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Taxonomy: TH- External Beam- Photons: Treatment planning using machine learning/Knowledge Based Planning/automation
| 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 5-3 | A Deep-Learning-Based Dual-Arc VMAT Plan Generation From Patient Anatomy for Prostate Simultaneous Integrated Boost (SIB) Cases Q Zhu1*, X Li2, Y Ni3, C Wang4, Q Wu5, Y Ge6, F Yin7, Q Wu8, (1) Duke Kunshan University, Kunshan, 32, CN, (2) Duke University Medical Center, Durham, NC, (3) ,,,(4) Duke University Medical Center, Durham, NC, (5) Duke University Medical Center, Durham, NC, (6) University of North Carolina at Charlotte, Charlotte, NC, (7) Duke University, Chapel Hill, NC, (8) Duke University Medical Center, Durham, NC |
| MO-IePD-TRACK 5-5 | Plan N Check: An Open-Source Tool for Automatic Head-And-Neck VMAT Planning in Varian Eclipse™ C Sample1*, H Clark2, (1) British Columbia Cancer Agency, Vancouver, BC, CA, (2) British Columbia Cancer Agency, Surrey, BC, CA |
| MO-IePD-TRACK 5-6 | A Fully-Automated Field-In-Field Algorithm Translatable to Multiple Disease Sites K Huang1,2*, D Rhee1,2, A Olanrewaju2, D Hancock2, L Zhang2, C Cardenas1,2, P Das2, S Beddar1,2, D Fuentes1,2, T Briere1,2, L Court1,2, (1) University of Texas MD Anderson UTHealth Graduate School of Biomedical Sciences, (2) University of Texas MD Anderson Cancer Center, Houston, TX |
| PO-GePV-M-33 | Three-Dimensional Dose Prediction Methodology for Gamma Knife B Zhang1*, A Babier1, T Chan1, M Ruschin2, (1) University of Toronto, Toronto, Ontario, CA, (2) Odette Cancer Centre, Toronto, ON, CA |
| PO-GePV-M-36 | Probabilistic Feature Extraction, Dose Statistic Prediction and Dose Mimicking for Automated Radiation Therapy Treatment Planning T Zhang1, 2*, R Bokrantz2, J Olsson1, (1) Department of Mathematics, KTH Royal Institute of Technology, Stockholm SE-100 44, Sweden (2) RaySearch Laboratories, Sveavagen 44, Stockholm SE-103 65, Sweden |
| PO-GePV-M-84 | A Dilated Convolution Neural Network to Predict Three-Dimensional Dose Distribution in IMRT Treatment of Cervical Carcinoma Y Yang, Z Yuan*, University of Science and Technology of China, Hefei, Anhui, China |
| PO-GePV-M-233 | A Knowledge-Based Automatic Lung IMRT Planning Method for Partial Heart Sparing L Yuan*, J Sohn, R Singh, E Weiss, S Kim, Virginia Commonwealth University, Richmond, VA |
| PO-GePV-M-244 | Performance Evaluation and Validation of Knowledge-Based Treatment Planning of VMAT for Post-Mastectomy Loco-Regional Radiotherapy Involving Internal Mammary Chain R Phurailatpam*, M Sah, J Jain, T Wadasadawala, K Joshi, J Swamidas, ACTREC, Navi Mumbai, MHIN, |
| PO-GePV-P-50 | An Implementation Strategy for Introducing Automated Treatment Planning Into the Clinic J Jackson*, H Kang, J Wick, J Roeske Loyola University Medical Center, Maywood, IL |
| PO-GePV-T-142 | Automated Plan Checking Software Demonstrates Continuous and Sustained Improvements in Safety and Quality: A 3-Year Longitudinal Analysis S Liu*, D Stuhr, Y Zhou, H Pham, J Xiong, J Mechalakos, S Berry, Memorial Sloan Kettering Cancer Center, New York, NY |
| PO-GePV-T-254 | The Future Is Here: An Inter-Comparative Study of AI and Human-Generated Plans Using Dosimetric Indices as a Guide to Plan Efficacy S Pokharel*, A Pacheco, S Tanner, GenesisCare, Naples, FLORIDA |
