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A B C D E F G H I J K L M N O P Q R S T U V W X Y Z # | show all

Keywords: Modeling
BReP-SNAP-I-5An Empirical Comparison of Weka Classifiers for Outcome Prediction Using An Imaging Habitats Definition and Feature Extraction Method On MRI
Q Han1*, R Palm2, K Latifi2, E Moros2, A Naghavi2, G Zhang2, (1) University of South Florida, Tampa, FL, (2) H. Lee Moffitt Cancer Center, Tampa, FL
BReP-SNAP-I-19Estimation of Tumor Tracer Kinetics Employing a Novel Cross Voxel Exchange Model
N Sinno1*, E Taylor1, M Milosevic1, D Jaffray2, C Coolens1 (1) Princess Margaret Cancer Centre, Toronto, ON, (2) UT MD Anderson Cancer Center, Houston, TX
BReP-SNAP-M-75Forecasting Individual Patient Response to Radiotherapy with a Dynamic Carrying Capacity Model
M Zahid*1, N Mohsin1, A Mohamed2, J Caudell1, L Harrison1, C Fuller2, E Moros1, H Enderling1, (1) H. Lee Moffitt Cancer Center, Tampa, FL, (2) UT M.D. Anderson Cancer Center, Houston, TX
BReP-SNAP-M-98Machine Learning Analysis of Treatment Session Time Components
R Kashani, D Smith*, C Mayo, University of Michigan, Ann Arbor, MI
BReP-SNAP-M-120Quantum-Inspired Approach to Predicting Geometric Changes in Head and Neck Cancer
J Pakela*, R Ten Haken, D McShan, M Matuszak, I El Naqa, University of Michigan, Ann Arbor, MI
BReP-SNAP-T-102Measurements, Simulation, and Analytical Model of Stray Photon and Neutron Absorbed Dose From MV External Beam Radiation Therapy
C Schneider1,2*, W Newhauser2, (1) Mary Bird Perkins Cancer Center, Baton Rouge, LA, (2) Louisiana State University, Baton Rouge, LA
BReP-SNAP-T-141What Knowledge-Based Dose Prediction Models Tell Us About Ovoid Vs. Ring Based Brachytherapy Applicators
K Kallis*, B Covele, A Simon, D Brown, D Scanderbeg, K Kisling, C Yashar, J Einck, L Mell, J Mayadev, K Moore, S Meyers, UC San Diego, La Jolla, CA
MO-CD-TRACK 1-1An Artificial Intelligence-Driven Agent for Rapid Head-And-Neck IMRT Plan Generation Using Conditional Generative Adversarial Networks (cGAN)
X Li1*, Y Sheng1, J Zhang1, W Wang1, F Yin1, Q Wu1, Y Ge2, Q Wu1, C Wang1, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC
MO-E-TRACK 2-2A Novel Pencil Beam Scanned Proton Beam Tracking Framework for Lung Tumours Using Liver Ultrasound and a Respiratory Motion Model
M Krieger1,2,a*, A Giger3,a, C Jud3, A Duetschler1,2, R Salomir4,5, O Bieri3,6, G Bauman3,6, D Nguyen3,6, P Cattin3, D Weber1,7,8, A Lomax1,2, Y Zhang1, (1) Paul Scherrer Institute, Villigen PSI, CH, (2) ETH Zurich, Zurich, CH, (3) University of Basel, Basel, CH, (4) University of Geneva, Geneva, CH, (5) University Hospital of Geneva, Geneva, CH, (6) University Hospital Of Basel, Basel, CH, (7) University Hosptial of Zurich, Zurich, CH, (8) Inselspital, Bern, CH, (a) Both authors contributed equally
PO-GeP-I-214Theoretical Framework for Evaluation of X-Ray Spectra Shape Effects On Dual-Energy Image Quality
