Authors: Steven DiBiase, Gurtej S. Gill, Haohua Billy Huang, Nicholas J. Lavini, Luxshan Shanmugarajah, Salar Souri, Samantha Wong
Affiliation: Stony Brook University, Northwell Health, Cornell University, NewYork-Presbyterian, New York-Presbyterian
Abstract Preview: Purpose: Clinical applications of deep learning-based algorithms have come to the radiation oncology field as organ at risk (OAR) auto contouring programs. We evaluated two of these algorithms’ (Radfo...
Authors: Theodore Higgins Arsenault, Kenneth W. Gregg, Beatriz Guevara, Lauren E Henke, Angela Jia, Rojano Kashani, Kyle O'Carroll, Alex T. Price, Adithya Reddy, Atefeh Rezaei, Daniel E Spratt, Runyon C. Woods
Affiliation: University Hospitals Seidman Cancer Center
Abstract Preview: Purpose: To evaluate the effect of unedited AI-generated contours used for online adaptive radiotherapy (FLOW-ART) on the plan quality of prostate treatments as compared to non-adaptive (non-ART) proc...
Authors: Jiayi Chen, Manju Liu, Ning Wen, Haoran Zhang, Yibin Zhang
Affiliation: Department of Radiation Oncology, Ruijin Hospital, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Department of Radiation Oncology,Ruijin Hospital, Shanghai Jiao Tong University School of Medicine
Abstract Preview: Purpose: This study introduces a novel Dual Energy CT (DECT)-Guided Intelligent Radiation Therapy (DEIT) platform designed to streamline and optimize the radiotherapy process. The DEIT system combines...
Authors: Shari Damast, Svetlana Kuznetsova, Christopher J. Tien
Affiliation: Yale University School of Medicine, Department of Therapeutic Radiology, Yale University School of Medicine
Abstract Preview: Purpose: Current contour similarity evaluation approaches (Dice Similarity Coefficient, Mean Distance to Agreement) are limited to geometric agreement without assessment of ultimate dosimetric impact....
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: Indrin J. Chetty, Jing Cui, Mitchell Kamrava, Tiffany M. Phillips, Jennifer M. Steers, Brad Stiehl
Affiliation: Department of Radiation Oncology,Cedars-Sinai Medical Center, Cedars-Sinai Medical Center
Abstract Preview: Purpose: Auto-contouring for HDR interstitial brachytherapy can be confounded by large deformation in anatomy and image quality. Here we evaluated the performance of an AI-based auto-contouring softwa...
Authors: Silambarasan Anbumani, Nicolette O'Connell, Eenas A. Omari, Amanda Pan, Eric S. Paulson, Lindsay Puckett, Monica E. Shukla, Dan Thill, Jiaofeng Xu
Affiliation: Elekta Inc, Elekta Limited, Linac House, Department of Radiation Oncology, Medical College of Wisconsin
Abstract Preview: Purpose: Accurate electron density information from on-board imaging is essential for direct dose calculations in adaptive radiotherapy (ART). This study evaluates a deep learning model for thoracic s...
Authors: Lavsen Dahal, Francesco Ria, Ehsan Samei, Justin B. Solomon, Liesbeth Vancoillie, Yakun Zhang
Affiliation: Duke University, Carilion Clinic, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose: Clinical diagnostic task-based optimization of CT procedures require precise and organ-specific assessments. This study investigates inter-organ noise variability to highlight the limitations...
Authors: Mylinh Dang, Laila A Gharzai, Xinlei Mi, Poonam Yadav
Affiliation: Northwestern Feinberg School of Medicine, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern Medicine
Abstract Preview: Purpose: Delineation of organs at risk (OAR) in the head/neck region requires substantial physician time. Many artificial intelligence (AI) based auto-contouring software are commercially available. T...
Authors: Austin Castelo, Xinru Chen, Caroline Chung, Laurence Edward Court, Jaganathan A Parameshwaran, Zhan Xu, Jinzhong Yang, Yao Zhao
Affiliation: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Imaging Physics, The University of Texas MD Anderson Cancer Center
Abstract Preview: Purpose:
To develop a deep learning-based segmentation model to automatically delineate tumors from full-body PET/CT images.
Methods:
PET/CT image pairs of 91 patients were collected for this...
Authors: Kristen A. Duke, Samer Jabor, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia
Affiliation: St. Mary's University, The University of Texas San Antonio, UT Health San Antonio
Abstract Preview: Purpose:
The Segment Anything Model (SAM) is a foundational box-prompt-based model for natural image segmentation. However, its applicability to zero-shot 3D medical image segmentation, particularl...
