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Taxonomy: TH- Small Animal RT: Planning
| BReP-SNAP-M-1 | 3D Dose Predictions and Plan Quality Assessment in MRI Guided Online Plan Adaptation Using Artificial Neural Network (ANN) Models D Yang*, A Thomas, Y Fu, H Gach, H Li, Washington University in St. Louis, St. Louis, MO |
| BReP-SNAP-M-9 | A Method to Predict the Patient-Specific Dose-Volume Histogram Curves for Radiotherapy Planning with Deep Learning X Chen*, K Men, J Yi, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, 100021,China |
| BReP-SNAP-M-23 | Automatic Location and Size Selection of Round Applicators for AccuBoost Treatments F West1,2*, S Roles1,2, J Patrick2,3, R Munbodh2,3, M Rivard2,3, J Hepel2,3, K Leonard2,3, D Wazer2,3, Z Saleh2,3, (1) University of Rhode Island, Kingston, RI, (2) Rhode Island Hospital, Providence, RI, (3) Warren Alpert Medical School of Brown University, Providence |
| BReP-SNAP-M-31 | Can Machine Learning Optimization (MLO) Auto Planning Clinically Replace Multi-Criteria Optimization (MCO) Manual Planning in Complex Pancreatic VMAT with Dose Painting? Y Wang*, H Prichard, J Wo, T Hong, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| BReP-SNAP-M-77 | Functionally Weighted Airway Sparing (FWAS) to Preserve Post-SAbR Respiratory Function E Vicente1*, A Modiri1, J Kipritidis2,3, A Hagan1, K Yu4, H Wibowo4, Y Yan5, D Owen6, M Matuszak6, P Mohindra1, R Timmerman5, A Sawant1, (1) University of Maryland School of Medicine, Baltimore, MD, (2) Northern Sydney Cancer Centre, Sydney, AU, (3) CRF Image X Institute, Sydney, AU, (4) Broncus Medical, Inc., San Jose, CA, (5) UT Southwestern Medical Center, Dallas, TX, (6) University of Michigan, Ann Arbor, MI. |
| BReP-SNAP-M-80 | Hard Constraint Approximation for Deep Learning in Radiation Therapy R McBeth*, M Lin, A Godley, S Jiang, D Nguyen, UT Southwestern Medical Center, Dallas, TX |
| BReP-SNAP-M-90 | Incorporating Explicit Dose-Volume Constraints in Deep Learning Improves Prediction of Deliverable Dose Distributions for Prostate VMAT Planning S Willems1*, L Vandewinckele1, E Sterpin2, K Haustermans3, W Crijns3, F Maes1, (1) KU Leuven, BE (2) KU Leuven/UC Louvain, BE (3) KU Leuven/UZ Leuven, BE |
| BReP-SNAP-M-104 | Multicenter Characterization of MRIs Used for Radiation Therapy: The Collaborative Quality Assurance Program L Conroy1*, C Foottit2, B Zhang1,3, A Elzibak4, R Hunter5, P Rapley6, J Kraus Himmelman7, E Gutierrez7, T Tadic1, A McNiven1, D Letourneau1, (1) The Princess Margaret Cancer Centre, University Health Network, Toronto, ON, CA, (2) The Ottawa Hospital, Ottawa, ON, CA, (3) Stronach Regional Cancer Centre, Newmarket, ON, CA (4) Sunnybrook Health Sciences Centre, Odette Cancer Centre, Toronto, ON, CA, (5) Juravinski Cancer Centre, Hamilton, ON, CA, (6) Thunder Bay Regional Health Sciences Center, Thunder Bay, ON, CA, (7) Ontario Health (Cancer Care Ontario), Toronto, ON, CA |
| BReP-SNAP-M-105 | Multi-Strategy Machine Learning Auto Planning for Liver SBRT: Improving Quality, Consistency and Efficiency for a Complex Treatment H Prichard*, J Wo, T Hong, Y Wang, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| BReP-SNAP-M-106 | Novel Angle Descriptor Projection Overlap Volume for Improved Quality Assurance of Lung Cancer Radiotherapy Treatment Plans J Zhang1*, Y Yang2, Y Chen2, J Zhang3, M Chen2, (1) The Hong Kong Polytechnic University, Hung Hom, Kowloon, HKSAR, (2) Zhejiang Cancer Hospital, University Of Chinese Academy Of Sciences, Hangzhou, Zhejiang, CHN, (3) Duke University Medical Center, Durham, NC |
| BReP-SNAP-M-151 | Variable Margin Expansion to Account for MR Image Distortion in Treatment Planning R Mumme*, C Nguyen, A Olanrewaju, P Castillo, J Wang, X Wang, C Cardenas, L Court, M Martel, J Yang, University of Texas MD Anderson Cancer Center, Houston, TX |
| BReP-SNAP-T-12 | A Method for Automatic Optimization of Breast Electronic Tissue Compensation Treatment Plans Based On the Breast Radius and Separation A Podgorsak1,2*, L Kumaraswamy3, (1) University at Buffalo, Buffalo, NY, (2) Roswell Park Comprehensive Cancer Center, Buffalo, NY, (3) Novant Health, Winston-Salem, NC |
| BReP-SNAP-T-19 | Accelerated Multi-Criterial Optimization for Pancreas SBRT Using Machine-Learning Dose Prediction P. James Jensen*, Jiahan Zhang, Q. Jackie Wu, Duke University Medical Center, Durham, NC |
| BReP-SNAP-T-28 | Bayesian Stochastic Frontier Analysis with Missing Data Management as Knowledge-Based Planning for Lung SBRT A Kroshko1,2*, O Morin3, L Archambault1,2, (1) Universite Laval, Quebec, QC, CA (2) CHU de Quebec - Hotel-Dieu de Quebec, Quebec, QC, CA (3) University of California San Francisco, San Francisco, CA, USA |
| BReP-SNAP-T-36 | Comparison of Pareto Surface Interpolations for Multi-Criterial Optimization Algorithm Evaluation P. James Jensen1,2*, Jiahan Zhang2, Q. Jackie Wu2, (1) Duke University Medical Center, Durham, NC, (2) Duke University Medical Center, Durham, NC |
| BReP-SNAP-T-37 | Comparison of Static and Dynamic Collimation of the Mevion S250i Proton Therapy System H Jin1*, H Grewal1, 2, S Ahmad1, (1) University of Oklahoma Health Science Center, Oklahoma City, OK, (2) The Oklahoma Proton Center, Oklahoma City, OK |