I Romadanov1*, M Sattarivand2, (1) Nova Scotia Health Authority, Halifax, NS, CA, (2) Nova Scotia Cancer Centre, Halifax, NS, CA
PO-GeP-M-18A Machine Learning Based Fully Automatic Magnification Calculation Method for Hip DR Photography
Y Jia1*, H Wang1, X Jin2, H Du1, W Chen1, B Yang2, (1) Shaanxi Key Laboratory of Network Data Intelligent Processing; UCLA School of Medicine, Xi'an, Shaanxi, CN, (2) Xian Honghui Hospital
PO-GeP-M-19A Machine Learning Model for Brain V12Gy/V60% Prediction of LINAC-Based Single-Iso-Multiple-Targets (SIMT) Stereotactic Radiosurgery (SRS): A Longitudinal Study
X Li*, J Zhang, Y Sheng, K Lafata, N Eclov, Y Cui, W Giles, J Adamson, A Rodrigues, Z Wang, S Yoo, F Yin, Q Wu, C Wang, Duke University Medical Center, Durham, NC
PO-GeP-M-28A Novel Method to Automate the Optimization of Multileaf Collimator Transmission and Dosimetric Leaf Gap Parameters for Eclipse Treatment Planning System
D DiCostanzo* and A Ayan*, The Ohio State University, Columbus, OH
PO-GeP-M-35A Patient-Specific Model for Tracking Lung Tumor During Radiotherapy Using Surrogate Signal
S Fakhraei*, D Sterling, E Ehler, P Alaei, University of Minnesota, Minneapolis, MN
PO-GeP-M-39A Study of Using Modulation Complexity Score as a Clinical Decision Aid for VMAT-Based Pancreas SBRT Treatment Planning
Y Zlateva*, X Li, F Yin, Q Wu, C Wang, Duke University Medical Center, Durham, NC
PO-GeP-M-136Deep Learning Prediction of Radiotherapy Treatment Machine Parameters
L Hibbard1*, (1) Elekta, Inc, St. Charles, MO
PO-GeP-M-166Dose to Circulating Blood in VMAT TBI Using a Dynamic Whole Body Blood Model
B Guo*, Cleveland Clinic Foundation, Cleveland, OH
PO-GeP-M-195Evaluating the Efficacy of Regression Models in Radiomics: A Study with NSCLC Patients
HHF Choi1*, (1) ,Vancouver, BC, CA
PO-GeP-M-331Predicting Treatment Outcome After Immunotherapy Based On Delta-Radiomic Model in Metastatic Melanoma
X Chen1*, M Zhou1, K Wang2, Z Wang4, Z Zhou4, (1) Xi'an Jiaotong University, Xi'an, Shaanxi, CN, (2) UT Southwestern Medical Center, Dallas, TX, (3) Peking University Cancer Hospital, Beijing, CN, (4) University Of Central Missouri, Warrensburg, Missouri
PO-GeP-M-358Radiomics Feature Robustness Under Different Image Perturbation Combinations and Intensities: A Study On Nasopharyngeal Carcinoma CT Images
J Zhang1, X Teng1*, Z Ma1, T Yu1, S Lam1, F Lee2, K Au2, W Yip2, J Cai1, (1) The Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, (2) Queen Elizabeth Hospital, HKSAR
PO-GeP-M-368Residual Learning by An Artificial Neural Network for a Radiotherapy Beam Monitoring System
Y Cho1*, (1) Cleveland Clinic, Cleveland, OH
PO-GeP-M-412Unboxing Artificial Intelligence "black-Box" Models - A Novel Heuristic
S Weppler1,2*, H Quon1,2, N Harjai1, C Beers1, L Van Dyke2, C Kirkby1,2,3, C Schinkel1,2, W Smith1,2, (1) University of Calgary, Calgary, AB, CA, (2) Tom Baker Cancer Centre, Calgary, AB, CA, (3) Jack Ady Cancer Centre, Lethbridge, AB, CA.