| PO-GePV-T-272 | Implementation of An Automated Workflow for Planning Tangential Breast Radiotherapy T Keiper*, D Hoffman, K Kisling, UC San Diego, La Jolla, CA |
| PO-GePV-T-283 | Improved Dose Prediction Performance with Data Clustering and Transfer Learning On H&N Cancer Patients D Nguyen*, T Bai, A Sadeghnejad Barkousaraie, R McBeth, A Balagopal, S Jiang, Medical Artificial Intelligence and Automation (MAIA) Laboratory, UT Southwestern Medical Center, Dallas, TX, USA |
| PO-GePV-T-287 | Knowledge-Based Planning Assisted Automatic Prostate Cancer Radiation Treatment Planning L Chen*, Z Xiong, A Godley, S Jiang, M Lin, UT Southwestern Medical Center, Dallas, TX |
| PO-GePV-T-292 | Dose Prediction Using Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma with Tomotherapy Y Liu1,3*, G Zhang1, Z Chen2, J Wang3, X Wang3, B Qu3,4, S Xu3,4, (1) Beihang University, Beijing, BJ, CN, (2) Manteia Medical Technologies, Xiamen, CN, (3) PLA General Hospital, Beijing, BJ, CN, (4) Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing, 100191, China; and Key Laboratory of Big Data-Based Precision Medicine (Beihang University), Ministry of Industry and Information Technology |
| PO-GePV-T-313 | Second Dosimetry Check with Deep DoseNet P Dong*, L Xing, Stanford University, Stanford, CA |
| SU-A-TRACK 6-2 | Generalizability Study of a Fluence Map Prediction Network L Ma*, M Chen, X Gu, W Lu, University of Texas Southwestern Medical Center, Dallas, TX |
| SU-A-TRACK 6-5 | Towards Automatic Metastasis-Directed Therapy Planning in a Three-Dimensional Beam Model Using Reinforcement Learning W Hrinivich1*, M Deek1, R Phillips2, H Li1, P Tran1, J Lee1, (1) Johns Hopkins University, Baltimore, MD, (2) Mayo Clinic, Rochester MN |
| SU-A-TRACK 6-6 | Towards Interpretable Intelligent Automatic Treatment Planning in Radiotherapy: Understanding the Decision-Making Behaviors of a Hierarchical Deep Reinforcement Learning Based Virtual Treatment Planner Network C Shen*, L Chen, X Jia, (1) University of Texas Southwestern Medical Center, Dallas, TX |
| SU-B-TRACK 6-1 | A Fully Automated Approach for Labeling, Contouring, and Treatment Planning of Vertebral Bodies, Including Automatic Verification to Prevent Errors T Netherton*, C Nguyen, C Cardenas, C Chung, A Klopp, L Colbert, D Rhee, C Peterson, R Howell, P Balter, L Court, MD Anderson Cancer Center, Houston, TX |
| SU-B-TRACK 6-2 | AI Agent Competing with Human in Planning Challenge: A Human-Expert Level Virtual Treatment Planner Network for Prostate Cancer Stereotactic Body Radiation Therapy (SBRT) C Shen1*, C Men2, Y Gao1, L Chen1, X Jia1, (1) University of Texas Southwestern Medical Center, Dallas, TX, (2) Elekta, Newbury Park, CA |
| SU-E-TRACK 5-7 | Prediction of Organ-At-Risk Doses Using An Artificial Intelligence Algorithm: Clinical Validation and Estimated Benefit to Treatment Planning for Lung SBRT P Brodin1*, L Schulte2, D Pappas2, W Martin3, X Shen3, A Basavatia3, N Ohri1, M Garg1, S Kalnicki3, C Carpenter2, W Tome1, (1) Albert Einstein College of Medicine and Montefiore Medical Center, Bronx, NY, (2) Siris Medical, Newburyport, MA, (3) Montefiore Medical Center, New York, NY |