| BReP-SNAP-T-41 | Development and Customization of the RayStation Automatic Breast Planning Module: A Program to Adapt the Provided Module to Suit Clinical Needs C Collins*, M Kaluarachchi, E Yu, J Hepel, M Schwer, Brown University Warren Alpert Medical School/Rhode Island Hospital, Providence, RI |
| BReP-SNAP-T-54 | Efficient Stochastic Optimization Accounting for Uncertainty in HDR Prostate Brachytherapy Needle Placement S Gerlach1*, F Siebert2, A Schlaefer1, (1) Hamburg University of Technology, Hamburg, HH, DE, (2) University Hospital of Schleswig-Holstein, Campus Kiel, Medical Physics, Kiel, KI, DE |
| BReP-SNAP-T-73 | Hierarchical Deep Reinforcement Learning for Intelligent Automatic Radiotherapy Treatment Planning C Shen*, L Chen, Y Gonzalez, D Nguyen, S Jiang, X Jia, The University of Texas Southwestern Medical Ctr, Dallas, TX |
| BReP-SNAP-T-76 | Image Guided Radiotherapy Without the Clinical Target Volume: Leveraging the Abscopal Effect S Yasmin-Karim*, R Mueller, N Bih, M Moreau, V Ainsworth, W Ngwa, harvard Medical School, Dane Farber cancer Institute, Brigham and Womens Hospital, Boston, MA |
| BReP-SNAP-T-84 | Improving Plan Quality Through Automation of Treatment Planning Processes Using Scripting in RayStation G Jarry*, M Ayles, M Brunet-Benkhoucha, D Martin, Hopital Maisonneuve-Rosemont, Montreal, CA, |
| BReP-SNAP-T-93 | Is Spot-Scanning Proton Beam Therapy Ready for the Single Target Brain Stereotactic Radiosurgery? A Dosimetric Comparison Study Among VMAT, SPArc and IMPT S Chang*, G Liu, P Chinnaiyan, L Zhao, W Zheng, D Yan, I Grills, C Stevens, P Kabolizadeh, X Li, X Ding, William Beaumont Hospital, Royal Oak, MI |
| BReP-SNAP-T-99 | Margin Verification for Hypofractionated Prostate Radiotherapy Using a Novel Dose Accumulation Workflow and Iterative CBCT M Studenski*, R Delgadillo, Y Xu, J Both, K Padgett, M Abramowitz, A Dal Pra, A Pollack, N Dogan, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL |
| BReP-SNAP-T-108 | ORBIT-RT: A Real-Time, Open Platform for Knowledge-Based Quality Control of Radiotherapy Treatment Planning B Covele*, K Puri, K Kallis, K Moore, UC San Diego, La Jolla, CA |
| BReP-SNAP-T-111 | Planning Dose Constraint Corrections for Increasing Accuracy in Radiotherapy J Stroom1*, A Taborda2, B Nijsten3, S Vieira4, C Greco5, (1) Champalimaud Centre for the Unknown, Lisbon, ,PT, (2) Champalimaud Centre for the Unknown, Lisbon, ,PT, (3) MAASTRO Clinic, Maastricht, ,NL, (4) Champalimaud Centre for the Unknown, Lisbon, ,PT, (5) Champalimaud Centre for the Unknown, Lisbon, ,PT |
| BReP-SNAP-T-114 | Predicting Optimal Plan Dose Distributions of Total Marrow Irradiation Using Deep Learning Model D Du*, J Neylon, J Wong, A Liu, City of Hope Medical Center, Duarte, CA |
| BReP-SNAP-T-117 | Prioritizing Patient-Specific Geometry: Beam Angle Optimization in Stereotactic Radiosurgery T Mann*, N Ploquin, K Thind, Tom Baker Cancer Center/University of Calgary, Calgary, AB |
| BReP-SNAP-T-132 | Towards a Treatment Planning Optimization Framework Utilizing Predicted Quality Assurance Outcomes From a Machine Learning Model to Maximize Plan Quality and Deliverability P Wall1*, J Fontenot1,2, (1) Louisiana State University, Baton Rouge, LA, (2) Mary Bird Perkins Cancer Center, Baton Rouge, LA |
| BReP-SNAP-T-138 | Using Deep Learning to Predict Beam-Tunable Pareto Optimal Dose Distribution for Intensity Modulated Radiation Therapy G Bohara*, A Sadeghnejad Barkousaraie, S Jiang, D Nguyen, UT Southwestern Medical Center, Dallas, TX |
| BReP-SNAP-T-140 | VMAT Simultaneous Integrated Total Marrow and Total Body Irradiation (SIMBA) DN Stanley1*, KA McConnell2, A Everett1, J Dodson1, K Keene1, A Mcdonald1, (1) The University of Alabama at Birmingham, Birmingham, AL, (2) UC San Diego, San Diego, CA |
| BReP-SNAP-T-141 | What 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 1-1 | An 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-EF-TRACK 3-15 | Deep DoseNet: A Deep Neural Network Based Dose Calculation Algorithm P Dong1*, L Xing1, (1) Stanford Univ School of Medicine, Stanford, CA |
| PO-GeP-M-14 | A Feasibility Study On the Development of a Deep Learning-Based Whole Brain Irradiation Automated Dose Calculation Algorithm A Jaffe*, J Keller, Thomas Jefferson University, Philadelphia, PA |
| PO-GeP-M-23 | A Multi-Observer Study Investigating the Effectiveness of Prostatic MpMRI to Dose Escalate Corresponding Histologic Lesions Using High Dose Rate Brachytherapy C. Smith1*, D. Hoover1,2, K. Surry1,2, D. D'Souza3, D. W. Cool1,3, Z. Kassam1,3, M. Bastian-Jordan4, J. A. Gomez1, M. Moussa1, J. L. Chin3, S. Pautler3, G. S. Bauman1,2,3, A. Ward1,3, (1) University of Western Ontario, London, ON, CA, (2) London Regional Cancer Program, London, ON, CA,(3) London Health Sciences Centre,(4) University Of Queensland, Queensland, Australia |
| PO-GeP-M-42 | Abdominal Synthetic CT Reconstruction Using Intensity Projection Prior for MRI-Only Adaptive Radiotherapy S Olberg1,2*, J Chun1,3, B Choi1,3, I Park1,3, H Kim1, T Kim1, J Kim3, O Green1, J Park1,2,3, (1) Department of Radiation Oncology, Washington University in St. Louis, St. Louis, MO, (2) Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, (3) Department of Radiation Oncology, Yonsei University College of Medicine, Seoul, KR |