PO-GeP-M-416Using Raman Spectroscopy and Machine Learning to Predict and Monitor Cellular Radiation Responses
X Deng*, K Milligan, R Ali-Adeeb, P Shreeves, S Van Nest, J Andrews, A Brolo, J Lum, A Jirasek, University of British Columbia, Kelowna, BC, CA, University of Victoria, Victoria, BC, CA, Deeley Research Centre, BC Cancer, Victoria, BC, CA, Weill Cornell Medicine, New York, NY, USA
PO-GeP-T-60A Practical Approach of Organ Specific Biologically Effective Dose Calculation for Re-Irradiation
A Tai*, E Quashie, E. Ahunbay, K Kainz, X Li, Medical College of Wisconsin, Milwaukee, WI
PO-GeP-T-70A Study of Reoxygenation of Cancer Cells After Application of Radiation of Different Sized Doses Using a Diffusion Model
Y Watanabe, E Dahlman*, University of Minnesota, Minneapolis, MN
PO-GeP-T-122Assessing the Importance of Oral Cavity Dosimetry On Patient Reported Xerostomia and Dysgeusia in Patients Receiving De-Intensified Treatment for Oropharynx Cancer
D Fried*, A Fuquay, S Das, B Chera, C Shen, K Pearlstein, Univ North Carolina, Chapel Hill, NC
PO-GeP-T-141Automatic IMRT Planning Via Static Field Fluence Prediction (AIP-SFFP): A Novel Local Attention Deep-Learning Design for Head-And-Neck IMRT Application
X Li1, J Zhang1, Y Sheng1, Y Chang1, H Stephens1, Q Wu1, F Yin1, Y Ge2, Q Wu1, C Wang1*, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC
PO-GeP-T-150Beam Model Matching of the Low-Intensity PBS Spot Halo with and Without a Range-Shifter at a Multi-Room Facility
C Ainsley*, University of Pennsylvania, Philadelphia, PA
PO-GeP-T-164Can Knowledge-Based Dose Prediction Models Inform Brachytherapy Needle Decision-Making for Cervical Cancer?
K Kallis*, D Brown, D Scanderbeg, K Kisling, B Covele, A Simon, C Yashar, J Einck, L Mell, J Mayadev, K Moore, S Meyers, UC San Diego, La Jolla, CA
PO-GeP-T-177Characterization of the Limitations of the Mobius3D Dosimetric Leaf Gap Offset Correction
A Shepard*, J Hansen, M Belanger, S Frigo, University of Wisconsin-Madison, Madison, WI
PO-GeP-T-224Comparison of MLC Beam Model Parameter Values in RayStation for VMAT Deliveries
J Hansen*, M Belanger, A Shepard, S Frigo, University of Wisconsin-Madison, Madison, WI
PO-GeP-T-349Dosimetric Implications for Post-Mastectomy Radiation Treatment (PMRT) with AlloX2 Tissue Expander
J Pan1*, M Lafreniere2, J Yang3, E Hirata4, M Sharma5, (1) UCSF Medical Center, Millbrae, CA, (2) Department of Radiation Oncology at UCSF, San Francisco, CA, (3) University Of California, San Francisco, ,,(4) University of California San Francisco, San Francisco, CA, (5) University of California San Francisco, Albany, CA
PO-GeP-T-461Fine Tuning of the Collimator Calibration Parameters for Modeling An Elekta Synergy Accelerator with An Agility Head in RayStation
P Trepanier*, F Girard, Centre integre de sante et de services sociaux de Laval, Laval, QCCA,
PO-GeP-T-464FLASH Proton Radiotherapy: Exploring the Tradeoff Between Dosimetric Plan Quality and Volumes Receiving Ultra-High Dose Rates
M Folkerts, A Magliari*, A Katsis, J Perez, A Harrington, E Abel, Varian Medical Systems, Inc., Palo Alto, California
PO-GeP-T-465FLASH: Flash Leverages A Sudden Hypoxia
F Van den Heuvel*, A Vella, K Petersson, M Brooke, M Hill, B Vojnovic, A Giaccia, CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford
PO-GeP-T-497Implementation and Evaluation of a Tool for Calculating 3D Dose Rates for Proton FLASH Radiotherapy Research
P Lansonneur, M Folkerts*, M Alcanzare, M Ropo, V Petaja, A Harrington, J Perez, E Abel, Varian Medical Systems, Inc., Palo Alto, California
PO-GeP-T-548Investigation On the Portability of Patient-Specific Quality Assurance Between Matched Linear Accelerators
B Barraclough*, S Frigo, Z Labby, University of Wisconsin, Madison, WI
PO-GeP-T-559Learning the Plausible VMAT Subspace with Deep Autoencoders
N Bice1*, N Kirby1, R Li1, C Kabat1, D Nguyen2, N Papanikolaou1, M Fakhreddine1, (1) UT Health San Antonio, San Antonio, TX, (2) UT Southwestern Medical Center, Dallas, TX.