| SU-F-TRACK 5-2 | Assessing the Practicality of Using a Single KBP Model for Multiple Linac Vendors R Douglas*, A Olanrewaju, L Zhang, L Court, University of Texas MD Anderson Cancer Center, Houston, TX |
| SU-F-TRACK 5-5 | Knowledge-Based Cardiac Sparing Treatment Planning for Lung Radiotherapy J Harms*, J Zhang, O Kayode, J Wolf, S Tian, N McCall, K Higgins, R Castillo, X Yang, Winship Cancer Institute of Emory University, Atlanta, GA |
| SU-F-TRACK 5-6 | Deep Learning-Based Dosimetric Plan Quality Assessment for Prostate Radiotherapy Y Nomura*, C Huang, Y Yang, N Kovalchuk, M Surucu, M Buyyounouski, L Xing, Stanford University, Stanford, CA |
| SU-IePD-TRACK 5-5 | Improved Dosimetric Performance of VMAT Total Marrow Irradiation Using Knowledge-Based Planning Approach K Ahn1,2*, M Koshy1,2, J Partouche2, D Rondelli1, P Patel1, Y Hasan2, H Liu2, B Aydogan2, (1) University of Illinois at Chicago, Chicago, IL, (2) University of Chicago, Chicago, IL |
| SU-IePD-TRACK 5-6 | Implementation of a Conditional Generative Adversarial Network for Automatic Prediction of 3D Dose Distributions in Prostate Cancer J Cunningham*, S Zhu, B Luo, Z Dai, N Wen, Henry Ford Health System, Detroit, MI |
| TH-D-TRACK 6-1 | 3D DenseNet for Improving Dose Prediction of Volumetric Modulated Arc Therapy in Prostate Cancer J Fu1*, K Singhrao1, Z Wang1, D Ruan1, D Low1, J Lewis2, X Qi1, (1) Department of Radiation Oncology, UCLA, Los Angeles, CA, (2) Department of Radiation Oncology, Cedars-Sinai Medical Center, Los Angeles, CA |
| TH-D-TRACK 6-3 | Attention-Gated U-Net Implementation in Total Marrow Irradiation Plan Dose Prediction D Du*, K Qing, W Watkins, C Han, T Ketcherside, J Wong, T Williams, A Liu, City of Hope National Medical Center, Duarte, CA |
| TH-D-TRACK 6-4 | Transfer Learning for Fluence Map Prediction in Adrenal Stereotactic Body Radiation Therapy W Wang1,2*, Y Sheng1, M Palta1, B Czito1, C Willett1, F Yin1,2, Q Wu1,2, Q J Wu1,2, Y Ge3, (1) Duke University Medical Center, Durham, NC, (2) Medical Physics Graduate Program, Duke University, Durham, NC, (3) University of North Carolina at Charlotte, Charlotte, 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-D-TRACK 6-6 | Feasibility Study of Cross-Modality IMRT Auto-Planning Guided by a Deep Learning Model G Szalkowski*, X Xu, S Das, P Yap, J Lian, University of North Carolina, Chapel Hill, NC |
| TH-E-TRACK 4-3 | Physician Evaluation of Deep Learning-Based Dose Predictions for Head and Neck Radiotherapy M Gronberg1,2*, S Gay2, B Beadle3, A Olanrewaju2, C Cardenas1,2, R Howell1,2, C Peterson1,2, C Fuller1,2, A Jhingran2, T Netherton1,2, D Rhee1,2, L Court1,2, (1) The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, TX, (2) The University of Texas MD Anderson Cancer Center, Houston, TX, (3) Stanford University, Stanford, CA |
| TH-E-TRACK 6-5 | Demonstrating the Practical Limitation of a Previously Validated Knowledge-Based Planning Model for SIB Lung SBRT of Large (> 5 Cm) Tumors J Visak*, A Webster, M Kudrimoti, R McGarry, M Randall, D Pokhrel, University of Kentucky, Lexington, KY |