| PO-GeP-M-70 | Assessment of CBCT-Based Dose Accumulation for Robustly Optimized Intensity-Modulated Proton Therapy Plans with Different Uncertainty Scenarios for Treatment of Prostate Cancer Y Xu*, M Butkus, N Brovold, A King, T Diwanji, A Pollack, M Abramowitz, N Dogan, Sylvester Comprehensive Cancer Center, University of Miami Miller School of Medicine, Miami, FL |
| PO-GeP-M-72 | Assessment of Need for MR Guidance in Adaptive RT A Sethi*, E Omari, J Ingram, K Stoeckigt, T Thomas, J Roeske, T Refaat, R Wynn, Loyola Univ Medical Center, Maywood, IL |
| PO-GeP-M-73 | Assessment of Ventilation-Perfusion (V/Q) Relationships with 68Ga V/Q PET/CT in Lung Cancer Patients Z Li1*, J Callahan2, M Hofman2, S Siva2, T Yamamoto1, (1) University of California Davis, Sacramento, CA, (2) Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia |
| PO-GeP-M-110 | Comparison of Artificial Intelligence Based Decision Support Tool for Head and Neck Treatment Planning R McBeth*, D Nguyen, H Wang, R Norouzi-Kandalan, m mashayekhi, X Zhong, Z Iqbal, A Godley, S Jiang, M Lin, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-134 | Deep Learning Based Treatment Plan Evaluation X Zhong*, D Nguyen, R McBeth, A Balagopal, m mashayekhi, M Lin, S Jiang, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-168 | Dosimetric Analysis of Automated Treatment Planning for Whole Brain Radiotherapy with A Deep Learning Approach E Han*, C Cardenas, C Nguyen, T Briere, j Li, D Yeboa, C Wang, L Court, M Martel, Z Wen, University of Texas MD Anderson Cancer Center, Houston, TX |
| PO-GeP-M-173 | Dosimetric Evaluation of Online Adaptive Planning for Patients with Glioblastoma Multiforme Cancer with MRI at Two Week Intervals D To*, M Liu, I Xhaferllari, D Lack, P Chinnaiyan, D Yan, Beaumont Health, Royal Oak, MI |
| PO-GeP-M-174 | Dosimetric Impact of Magnetic Resonance Imaging Distortion in Photon and Proton Treatment Plans Y Yan1*, J Yang2, F Pirlepesov1, Y Ding2, J Uh1, L Zhao1, T Merchant1, J Wang2, C Hua1, (1) St. Jude Children's Research Hospital, Memphis, TN, (2) MD Anderson Cancer Center, Houston, TX |
| PO-GeP-M-196 | Evaluating the Performance of a Generative Adversarial Network-Based (GAN-Based) Architecture for Automatic Detection of Fiducial Markers in Prostate MRI-Only Radiotherapy Images K Singhrao1*, J Fu1, D Ruan2, A Mikaeilian2, N Parikh2, A Kishan2, J Lewis3,(1) David Geffen School of Medicine at UCLA, Los Angeles, CA, (2) Dept. of Radiation Oncology, UCLA, Los Angeles, CA, (3) Cedars-Sinai Medical Center, Beverly Hills, CA |
| PO-GeP-M-197 | Evaluation of a Novel Automated Treatment Planning Tool for Cervical Cancer in IMRT J Guo*, J Zhou, L Chen, J Ni, Y Xu, G Gan, W Gon, C Ma, Y Li, W Zhan, X Xu, S Qin, The first affiliated hospital of Soochow University, Suzhou, JiangsuCN, |
| PO-GeP-M-207 | Evaluation of Simple MR-Based Online Adaptive Radiotherapy for Prostate Cancer I Xhaferllari*, D Lack, R Levitin, D To, M Liu, J Liang, D Krauss, D Yan, William Beaumont Hospital, Royal Oak, MI |
| PO-GeP-M-210 | Evaluation of T2-Weighted MRI Pulse Sequences for Visualization and Sparing of Urethra with MR-Guided Radiation Therapy (MRgRT) On-Board MRI J Pham*, R Savjani, Y Gao, M Cao, P Hu, D Low, M Steinberg, A Kishan, Y Yang, UCLA, Los Angeles, CA |
| PO-GeP-M-221 | Feasibility of MRI-Only Treatment Planning for Stereotactic Body Radiation Therapy (SBRT) for Prostate Cancer R Nosrati1*, W Lam2,3, M Paudel2,3, A Pejovic-Milic4, G Morton2,3, G Stanisz2,3, (1) Harvard Medical School, Boston Children's Hospital, Boston, MA (2) Sunnybrook Health Sciences Centre, Toronto, ON, Canada (3) University of Toronto, Toronto, ON, Canada, (4) Ryerson University, Toronto, ON, Canada |
| PO-GeP-M-288 | Low Cost MRI Geometric Distortion Phantom Made From Paintballs and Pourable Foam B Rasmussen*, X Du, R Milano, UP Health System - Marquette General Hospital, Marquette, MI |
| PO-GeP-M-302 | MRI Visualization of Applicators for Skin HDR Brachytherapy E Kaza*, R Cormack, P Devlin, I Buzurovic; Dana-Farber/Brigham And Women's Cancer Center, Harvard Medical School, Boston, MA |
| PO-GeP-M-333 | Prediction of Optimal Weighting Factors Into the Objective Function On IMRT Plans E Cisternas Jimenez*, F Yin, Duke University Medical Center, Durham, NC |
| PO-GeP-M-367 | Region Specific Dose Prediction Using Deep Neural Networks: A Feasibility Study On the Planning Target Volume of Prostate IMRT Patients D Nguyen*, S Jiang, Medical Artificial Intelligence and Automation (MAIA)Laboratory, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-369 | Results of a Prospective Trial Examining MRI Sialography Guided Parotid Ductal Sparing D Fried*, T Zhu, S Das, L Marks, C Shen, K Pearlstein, B Chera, Univ North Carolina, Chapel Hill, NC |
| PO-GeP-M-380 | Simplifying 3D Bolus Creation Using 3D Slicer P Koistinen1*, M Koistinen2, (1) Univerisity of Rhode Island, Millis, MA, (2) CHEM Center for Rad Oncology, Millis, MA |
| PO-GeP-M-408 | Towards Safer Artificial Intelligence-Based Radiation Therapy Treatment Planning: Adding Uncertainty Estimation to Volumetric Dose Prediction Using An Approximate Bayesian Method On Deep Neural Networks D Nguyen*, A Balagopal, A Sadeghnejad Barkousaraie, R McBeth, S Jiang, Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-M-409 | Transferring Beam Navigation Behavior From Human to Robot: An Evidence Driven Decision Making Model for Liver SBRT Y Sheng*, W Wang, R Li, C Wang, J Zhang, X Li, H Stephens, Q Wu, F Yin, Y Ge, Q Wu, Duke University Medical Center, Durham, NC |