Authors: Aditya P. Apte, Joseph O. Deasy, Jue Jiang, Nancy Lee, Sudharsan Madhavan, Nishant Nadkarni, Lopamudra Nayak, Harini Veeraraghavan, Wei Zhao
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: To track early response to radiotherapy using digital twins, it is crucial to quantify tumor volume and mass changes. Traditional tumor detection methods, particularly in image registration, ...
Authors: Laura I. Cervino, Karen Episcopia, Hsiang-Chi Kuo, Sangkyu Lee, Seng Boh Gary Lim, Shih-Chi Lin, Grace Tang
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: This study evaluated the performance of the HyperSight Cone-Beam Computed Tomography (CBCT) system on a TrueBeam C-arm LINAC (TB) and two Ethos ring-gantry LINACs (ES) for adaptive radiation ...
Authors: Stephen Balter, Haegin Han, Cari M Kitahara, Taeeun Kwon, Choonsik Lee, Martha S Linet, Donald L. Miller
Affiliation: Columbia University Medical Center, National Cancer Institute, Food and Drug Administration
Abstract Preview: Purpose: Staff in fluoroscopically guided interventional (FGI) procedures are exposed to radiation. Inhomogeneous radiation fields and variability in procedural conditions make it challenging to deriv...
Authors: Benjamin Haibe-Kains, Ruiyan Ni, Alexandra Rink
Affiliation: Department of Medical Biophysics, University of Toronto, University Health Network
Abstract Preview: Purpose: Accurate auto-segmentation for targets and organs-at-risk (OARs) using deep learning reduces the delineating time in radiotherapy. In high-dose-rate brachytherapy, specific clinical criteria ...
Authors: Wesley E. Bolch, Lotem Buchbinder Shadur, Natalia Estefania Carrasco-Rojas, Chansoo Choi, Robert Joseph Dawson, Shaheen Dewji, John Francis, Emmanual Mate-Kole, Wyatt Smither, Yitian Wang
Affiliation: Georgia Tech, University of Florida, Nuclear and Radiological Engineering and Medical Physics Programs, Georgia Institute of Technology
Abstract Preview: Purpose: Develop a software application capable of assessing the radiation contamination of military service members and members of the general public for fast, on-site radiological triage. The softwa...
Authors: Xiance Jin
Affiliation: 1st Affiliated Hospital of Wenzhou Medical University
Abstract Preview: Purpose:
Deep learning deformable registration models was proposed to predict optimal dose distributions a with a few of optimal planned doses using a few-shot learning for cervical cancer.
Meth...
Authors: Yang Lei, Tian Liu, Ren-Dih Sheu, Meysam Tavakoli, Jing Wang, Kaida Yang, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai, Department of Radiation Oncology, Emory University
Abstract Preview: Purpose:
The study aimed to improve target and organ at risk (OAR) segmentation in low-dose-rate brachytherapy (LDR-BT) for prostate cancer treatment, by integrating clinical guidelines into deep l...
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: Wookjin Choi, Jun Li
Affiliation: Thomas Jefferson University
Abstract Preview: Purpose: Resin Yttrium-90 (Y-90) selective internal radiation therapy (SIRT) is a radioembolization procedure which uses Y-90 microspheres to treat metastatic liver cancer. In the procedure, liver vol...
Authors: Gregory T. Armstrong, James E. Bates, Lei Dong, Ralph Ermoian, Jie Fu, Christine Hill-Kayser, Rebecca M. Howell, Sharareh Koufigar, John T. Lucas, Thomas E. Merchant, Tucker J. Netherton, Sogand Sadeghi
Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Epidemiology and Cancer Control, St. Jude Children’s Research Hospital, St. Jude Children's Research Hospital, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, St. Jude Children’s Research Hospital, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology, University of Washington/ Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: This study evaluates the adaptability and limitations of commercially available (MIM, RayStation) tools trained on predominately adult datasets (ages 20–60+ years) for delineating organs at r...
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: Neha Devi, Deon M. Dick
Affiliation: Jaeger Corporation
Abstract Preview: Purpose: To evaluate the impact of bladder volume variation on small bowel dose in prostate cancer patients with nodal involvement and assess the efficacy of a 'half-full bladder' guideline for minimi...
