Authors: Gabriel Lucas Andrade de Sousa, Einsley-Marie Janowski, Cam Nguyen, Krishni Wijesooriya
Affiliation: Department of Radiation Oncology, University of Virginia, Department of Physics, University of Virginia
Abstract Preview: Purpose: Optimizing radiation therapy (RT) to spare the immune system may improve Overall Survival (OS) in cancer patients. This study develops a computational algorithm to identify optimal dose-volum...
Authors: Katherine Brown, Will Buchsbaum, Mylinh Dang, Joshua Dean, Sameer Faruquee, Gracie Huff, Claudia Maldonado, Gaby Min, David O'Neill, Gemily Wang, Poonam Yadav
Affiliation: Northwestern University, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern Medicine
Abstract Preview: Purpose:
To establish an abdominal imaging phantom that includes deformable organs including the stomach, duodenum, and pancreas. This phantom will be used in routine calibration of an MRI-LINAC sy...
Authors: Asma Amjad, Renae Conlin, Beth A. Erickson, William Hall, Eric S. Paulson, Christina M. Sarosiek
Affiliation: Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: The adapt-to-shape (ATS) workflow on the MR-Linac involves manual contour edits followed by treatment plan reoptimization on daily pre-beam MRIs. A verification image is acquired after plan o...
Authors: Shahed Badiyan, Tsuicheng D. Chiu, Viktor M. Iakovenko, Steve Jiang, Christopher Kabat, Mu-Han Lin, Roberto Pellegrini, Arnold Pompos, Edoardo Salmeri, David Sher, Sruthi Sivabhaskar, Justin D. Visak
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, Global Clinical Science, Elekta AB, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Adaptive treatment planning requires robust strategies to enable streamlined on-couch processes, creating a significant barrier for planners transitioning from conventional to adaptive planni...
Authors: Zixu Guan, Takahiro Iwai, Takashi Mizowaki, Mitsuhiro Nakamura, Michio Yoshimura
Affiliation: Kyoto University, Department of Radiation Oncology and Image-applied Therapy, Graduate School of Medicine, Kyoto University
Abstract Preview: Purpose:
The goal of this study is to develop a fully automated treatment planning approach for VMAT in pancreatic cancer that can convert patient anatomy into LINAC machine parameters. In this wor...
Authors: Kenneth W. Gregg, Beatriz Guevara, Lauren E Henke, Rojano Kashani, Kyle O'Carroll, Gisele Castro Pereira, Christian Erik Petersen, Alex T. Price, Meiying Xing, Reine abou Zeidane
Affiliation: university hospital, University Hospitals Seidman Cancer Center
Abstract Preview: Purpose:
Experimental data have shown the inconsistent monitor unit and target coverage in Ethos 1.1. This can lead to inaccurate dose delivery, compromising patient safety and treatment outcomes. ...
Authors: Jennifer L. Dolan, Chengyin Li, Parag Parikh, Doris N. Rusu, Kundan S Thind
Affiliation: Henry Ford Health, Cedars-Sinai Medical Center
Abstract Preview: Purpose: The time and resource demands of online Adaptive Radiation Therapy (ART) can limit its widespread clinical adoption and potentially impact patient throughput. To address this, we developed a ...
Authors: Christopher G. Ainsley, Pradeep Bhetwal, Yingxuan Chen, Wookjin Choi, Vimal K. Desai, Karen E. Mooney, Adam Mueller, Hamidreza Nourzadeh, Yevgeniy Vinogradskiy, Maria Werner-Wasik
Affiliation: Thomas Jefferson University
Abstract Preview: Purpose: MR-guided adaptive radiotherapy (MRgART) has demonstrated improved outcomes for patients with pancreatic cancer. However, the time-consuming re-segmentation of targets and organs-at-risk (OAR...
Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek
Affiliation: Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: In an effort to improve contouring accuracy for abdominal MR guided online adaptive radiotherapy (MRgOART), patient-specific deep learning-based auto-segmentation (PS-DLAS) has been proposed....
Authors: Asma Amjad, Renae Conlin, Eric S. Paulson, Christina M. Sarosiek
Affiliation: Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose:
MR-guided adaptive radiation therapy (MRgART) is transforming clinical workflows, requiring fast, accurate organs-at-risk (OARs) contouring. While deep learning auto-segmentation (DLAS) of...
Authors: Karyn A Goodman, Yang Lei, Tian Liu, Pretesh Patel, Jing Wang, Kaida Yang, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: This study aims to improve organ-at-risk (OAR) segmentation in pancreatic cancer stereotactic body radiotherapy (SBRT) by integrating clinical guidelines into deep learning workflows. We use ...
Authors: Samuel L. Brady, Shruti Hegde, Alexander Knapp, Usman Mahmood, Joseph G. Meier, Elanchezhian Somasundaram, Zachary Taylor
Affiliation: Cincinnati Children's Hospital Medical Ctr, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Cincinnati Children's Hospital Medical Center, Cincinnati Childrens Hospital Med Ctr
Abstract Preview: Purpose:
To assess how two benchmark multi-organ CT segmentation models respond to varying image noise levels.
Methods:
This study utilized the pediatric CT dataset from The Cancer Imaging Ar...
Authors: James M. Balter, Lando S. Bosma, Jorge Tapias Gomez, Nishant Nadkarni, Mert R Sanbuncu, William Paul Segars, Ergys D. Subashi, Neelam Tyagi, Harini Veeraraghavan
Affiliation: University of Michigan, The University of Texas MD Anderson Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Carl E. Ravin Advanced Imaging Laboratories and Center for Virtual Imaging Trials, Duke University Medical Center, Cornell University, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: Develop patient-specific virtual digital twin (VDT) cohorts modeling physically realistic spatio-temporal gastrointestinal (GI) organs (stomach and duodenum) digestive motion.
Methods: Pat...