| PO-GeP-M-411 | Treatment Planning for Patients with Resected GBM: Is Post-Operative MRI Enough? S Thrower*, K Brock, Y Hasan, C Chung, UT MD Anderson Cancer Center, Houston, TX |
| PO-GeP-M-418 | Utilizing Knowledge-Based Planning Model to Predict Achievable Prescription Dose for Mesothelioma Patients with Two Intact Lungs L KUO*, A Rimner, E Yorke, Memorial Sloan-Kettering Cancer Center, New York, NY |
| PO-GeP-M-420 | Validation of a Commercial Continuous Hounsfield Unit (HU) Pelvis Synthetic CT Platform for MR-Only Prostate Radiotherapy Treatment Planning V Yu*, M Hunt, L Cervino, A Damato, N Tyagi, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GeP-P-8 | How to Create a Valuable Medical Physics Practice G Hogan*, SC&H Capital, Ellicott City, MD |
| PO-GeP-P-35 | Defining the Role of the Medical Physics Profession in Health Care Through Legislation: The Intelligent Approach to Positioning the Profession for the Future J Malicki*, Poznan University of Medical SciencesPoznanPL |
| PO-GeP-P-57 | An External Beam Radiation Oncology Treatment Plan Library (ROTPL) for Head and Neck Cancer H Wang*, L Court, R Mumme, C Nguyen, S Beddar, X Wang, C Wang, S Tung, M Martel, A Moreno, J Reddy, A Garden, D Rosenthal, J Phan, MD Anderson Cancer Center, Houston, TX |
| PO-GeP-P-71 | A New Treatment Planning Technique Combining Stereotactic Radiosurgery (SRS), and Fractioned Stereotactic Radiosurgery (FSRS) Treatment Plans, to Minimize Undesirable Dose Contributions When Treating Multiple Brain Metastases (MBM) Using Brainlab Elements X Liu*, J Vinci, O Bhattasali, M Girvigian, K Lodin, R Boone, J Rahimian, Department of Radiation Oncology, Kaiser Permanente, Los Angeles Medical Center, CA |
| PO-GeP-P-89 | Dose Distributions and Comparisons in 3D Lattice Radiotherapy LRT and 2D GRID Beam M Xu, R Foster*, Y Qin, M Stutz, Northwestern Medicine, Geneva, IL and Radiation Oncology Consultants, Ltd., Oak Brook, IL |
| PO-GeP-P-92 | On The Effectiveness and Accuracy of .Decimal's 3D Scanner System J Newton*, K Li, John R. Marsh Cancer Center, Hagerstown, MDAF, |
| PO-GeP-T-7 | A Comparative Dosimetric Evaluation in Carcinoma Right Breast for Convex Chest Wall, Post Breast Conservation Surgery Using Two Distinct Radiotherapy Techniques H Kaur*, S Hazarika, J Ninan, P Chandrasenan, K Verma, S Goel, P Handa, S Pande, Artemis Hospitals, Gurugram, HRIN, |
| PO-GeP-T-58 | A Phantom Study to Compare 3D-Printed and Traditionally Hand-Made Boluses in Radiotherapy for Head and Neck Cancer M Wong*, FKH Lee, T Lee, G Ng, T Chung, M Lam, K Ma, V Cheung, Queen Elizabeth Hospital, Hong Kong SAR |
| PO-GeP-T-72 | A Study On Active Scanning Path Optimization for Intensity Modulated Proton Therapy Based On Improved Adaptive Genetic Algorithm Lian Zhang1, Wenjun Fan2*, Jun Han1, Jing Yang1, Xiaohui Zhu1, Biao Tu1, Zhiwen Liang and Sheng Zhang1, (1) Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, CN, (2) Department of Otolaryngology, Renmin Hospital of Wuhan University, Wuhan, Hubei, CN. |
| PO-GeP-T-87 | Adaptive Planning, Yes Or No: On Lung SBRT Tumors with Large Shifts During Daily Image Guidance T Ma*, C Liu, S Ahmed, N Yu, P Qi, K Stephans, G Videtic, P Xia, The Cleveland Clinic Foundation, Cleveland, OH |
| PO-GeP-T-95 | An Efficient Optimization Method for Prostate LDR Planning by Including Prior Knowledge G Szalkowski1*, R Chen2, T Royce1, A Wang1, J Lian1, (1) University of North Carolina at Chapel Hill, Chapel Hill, NC, (2) University Of Kansas |
| PO-GeP-T-100 | An Intelligent IMRT Optimization Method Based On Radiobiological Effects Oriented by Dose Prediction X Lu1*, Y Li2, A Wu1, M Qi1, Y Liu1, L Zhou1, T Song1, (1) Southern Medical University, Guangzhou, Guangdong, CN, (2) Sun Yat-sen University Cancer Center, Guangzhou, Guangdong, CN |
| PO-GeP-T-106 | Analysis of a Machine Learning Based Planning Tool for Parotid Dose Prediction and Sparing N Shaheen*, R Bayliss, P Hill, University of Wisconsin, Madison, WI |
| PO-GeP-T-125 | Assessment of a Knowledge-Based RapidPlan Model for Craniospinal Irradiation (CSI) G Luo*, G Twork, M Price, G Ding, Vanderbilt University, Nashville, TN |
| PO-GeP-T-131 | Atlas-Based PTV Contouring for Total Marrow Irradiation A Cherpak1,2,3*, K Chytyk-Praznik1,2,3, L Mulroy1,2, (1) Nova Scotia Health Authority, Halifax, NS, CA (2) Department Of Radiation Oncology, Dalhousie University, Halifax, NS, CA (3) Department of Physics and Atmospheric Sciences, Dalhousie University, Halifax, NS, CA |
| PO-GeP-T-141 | Automatic 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-144 | Automation of Eyeplaque Procedures for Greater Integration and Safety Q Diot*, D Westerly, M Miften, University of Colorado School of Medicine, Aurora, CO |
| PO-GeP-T-145 | Automation of the Treatment Planning Process for IMRT Prostate Cases P Kapoor1,2*, (1) Virginia Commonwealth University, (2)Hunter Holmes McGuire Veterans Affairs Medical Center, Richmond, VA |
