notre Dame, IN |
| TU-CD-TRACK 5-0 | Radiomics and Outcome Prediction H Aerts1*, Y Xiao2*, F Yin3*, E Deutsch4*, (1) Dana-Farber/Brigham Womens Cancer Center, Boston, MA, (2) University of Pennsylvania, Philadelphia, PA, (3) Duke University, Chapel Hill, NC, (4) Universite Paris-saclay, Villejuif, FR |
| TU-D-TRACK 6-2 | Modeling DNA Structure with Different Chromatin Compaction in TOPAS-NBio Using Hi-C Representations D Yoo1*, N Henthron2, S Ingram2, M Merchant2, K Kirkby2, J Warmenhoven2, A McNamara1, K Held1, H Paganetti1, J Schuemann1, (1) Department of Radiation Oncology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA (2) Division Of Cancer Sciences, Faculty Of Biology, Medicine and Health, The University of Manchester and The Christie NHS Foundation Trust, Manchester, United Kingdom |
| TU-E-TRACK 5-5 | Modeling of Cellular Response After FLASH Irradiation: A Quantitative Analysis Based On the Radiolytic Oxygen Depletion Hypothesis H Zhu1*, J Li2, X Deng1, R Qiu2, Z Wu2, H Zhang2, (1) Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Gangzhou, Guangdong, CN, (2) Department of Engineering Physics, Tsinghua University, Beijing, CN |
| TU-EF-TRACK 4-3 | Biologically Guided Deep Learning for Post-Radiation PET Image Outcome Prediction: A Feasibility Study of Oropharyngeal Cancer Application C Wang1*, H Ji2, A Bertozzi2, D Brizel1, Y Mowery1, F Yin1, K Lafata1, (1) Duke University Medical Center, Durham, NC, (2) University of California, Los Angeles, Los Angeles, CA |
| WE-C-TRACK 3-6 | Predicting Ion Chamber Disagreements for Patient-Specific Quality Assurance of VMAT Plans Using Machine Learning P Wall*, M Lafreniere, E Hirata, O Morin, G Valdes, A Witztum, University of California San Francisco, San Francisco, CA |
| WE-D-TRACK 6-4 | Synthetic Magnetic Resonance Imaging From Computed Tomography Simulation Scans Using Deep-Learning Models for High-Dose-Rate Prostate Brachytherapy H Kang1*, M Abuhamad2, A Solanki1, J Roeske1, (1) Loyola University Medical Center, Maywood, IL, (2) Loyola University Chicago, ,Chicago, IL |
| WE-IePD-TRACK 2-2 | Evaluating and Improving the Bloch Fitting Method for the Analysis of AcidoCEST MRI T Li1,2*, K Jones1, A Kotrotsou1, S Zhang1, M Pagel1 (1) UT MD Anderson Cancer Center, Houston, TX. (2) The University of Texas Health Science Center at Houston Graduate School of Biomedical Sciences, Houston, TX. |
| WE-IePD-TRACK 4-5 | Prediction of Cardiotoxicity for Breast Cancer Patients Using Light Gradient Boosting Machine Z Jiang1*, P Diao2, Y Liang3, K Dai4, H Li5, H Wang6, Y Chen7, M Lu8, Y Kuang9, (1) University of Nevada, Las Vegas, Las Vegas, NV, (2) Sichuan Cancer Hospital, Chengdu, Sichuan, CN, (3) Sichuan Cancer Hospital, Chengdu, Sichuan, CN, (4) Sichuan Cancer Hospital, Chengdu, Sichuan, CN, (5) Sichuan Cancer Hospital, Chengdu, Sichuan, CN, (6) Sichuan Cancer Hospital, Chengdu, Sichuan, CN, (7) Sichuan Cancer Hospital, Chengdu, Sichuan, CN, (8) Sichuan Cancer Hospital, Chengdu, Sichuan, CN, (9) University of Nevada, Las Vegas, Las Vegas, NV |
| WE-IePD-TRACK 5-2 | Multi-Domain Statistical Modeling of Treatment Tolerance in Patients with Gastric and Esophageal Adenocarcinoma A Toronka*, M Defreitas, B Konkel, M Nedrud, I Zaki, A Valentine, S Cubberley, F Yin, M Bashir, K Lafata, Duke University, Durham, NC |
| WE-IePD-TRACK 5-3 | RayStation Photon Beam Modeling Challenges for Small Targets Treated with VMAT and Dynamic Conformal Arcs J Huang*, S Frigo, University of Wisconsin- Madison, Madison, WI |
| WE-IePD-TRACK 5-4 | A Mechanistic Model for the Calculation of Oxygen Dependent DNA Damage After Radiation Exposure H Zhu1*, J Li2, X Deng1, R Qiu2, Z Wu2, H Zhang2, (1) Department of Radiation Oncology, Sun Yat-sen University Cancer Center, Gangzhou, Guangdong, CN, (2) Department of Engineering Physics, Tsinghua University, Beijing, CN, |
| WE-IePD-TRACK 6-1 | Incorporating Organ Deformation in Biological Modeling and Patient Reported Outcome Study for Permanent Prostate Brachytherapy S To1*, P Mavroidis1, R Chen2, A Wang1, T Royce1, T Zhu1, J Lian1, (1) University of North Carolina, Chapel Hill, NC, (2) University Of Kansas, Kansas City, KS, |
| WE-IePD-TRACK 6-4 | Characterization of the Response of 9L Orthotopic Brain Tumors to 3D Conformal Radiation Therapy Using Contrast Enhanced MRI G Valadie*, S Brown, J Ewing, G Divine, N Tavarekere, K Farmer, G Cabral, Henry Ford Hospital, Detroit, MI |