| 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-F-TRACK 5-3 | An International Validation of Knowledge-Based Planning A Babier1*, B Zhang1, R Mahmood1, V G L Alves2, A Barragan Montero3, J Beaudry4, C Cardenas5, Y Chang6, Z Chen7, J Chun8, H Eraso9, E Faustmann10, S Gaj11, S Gay5, M Gronberg5, J He12, G Heilemann13, S Hira14, Y Huang15, F Ji16, D Jiang16, J Jimenez Giraldo9, H Lee17, J Lian18, K Liu19, S Liu16, K Marixa9, J Marrugo9, K Miki20, T Netherton5, D Nguyen21, H Nourzadeh22, A Osman23, Z Peng6, J Quinto Munoz9, C Ramsl10, D Rhee5, J Rodriguez Arciniegas9, H Shan24, J V Siebers2, M H Soomro2, K Sun25, A Usuga Hoyos9, C Valderrama9, R Verbeek26, E Wang7, S Willems27, Q Wu16, X Xu18, S Yang28, L Yuan29, S Zhu30, L Zimmermann13, K L Moore31, T G Purdie32, A L McNiven32, T C Y Chan1, (1) University of Toronto, Toronto, CA, (2) University of Virginia Health System, Charlottesville, VA, (3) Universite Catholique de Louvain, Brussels, BE, (4) Memorial Sloan Kettering Cancer Center, New York, NY, (5) University of Texas MD Anderson Cancer Center, Houston, TX, (6) University of Science and Technology of China, Hefei, ,CN, (7) WolHelp Technology (Shenzhen) Co Ltd, CN, (8) Yonsei University, Seoul, KR, (9) National University Of Colombia, CO, (10) Vienna University Of Technology, Vienna, AT, (11) Cleveland Clinic, Cleveland, OH, (12) Shanghai Jiao Tong University, Shanghai, CN, (13) Medical University Of Vienna, Vienna, AT, (14) Johns Hopkins University, Baltimore, MD, (15) Peking University Cancer Hospital & Institute, Beijing, CN, (16) Anhui University, Hefei, CN, (17) Massachusetts General Hospital, Boston, MA, (18) University of North Carolina, Chapel Hill, NC, (19) Taiwan Ai Labs, Taipei, TW, (20) Hiroshima University, Hiroshima, JP, (21) UT Southwestern Medical Center, Dallas, TX, (22) Thomas Jefferson University, Philadelphia, PA, (23) Al-Neelain University, Khartoum, SD, (24) Fudan University, Shanghai, CN, (25) Studio Vodels, Atlanta, GA, (26) Aalto University, Espoo, FI, (27) KU Leuven, Leuven, BE, (28) Sichuan University, Chengdu, CN, (29) Virginia Commonwealth University Medical Center, Richmond, VA, (30) Henry Ford Health System, Detroit, MI (31) UC San Diego, La Jolla, CA, (32) Princess Margaret Cancer Centre, Toronto, CA |
| TH-F-TRACK 5-6 | Understanding Physician's Preference in Treatment Planning of Stereotactic Body Radiation Therapy of Prostate Cancer Y Gao*, C Shen, Y Gonzalez, X Jia, The University of Texas Southwestern Medical Center, Dallas, TX |
| TU-IePD-TRACK 3-7 | Development of a Deep Learning Model That Can Create Multiple Auto-Plans Prioritizing Different Clinical Goals to Facilitate Decision Making in Complex Pancreatic VMAT with Dose Painting Y Wang*, J Wo, T Hong, Department of Radiation Oncology Massachusetts General Hospital, Harvard Medical School, Boston, MA |
| WE-D-TRACK 1-5 | Artificial Intelligence for Dose-Volume Histogram Based Clinical Decision-Making Support System in Radiation Therapy Plans for Brain Tumors P Siciarz*, B McCurdy, S Alfaifi, S Rathod, E Van Uytven, R Koul, CancerCare Manitoba, Winnipeg, MB, Canada |
| WE-IePD-TRACK 3-4 | Powering Predictive Treatment Planning by a Seq2seq Deep Learning Predictor D Lee*, Y Hu, L KUO, S Alam, E Yorke, A Rimner, P Zhang, Memorial Sloan-Kettering Cancer Center, New York, NY |
| WE-IePD-TRACK 6-3 | Modeling the Geometric Relationship Between Targets and OARs for Patient-Specific Plan Quality Evaluation in Head and Neck Radiotherapy E Aliotta*, C Della-Biancia, K Ohri, M Aristophanous, Memorial Sloan Kettering Cancer Center, New York, NY |