| PO-GeP-T-153 | Benchmarking DVH Prediction Uncertainty Between Different Knowledge-Based Models B Covele*, K Moore, UC San Diego, La Jolla, CA |
| PO-GeP-T-164 | Can 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-183 | CivaSheet Commissioning and Workflow Planning S Morelli1*, J Woollard1, A Ayan1, N Gupta1, (1) The Ohio State University, Columbus, OH |
| PO-GeP-T-210 | Comparing SBRT Lung Treatment Plans Using Acuros and AAA to Evaluate Dosimetric Guidelines M Webster*, J Yeo, S Tanny, N Joyce, University of Rochester, Rochester, NY |
| PO-GeP-T-222 | Comparison of Forward-Planned and Inversely-Optimized Dose Distributions for Patients Treated Using An Hybrid Interstitial Gynecological Applicator Placed Under MRI Guidance for Treatment of Cervical Cancer S Simiele1*, G Mcginnis2, M Cunningham1, C Starks1, L Colbert2, M Joyner2, L Lin2, A Jhingran2, P Eifel2, A Venkatesan3, A Klopp2, (1) Department of Radiation Physics, MD Anderson Cancer Center, Houston, TX, (2) Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX, (3) Abdominal Imaging Department, MD Anderson Cancer Center, Houston, TX |
| PO-GeP-T-224 | Comparison 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-227 | Comparison of Prone Versus Supine Position and Different Planning Technique of Anal Cancer Treated with Pencil-Beam Scanning Proton Beam: Cross Institutional Dosimetric and IGRT Studies S Ito1*, A Anand2, J Lentz2, C Beltran3, (1) Mayo Clinic, Rochester, MN, (2) Mayo Clinic Arizona, Phoenix, AZ, (3) Mayo Clinic, Jacksonville, FL |
| PO-GeP-T-236 | Compounding Automation Features From Multiple APIs for Automated Linear Accelerator Quality Assurance M Schmidt1,2*, C Raman1, Y Wu1, N Knutson1, F Reynoso1, Y Hao1, M Yaqoub1, G Hugo1, E Sajo2, B Sun1, (1) Washington University School of Medicine in St. Louis, Saint Louis, MO, AF,(2) Univ Massachusetts Lowell, Lowell, MA |
| PO-GeP-T-316 | Dosimetric Advantage of Individual Field Simultaneously Optimized Scanning Proton Beam Therapy Over VMAT for Gynecological Cancer R Ger*, J Lentz, S Vora, M Bues, D Hernandez Morales, C Schulz, P Lara, A Ridgway, J Gagneur, A Anand, Mayo Clinic Arizona, Phoenix, AZ |
| PO-GeP-T-320 | Dosimetric and NTCP Analysis of Hippocampus, Parotid and Lacrimal Glands Sparing After Whole Brain IMRT M Oakey*, J Fenoli, K Wang, S Das, P Mavroidis, Univ North Carolina, Chapel Hill, NC |
| PO-GeP-T-328 | Dosimetric Comparison of Hybrid Plan, 3DCRT and VMAT in Patients with Left-Sided Breast Cancer S Ahn1*, E Ju1,2, N Jung1, Y Kim2, (1) Department of Radiation Oncology, Kangwon National University Hospital, Chuncheon-si, Republic of Korea, (2) Department of Bio-medical Science, Graduate School of Korea University, Sejong, Republic of Korea, (3) Department of Radiation Oncology, Asan Medical Center, Seoul, Republic of Korea |
| PO-GeP-T-330 | Dosimetric Comparison of Inverse Planning Simulated Annealing and Graphical Optimization in Oncentra Based MUPIT Interstitial Plans G Narayanasamy*, S Morrill, M Bimali, E Galhardo, F Kalantari Mahmoudabadi, G Lewis, University of Arkansas for Medical Sciences, Little Rock, AA |
| PO-GeP-T-331 | Dosimetric Comparison of Lung Cancer SBRT Treatment Plans Using Varian Truebeam and Vitalbeam H Sha*, P Aryal, Bayhealth Medical Center, Dover, DE |
| PO-GeP-T-340 | Dosimetric Evaluation of a New Integrated SBRT/IMRT Treatment System D wu1*, A Eldib2, L Chen3, Q Jiang4, B Wang5, T Dos Santos6, C Ma7, (1) The People`s Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, CN, (2) Fox Chase Cancer Center, Philadelphia, PA, (3) Fox Chase Cancer Center, Philadelphia, PA, (4) The First Affliated Hospital of Chongqing Medical University, Chongqing, Chongqing, CN, (5) Temple University Hospital, Philadelphia, PA, (6) AdventHealth at Gordon, Calhoun, GA, (7) Fox Chase Cancer Center, Philadelphia, PA |
| PO-GeP-T-342 | Dosimetric Evaluation of Jaw Tracking Technique for Multiple Off-Axis Targets J Yoon*, University of Rochester, Rochester, NY |
| PO-GeP-T-354 | Dosimetrical Assessment of JawTracking Technique in RapidArc for Patients with Lateralized Targets R Diaz Moreno*, C Venencia, A Garcia, M Almada, F Munoz, Instituto Zunino - Fundacion Marie Curie, Cordoba, ARGENTINA |
| PO-GeP-T-358 | Dosimetry of the Beta Ophthalmic Applicator Utilized in the Cook Et Al. RCT for Scar Prophylaxis in Glaucoma Trabeculectomy Surgery A Cohen* (1), L Marsteller (1), IE Murdoch (1,2), N Joubert (3), J Wetter (3), G Maree (3), W Lutz (1); (1) Radiance Therapeutics, Inc.; (2) Institute of Ophthalmology, University College London, UK; (3) Department of Radiation Oncology, University of Cape Town, Groote Shuur Hospital, ZA. |
| PO-GeP-T-371 | Effects of Tissue Inhomogeneity and Applicator Physical Properties On 3-D HDR Tandem and Ovoid Treatment Plans D Kahn*, J Roberson, Z Xu, J Kim, S Lu, A Hsia, X Qian, S Ryu, Stony Brook University Hospital, Department of Radiation Oncology, Stony Brook, NY |
| PO-GeP-T-374 | Efficient Proton Arc Therapy with Column-Generation Planning M Chen*, X Gu, W Lu, UT Southwestern Medical Center, Dallas, TX |
| PO-GeP-T-391 | Evaluating Knowledge-Based Planning Performance for Challenging Cases: Prostate Bed VMAT with Nodal Involvement and Significant OAR Overlaps L Fu*, Y Lo, Mount Sinai Health System, New York, NY |