PO-GeP-T-578Method for Optimizing MLC Beam Model Parameters in RayStation for VMAT Deliveries
J Hansen*, M Belanger, A Shepard, S Frigo, University of Wisconsin-Madison, Madison, WI
PO-GeP-T-585Modeling MLC and Jaws Effective Position Correction of An Elekta Synergy Accelerator with An Agility Head in RayStation
P Trepanier*, F Girard, Centre integre de sante et de services sociaux de Laval, Laval, QCCA,
PO-GeP-T-612Optical Calorimetry for Radiation Dosimetry
J Roberts1*, J Meyer2, S Marsh3, A Moggre4, (1) University of Canterbury, Christchurch, CAN, NZ, (2) University of Washington, Seattle, WA, (3) University of Canterbury, Christchurch, CAN, NZ, (4) Canterbury District Health Board, Christchurch, CAN, NZ
PO-GeP-T-624Oxygen Depletion From Proton Spot Scanning: Could Delivery-Optimisation Meet the Conditions for FLASH?
B Rothwell1*, M Lowe2,1, N Kirkby1, M Merchant1, A Chadwick1, R Mackay2,1, K Kirkby1, (1) Division of Cancer Sciences, The University of Manchester, Manchester, UK (2) Christie Medical Physics and Engineering, The Christie NHS Foundation Trust, Manchester, UK
PO-GeP-T-628Patient Specific Collision Zones for 4-Pi SRS/SRT
C Northway1*, J Lincoln1, B Little2, A Syme123, C Thomas1234, (1)Department of Physics and Atmospheric Science, Dalhousie University, Halifax, NS, CA, (2)Department of Medical Physics, Nova Scotia Health Authority, Halifax, NS, CA, (3)Department of Radiation Oncology, Dalhousie University, Halifax, NS, CA, (4)Department of Radiology, Dalhousie University, Halifax, NS, CA,
PO-GeP-T-728SLD Repair Impact On Treatment Effectiveness of Proton Therapy with Various Cell Specific Parameters
K Kasamatsu1*, T Matsuura2,3,4, S Tanaka2,4, K Umegaki2,3,4, (1) Hokkaido University Graduate school of Biomedical Science and Engineering, Sapporo, JP, (2) Hokkaido Universty Faculty of Engineering, Sapporo, Hokkaido, JP, (3) Hokkaido University Global Station for Quantum Medical Science and Engineering, Global Institution for Collaborative Research and Education (GI-CoRE), Sapporo, Hokkaido, JP, (4) Hokkaido University Hospital Proton Beam Therapy Center, Sapporo, Hokkaido, JP
PO-GeP-T-803Towards Monte Carlo Modeling of the Optical Properties of Plastic Scintillation Detectors
E Simiele*, L DeWerd, University of WI-Madison/MRRC, Madison, WI
PO-GeP-T-805Treatment Couch Modeling Via Measured Attenuation Using a Symmetrically Shaped Stereotactic Phantom
X Du*, B Rasmussen, UP Health System - Marquette General Hospital, Marquette, MI
SU-C-TRACK 1-2A Practical Model for Equilibrium Dose Measurement
K Grizzard*, D Vergara, J Moroz, M Hoerner, Yale New Haven Hospital and Yale University School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, CT
SU-C-TRACK 1-3Experimental Validation of a Linear Boltzmann Transport Equation Solver for Rapid CT Dose Map Generation
S Principi1*, Y Liu2, Y Lu2, D K Ragan2, A Wang3, A Maslowski4, T Wareing4, T G Schmidt1, (1) BME department, Medical College of Wisconsin and Marquette University, Milwaukee, WI, (2) Department of Radiology, Medical College of Wisconsin, Milwaukee, WI, (3) Department of Radiology, Stanford University, Stanford, CA, (4) Varian Medical Systems, Palo Alto, CA
TH-B-TRACK 5-0Outcome Modeling and Response Prediction
X Li1*, I El Naqa2*, J Seuntjens3*, X Qi4*, (1) Medical College of Wisconsin, Milwaukee, WI, (2) University of Michigan, Ann Arbor, MI, (3) McGill University, Montreal, QC, (4) UCLA School of Medicine, Los Angeles, CA
