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Taxonomy: TH- External Beam- Photons: Treatment planning using machine learning/Knowledge Based Planning/automation
| 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-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-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-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-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-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-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-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-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-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-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 |
| 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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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-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 |
| 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 3-7 | 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 3-10 | 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 |