| PO-GeP-T-397 | Evaluation of a Novel AI-Driven Automated Treatment Planning System X Ray*, M Moazzezi, C Bojechko, K Moore, University of California San Diego, La Jolla, CA |
| PO-GeP-T-398 | Evaluation of a Novel Automation Software for Generating Field-In-Field Plans for Various Treatment Sites C Esquivel1*, L Patton1, B Doozan2, K Nelson1, D Boga1, T Navarro3, (1) Texas Oncology San Antonio, San Antonio, TX, (2) Texas Oncology, McAllen, TX, (3) Texas Oncology Brownsville,TX |
| PO-GeP-T-415 | Evaluation of Single Isocenter Multiple Lesion Plan Quality Optimized with Different Aperture Shape Controller Setting S Li*, CARTI, Little Rock, AR |
| PO-GeP-T-420 | Evaluation of the RayStation Monte Carlo Dose Algorithm for Small-Field Surface-Collimated Electrons J Fenoli1*, M Oakey1, S Andrew2, R McGurk1, (1) University of North Carolina Chapel Hill, Chapel Hill, NC, (2) Rex UNC Radiation Oncology, Raleigh, NC |
| PO-GeP-T-423 | Evaluation of Three Conversion Factors From Dose to Medium to Dose to Water in the Radiotherapy Dosimetry H ALHAMADA1*, (1) Nuclear Metrology Department, Faculty of Applied Sciences, ULB, BE. |
| PO-GeP-T-430 | Examination of the Best Head Tilt Angle for Parotid Gland Preservation in Whole-Brain Radiotherapy Using Four-Field Box Technique H Shimizu1,2*, K Sasaki2, T Aoyama1, H Tachibana1, H Tanaka1, Y Koide1, T Iwata1, T Kitagawa1, T Kodaira1, (1) Aichi Cancer Center, Nagoya, Aichi, JP, (2) Gunma Prefectural College of Health Sciences, Maebashi, Gunma, JP |
| PO-GeP-T-441 | Eye Plaque Brachytherapy Treatment Planning with Fundus Image-Based Tumor Modeling: Retrospective Institutional Experience I Chibirev1*, S Meltsner2, Z Chang3, M Materin4, D Kirsch5, O Craciunescu6, (1) Duke University, Durham, NC, (2) Duke University Medical Center, Durham, NC, (3) Duke University Medical Center, Durham, NC, (4) Duke University Medical Center, ,,(5) Duke University Medical Center, Durham, NC, (6) Duke University Medical Center, Durham, NC |
| PO-GeP-T-445 | Feasibility of An Automated Clinical Workflow for Assessing Risk Organ Doses in Lung Re-Irradiation Patients Using EQD2 D Aramburu Nunez*, A Shepherd, X Li, A Wu, C Simone, A Rimner, A Jackson, L Santanam, E Yorke, Memorial Sloan-Kettering Cancer Center, New York, NY |
| PO-GeP-T-450 | Feasibility of Newly Designed Overlapping Half Beam Based Multi-Isocentric Volumetric Arc Modulated Therapy (OHMI-VMAT) Method for Pediatric Total Body Irradiation J Lee1,2*, B Park1,3, S Hong1,2, (1) Konkuk University Medical Center, Seoul, KR, (2) Konkuk University, Graduate School of Engineering, Seoul, KR, (3) Korea University, Graduate School, Seoul, KR, |
| PO-GeP-T-454 | Feasibility of Soft Robot Assisted 4pi Supine Breast Radiotherapy D Shang*, W Gu, Q Lyu, R Neph, K Sheng, UCLA School of Medicine, Los Angeles, CA |
| PO-GeP-T-478 | Hippocampal-Sparing in Whole-Brain Radiation Therapy: A New VMAT Treatment Planning and Optimization Technique D Soultan*, M Smith, J Edds, H Saleh, H Saleh, Kansas University Medical Center, Department of Radiation Oncology, Kansas City, KS |
| PO-GeP-T-481 | Image Quality of Commercial CT Metal Artifact Reduction Algorithms and a Novel In-House Algorithm for Head and Neck (H&N) Cancer and the Dosimetric Implications On Proton Therapy D Branco*, S Kry, P Taylor, X Zhang, J Rong, S Frank, D Followill, The University of Texas MD Anderson Cancer Center, Houston, TX |
| PO-GeP-T-509 | Improving Clinical Efficiency and Consistency with RapidPlan for Single Lesion Brain SRS Q Shang*, K Koch, D Wiant, T Hayes, N Koch, B Sintay, H Liu, Cone Health Cancer Center, Greensboro, NC |
| PO-GeP-T-510 | Improving SBRT Lung Plans by the Addition of Oblique Beams; a Clinical Experience M Webster*, J Yeo, S Tanny, N Joyce, University of Rochester, Rochester, NY |
| PO-GeP-T-515 | Incorporating Parotid Gland Sub-Region Importance Data Into the Radiotherapy Treatment Planning Process C Sample1,2, J Wu2, S Thomas2, H Clark2, (1) University of British Columbia, Vancouver, BC, CA, (2) BC Cancer Agency, Vancouver, BC, CA |
| PO-GeP-T-522 | Integrating Knowledge-Based Models for An Enhanced Iterative Automated Treatment Planning Process M Vaccarelli1,2*, J Baker3, (1) Hofstra University, Hempstead, NY, (2) Northwell Health, Lake Success, NY, (3) Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY |
| PO-GeP-T-538 | Investigation of Plan Quality Metrics for Multiple-Metastases Using Single-Isocenter Radiosurgery Technique with Millennium MLC E Flores-Martinez1*, G Kim2, (1) University of Chicago Hospitals, Chicago, IL, (2) University of California, San Diego, La Jolla, CA |
| PO-GeP-T-545 | Investigation of the Potential for Dosimetric Accuracy Improvements When Utilizing the Gammex 1467 Advanced Electron Density Phantom for CT Number Conversions of High-Density Biologic Material A Plypoo1*, E Lobb2, K Ruchala3, (1) Ascension St. Elizabeth Hospital, Appleton, WI, (2) Ascension St. Elizabeth Hospital, Appleton, WI, (3) GAMMEX Inc., Madison, WI |
| PO-GeP-T-555 | Jaw Tracking VMAT: A Robust Option in Delivering Whole Breast Radiotherapy with a Simultaneously-Integrated Boost (WBRT+SIB) K Guida*, J Coster, S Howard, V Radice, University of Kansas Cancer Center, Kansas City, KS |