TH-B-TRACK 5-1Introduction: outcome modeling and response prediction from analytic to data-driven
X Li1*, I El Naqa2*, J Seuntjens3*, X Qi4*, (1) Medical College of Wisconsin, Milwaukee, WI, (2) University of Michigan, Ann Arbor, MI, (3) McGill University, Montreal, QC, (4) UCLA School of Medicine, Los Angeles, CA
TH-B-TRACK 5-2Radiomics and radiogenomics modeling with machine learning
X Li1*, I El Naqa2*, J Seuntjens3*, X Qi4*, (1) Medical College of Wisconsin, Milwaukee, WI, (2) University of Michigan, Ann Arbor, MI, (3) McGill University, Montreal, QC, (4) UCLA School of Medicine, Los Angeles, CA
TH-B-TRACK 5-3Multimodality radiomics and deep learning for outcome modeling: application in head & neck cancer
X Li1*, I El Naqa2*, J Seuntjens3*, X Qi4*, (1) Medical College of Wisconsin, Milwaukee, WI, (2) University of Michigan, Ann Arbor, MI, (3) McGill University, Montreal, QC, (4) UCLA School of Medicine, Los Angeles, CA
TH-B-TRACK 5-4Opportunities and challenges of outcome modeling & response prediction for radiation therapy
X Li1*, I El Naqa2*, J Seuntjens3*, X Qi4*, (1) Medical College of Wisconsin, Milwaukee, WI, (2) University of Michigan, Ann Arbor, MI, (3) McGill University, Montreal, QC, (4) UCLA School of Medicine, Los Angeles, CA
TH-C-TRACK 1-1Adaptive Spectral Inconsistency Modeling for Photon-Counting Detector CT
Binxiang Qi, Hewei Gao*, Tsinghua University, Haidian Dist, 11CN,
TH-D-TRACK 6-4Successful Conformal Avoidance for Extremely Hypo-Fractionated Prostate Radiotherapy
J Stroom*, C Greco, O Pares, N Pimentel, V Louro, S Vieira, J Kociolek, Z Fuks, Champalimaud Centre for the Unknown, Lisbon,PT
TU-C-TRACK 3-3Dose to the Left Ventricle and the Right Atrium as Well as Mean Lung Dose Predicts Overall Survival in RTOG 0617: An Updated and More Detailed Cardiopulmonary Dose-Response Model
M Thor*, A Apte, R Haq, R Pandya, A Iyer, JH Oh, JO Deasy, Memorial Sloan-Kettering Cancer Center, New York, NY
TU-EF-TRACK 2-10A Hierarchical Motion Model From Dynamic MRI to Characterize Abdominal Configuration Changes
Y Zhang1*, J Balter1, R Kashani1, Y Cao1, J Dow1, A Johansson2, (1) Univ Michigan, Ann Arbor, MI, (2) Uppsala University, Uppsala, SE
TU-EF-TRACK 3-2A Deep-Learning Method of Automatic VMAT Planning Via MLC Dynamic Sequence Prediction (AVP-DSP) Using 3D Dose Prediction: A Feasibility Study of Prostate Radiotherapy Application
Y Ni1*, J Zhang2, Y Sheng2, X Li2, J Ye3, Y Ge4, Q Wu2, C Wang2, (1) Duke Kunshan University, Kunshan, 32, CN, (2) Duke University Medical Center, Durham, NC, (3) Swedish Medical Center, Seattle, WA, (4) University of North Carolina at Charlotte, Charlotte, NC,
TU-EF-TRACK 3-3A Lightweight Deep-Learning Model for Automatic IMRT Planning Via Fluence Map Prediction with a 2.5D Implementation: A Study of Head-And-Neck IMRT Application
C Wang1*, X Li1, Y Sheng1, J Zhang1, K Lafata1, F Yin1, Q Wu1, Y Ge2, Q Wu1, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC
TU-EF-TRACK 3-7BEST IN PHYSICS (THERAPY): Insights Into Planning Techniques Mastered by An Autoplanning Robot: Can An AI Planning Agent Be Interpretable and Tractable?
J Zhang1*, C Wang1, Y Sheng1, F Yin1, Y Ge2, Q Wu1, (1) Duke University Medical Center, Durham, NC, (2) The University of North Carolina at Charlotte, Chartlotte, NC