| PO-GeP-T-556 | Knowledge-Based RapidPlan Models for Left and Right Breast Using RapidArc O Apaza*, A Garcia, M Almada, C Venencia, Instituto Zunino - Fundacion Marie Curie, Cordoba, ARGENTINA |
| PO-GeP-T-578 | Method 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-602 | Non-Coplanar VMAT Improves Plan Quality for Locoregional Radiotherapy of Left-Sided Breast Cancer with Internal Mammary Nodes Y Xu*, Y Xu, Z Hu, Y Tian, P Ma, K Men, S Wang, J Dai, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences, Beijing, BeijingCN, |
| PO-GeP-T-641 | Plan Quality Assessment for Rectal Cancer Patients Using Prediction of Organ-At-Risk Dose Metrics A Vaniqui*, R Canters, F Vaassen, C Hazelaar, I Lubken, K Kremer, C Wolfs, W Van Elmpt, Maastro, Maastricht, NL, |
| PO-GeP-T-644 | Possible to Treat with Lower Energy? Tangential Whole Breast Irradiation with Electronic Tissue Compensation Technique S Wisnoskie1*, D Zheng1, A Wahl1, N Bennion1, A Granatowicz1, X Liang2, S Zhou1, (1) University of Nebraska Medical Center, Omaha, NE, (2) University of Florida, Jacksonville, FL |
| PO-GeP-T-654 | Prediction of Three-Dimensional Radiotherapy Optimal Dose Distributions for Lung Cancer Patients with Asymmetric Network Y Shao*, H Chen, Z Xu, Department Of Radiation Oncology, Shanghai Chest Hospital, Shanghai Jiao Tong University |
| PO-GeP-T-691 | Rapid Delivery of GRID Therapy for Deep-Seated Bulky Tumors: A Novel 3D MLC-Based Forward Planning Treatment Technique Via 10MV-FFF Beam D Pokhrel*, L Critchfield, M Halfman, M Bernard, W St Clair, M Randall, M Kudrimoti, University of Kentucky, Lexington, KY |
| PO-GeP-T-694 | RayStation Planning for Single Isocenter Multiple Metastases Radiosurgery Treatments R Hamilton1*, L Smith-raymond2, A Locke3, B Stea4, (1) Univ Arizona, Tucson, AZ, (2) Banner University Medical Center Tucson, ,,(3) Banner University Medical Center Tucson, ,,(4) University Of Arizona, |
| PO-GeP-T-707 | Retrospective Dosimetric Study of a Novel Automation Software for Whole Brain Planning Field-In-Field Treatment Plans C Esquivel*, A Tipton, L Patton, D Baldassari, B Lin, Texas Oncology San Antonio, San Antonio, TX |
| PO-GeP-T-739 | Stereotactic Body Radiation Therapy (SBRT) Lung Cancer Treatment Plans Comparison Between HD MLC and Normal MLC M Pudasaini1, T Leventouri1, S Pella1,2, W Muhammad1* (1) Florida Atlantic University, Boca Raton, FL, (2) 21st Century Oncology, Boca Raton, FL |
| PO-GeP-T-758 | The Benefit of Lens PRV in VMAT for Glioblastoma M Alfishawy*, International Medical Center, El Shrouk City, Cairo, Egypt |
| PO-GeP-T-775 | The Impact of Knowledge-Based Planning and Multicriteria Optimization On Treatment Planning Workflows J Rogers*, M DiMascio, J Stanulus, W Ramer, A Boe, Atrium Health - Rock Hill and Lancaster Radiation Therapy, Indian Land, SC |
| PO-GeP-T-787 | The Research of Automatic Treatment Planning by DVH Prediction Based On Classifying the OARs Overlap X Pan1,2*, D Wang1,3,4, J Jia1,3,4, L Hu1, FDS Team (1) Institute Of Nuclear Energy Safety Technology, Chinese Academy Of Sciences, CN (2) University of Science and Technology of China, Hefei Anhui, CN, (3) Anhui Engineering Technology Research Center of Accurate Radiotherapy, Hefei Anhui, CN (4) CAS SuperAccuracy Science & Technology Co., Ltd., Nanjing, Jiangsu, CN |
| PO-GeP-T-801 | Towards Automation of Treatment Planning System Quality Control J A Lovis1*, L Van Dyke1, M Roumeliotis1,2, K Thind1,2, S Quirk1,2, (1) Tom Baker Cancer Centre, Calgary, AB, CA, (2) University of Calgary, Calgary, AB, CA |
| PO-GeP-T-810 | Treatment Planning System Transition: The Effects On Treatment Plan Quality N Stathakis1*, W Jones1, E Maani1, P Mavroidis2, C Kabat1, (1) University Of Texas Health San Antonio,(2) Univ North Carolina, Chapel Hill, NC |
| PO-GeP-T-827 | Using Flattening Filter Free (FFF) Beams in Electronic Tissue Compensation (ECOMP) Whole Breast Irradiation with Deep Inspiration Breath Hold (DIBH) for Left Sided Breast Cancer S Wisnoskie1*, D Zheng1, A Wahl1, N Bennion1, A Granatowicz1, X Liang2, S Zhou1, (1) University of Nebraska Medical Center, Omaha, NE, (2) University of Florida, Jacksonville, FL |
| PO-GeP-T-858 | Automated SBRT Planning Using Constrained Hierarchical Optimization: Three Year Clinical Experience with Over 1900 Patients L Hong*, Y Zhou, Q Huang, J Yang, H Pham, J Mechalakos, M Hunt, J Deasy, M Zarepisheh, Memorial Sloan Kettering Cancer Center, New York, NY |
| SU-CD-TRACK 2-4 | A Data Driven Fully Automated Contouring and Planning Solution for Cervical Cancer D Rhee1*, A Jhingran1, B Rigaud1, K Huang1, K Kisling2, B Beadle3, C Cardenas1, S Kry1, S Vedam1, L Zhang1, K Brock1, W Shaw4, D O'Reilly4, H Simonds5, L Court1, (1) University of Texas MD Anderson Cancer Center, Houston, TX, (2) UC San Diego, La Jolla, CA, (3) Stanford University, Stanford, CA, (4) University of the Free State, Bloemfontein, ,ZA, (5) Stellenbosch University, Stellenbosch, ,ZA, |
| SU-E-TRACK 3-2 | Error Reduction by Synergizing Event Reporting and Automation M Lin*, Y Park, A Pompos, R Reynolds, A Owrangi, D Sher, S Jiang, A Godley, UT Southwestern Medical Center, Dallas, TX |
| TH-A-TRACK 3-2 | Accurate Post-Implantation Dose Reconstruction of CivaSheet, Aided by Machine Learning Techniques I Veltchev*, R Price, X Chen, K Howell, J Meyer, C Ma, Fox Chase Cancer Center, Philadelphia, PA |
| TH-A-TRACK 5-4 | Investigation of Tumor Bed Delineation Accuracy for Targeted Breast Radiation Therapy Following Oncoplastic Surgery G Aldosary1*, J Caudrelier2, T Tse3, A Arnaout3, C Foottit4, J Belec4, E Vandervoort1,4, (1) Department of Physics, Carleton University, Ottawa, ON, Canada, (2) Department of Radiation Medicine, The Ottawa Hospital Cancer Centre, Ottawa, ON, Canada, (3) Department of General Surgery, The Ottawa Hospital, Ottawa, ON, Canada, (4) Department of Medical Physics, The Ottawa Hospital Cancer Centre, Ottawa, ON, Canada |
| TH-AB-TRACK 4-11 | Using Hyperpolarized Xe-129 Gas-Exchange MRI to Guide Functional Avoidance Planning in Thoracic Radiation Therapy L Rankine1,2*, Z Wang2, E Bier2, C Kelsey3, L Marks1, B Driehuys2, S Das1, (1) University of North Carolina School of Medicine, Chapel Hill, NC, (2) Duke University, Durham, NC, (3) Duke University Medical Center, Durham, NC |
| TU-C-TRACK 3-5 | SIB-VMAT for Left-Breast DIBH Patients On Halcyon Linac: A Novel Approach to Integrate Electron Breast Boost Dose in the Primary Beams J Snyder*, M Bernard, D Pokhrel, University of Kentucky, Lexington, KY |
| TU-EF-TRACK 2-4 | Toward MR-Guided Proton Therapy: Synthesizing Relative Proton Stopping Power Images with Multiple Sequence MRI C Wang*, J Uh, C Hua, S Acharya, St. Jude Childrens Research Hospital, Memphis, TN |
| TU-EF-TRACK 2-6 | Using Prediction Models to Analyze MR Guided Radiation Therapy Plans A Thomas1, J Olick-Gibson2*, P Parikh3, D Yang2, (1) MD Anderson Cancer Center, Spring, TX, (2) Washington University in St. Louis, Saint Louis, MO, (3) Henry Ford Cancer Institute, Saint Louis, MO |
| TU-EF-TRACK 2-10 | A 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 2-13 | MRI-To-CBCT Registration for Alignment of Prostate Cancer Patients in An MRI-Only Simulation Workflow: Implementation of a GPU-Accelerated Elasticity-Based Biomechanical Model Mediated by Synthetic-CT Images K Singhrao1*, B Stiehl1, M Lauria1, J Fu1, N Parikh2, A Kishan2, A Santhanam2, J Lewis3, (1) David Geffen School of Medicine at UCLA, Los Angeles, CA, (2) Dept. of Radiation Oncology, UCLA, Los Angeles, CA, (3) Cedars-Sinai Medical Center, Beverly Hills, CA |
| TU-EF-TRACK 3-2 | A 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 3-3 | A 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 3-5 | Automation of Palliative Radiotherapy Treatment Planning Using Independent Models to Prevent Errors From Propagating Through the Planning Process T Netherton1*, D Rhee2, C Cardenas3, C Chung4, A Klopp5, L Colbert6, C Nguyen7, V Kolluru8, R Douglas9, C Peterson10, R Howell11, P Balter12, L Court13, (1) MD Anderson Cancer Center, Houston, TX, (2) MD Anderson Cancer Center, Houston, TX, (3) University of Texas MD Anderson Cancer Center, Houston, TX, (4) MD Anderson Cancer Center, Houston, TX, (5) MD Anderson Cancer Center, Houston, TX, AF, (6) Md Anderson Cancer Center, ,,(7) MD Anderson Cancer Center, Houston, TX, (8) Ut Md Anderson Cancer Center, ,,(9) The University Of Texas Md Anderson Cancer Center, ,,(10) The University of Texas MD Anderson Cancer Center, Houston, TX, (11) The University of Texas MD Anderson Cancer Center, Houston, TX, (12) UT MD Anderson Cancer Center, Houston, TX, (13) UT MD Anderson Cancer Center, Houston, TX |
| TU-EF-TRACK 3-6 | Fluence Map Prediction Using Deep Learning Models: A Pilot Study for AI-Driven Pancreas SBRT Planning W Wang1*, Y Sheng1, J Zhang1, X Li1, P Jensen1, C Wang1, F Yin1, Q Wu1, Y Ge2, QJ Wu1, (1) Duke University Medical Center, Durham, NC, (2) University of North Carolina at Charlotte, Charlotte, NC |
| TU-EF-TRACK 3-7 | BEST 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 |
| TU-EF-TRACK 3-8 | Inter-Institutional Dose Prediction with Deep Convolutional Neural Network and Transfer Learning for Prostate Cancer VMAT Treatments R Norouzi-Kandalan1,2*, D Nguyen1, M Lin1, A Barragan Montero3, S Breedveld4, K Namuduri2, S Jiang1, (1) UTSouthwestern Medical Center, Dallas, TX(2) University of North Texas, Denton, TX (3) Universite Catholique de Louvain, Brussels, VBR, BE (4) Erasmus University Medical Center, RTM, NL |
| TU-EF-TRACK 3-9 | Knowledge-Based Three-Dimensional Dose Prediction for Tandem-And-Ovoid Brachytherapy K Cortes*, A Simon, K Kallis, J Mayadev, S Meyers, K Moore, University of California San Diego, La Jolla, CA |
| TU-EF-TRACK 3-10 | Machine Learning for Lung SBRT Auto Planning and Clinical Decision Support S Zieminski*, H Willers, F K Keane, M Khandekar, Y Wang, Department of Radiation Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| TU-EF-TRACK 3-15 | Human Knowledge Augmented Deep Reinforcement Learning for Intelligent Automatic Radiotherapy Treatment Planning C Shen*, L Chen, Y Gonzalez, D Nguyen, S Jiang, X Jia, The University of Texas Southwestern Medical Ctr, Dallas, TX |
| WE-B-TRACK 2-7 | Toward Intraoperative 3D Ultrasound Guidance and Verification During Gynecologic Brachytherapy Procedures J R Rodgers1*, J Bax1, L Mendez2, V Velker2, D D'Souza2, K Surry2, W T Hrinivich3, I Gyackov1, A Fenster1, (1) Robarts Research Institute, Western University, (2) London Regional Cancer Program, London, ON, CA, (3) Johns Hopkins University School of Medicine, Baltimore, MD |