Authors: Elizabeth L. Covington, Robert T. Dess, Charles S. Mayo, Michelle L. Mierzwa, Dan Polan, Jennifer Shah, Claire Zhang
Affiliation: University of Michigan, Department of Radiation Oncology, University of Michigan
Abstract Preview: Purpose: Auto-segmentation improves contour consistency and standardization in radiation therapy but may introduce variations from current practices, potentially impacting treatment outcomes and toxic...
Authors: Jihun Kim, Jin Sung Kim, Jun Won Kim, Yong Tae Kim, Chanwoong Lee, Jihyn Pyo, Young Hun Yoon
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine
Abstract Preview: Purpose: Although segmentation foundation models have recently demonstrated promising zero-shot performance on natural images, its clinical application to magnetic resonance (MR) images still requires...
Authors: Cheng-En Hsieh, Shen-Hao Li, Hsin-Hon Lin, Shu-Wei Wu, An-Ci Yang
Affiliation: Department of Medical Imaging and Radiological Sciences, Chang Gung University, Proton and Radiation Therapy Center, Chang Gung Memorial Hospital, Proton and Radiation Therapy Center, Chang Gung Memorial Hospital Linkou
Abstract Preview: Purpose:
The aim of this study is to develop a framework of generating patient-specific phantom tailored for head and neck proton therapy. From these phantoms, digital reference objects based on th...
Authors: Wesley E. Bolch, Chansoo Choi, Chan Hyeong Kim, Suhyeon Kim, Bangho Shin, Yeon Soo Yeom
Affiliation: Hanyang University, University of Florida, 2) Department of Radiation Convergence Engineering, Yonsei University
Abstract Preview: Purpose: Task Group 103 of International Commission on Radiological Protection (ICRP) recently released new-generation adult and pediatric mesh-type reference computational phantoms (MRCPs) via ICRP P...
Authors: John Ginn, Chenlu Qin, Deshan Yang
Affiliation: Duke University, Department of Radiation Oncology, Duke University
Abstract Preview: Purpose: Clinical implementation of auto-segmentation tools has been hindered by poor interpretability and generalizability of AI models, necessitating the development of automated contour quality ass...
Authors: Max Chen, Sean Marquardt, Ben Yang, Kai Yang
Affiliation: Cary Academy, Massachusetts General Hospital, Winchester High School
Abstract Preview: Purpose: To analyze the impact of scanner model variation on the effective dose conversion factor (“k-factor”), which is most commonly used for CT effective dose calculation.
Methods: The stand...
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: Yukio Fujita, Syoma Ide, Fumiki Ito, Kei Ito, Satoshi Kito, Keiko Murofushi, Yujiro Nakajima, Yuhi Suda, Kentaro Taguchi, Naoki Tohyama, Fumiya Tsurumaki, Riku Watanabe
Affiliation: Komazawa University Graduate School, Komazawa University, Tokyo Metropolitan Cancer and Infectious Diseases Center Komagome Hospital
Abstract Preview: Purpose: Spine stereotactic body radiotherapy (SBRT) demands high precision due to the target's proximity to the spinal cord and steep dose gradients, which complicate treatment planning. Consequently...
Authors: Caroline Chung, Michael Knopp, Stephen F. Kry, Hunter S. Mehrens, John Rong
Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center, University of Cincinnati
Abstract Preview: Purpose: To evaluate the variability of CT dose index (CTDIvol) and radiomics features across a large cohort of radiotherapy simulation CT scans from multiple institutions.
Methods: Three IROC phan...
Authors: Ming Chao, Thomas Chum, Tenzin Kunkyab, Yang Lei, Tian Liu, Richard G Stock, Hasan Wazir, Junyi Xia, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai
Abstract Preview: Purpose:
This study aims to develop effective strategies for multi-organ segmentation of pelvic cone-beam computed tomography (CBCT) images based on transformer models to facilitate adaptive radiat...
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: Ali Ammar, Quan Chen, Jingwei Duan, Yi Rong, Nathan Y. Yu, Libing Zhu
Affiliation: Mayo Clinic Arizona, University of Alabama at Birmingham
Abstract Preview: Purpose: Clinical performance of deep learning-based auto-segmentation (DLAS) can degrade over time due to AI “aging” from unseen data input compared to the initial model training data. This study aim...
Authors: Em Harkness, Francesco Ria, Ehsan Samei
Affiliation: Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose:
A recently introduced mathematical method quantifies performance-based clinical risk to create a risk-to-risk assessment with radiation risk, rendering the so-called total risk. However, t...
Authors: Ramesh Boggula, Yash Somnay, Hualin Zhang
Affiliation: Radiation Oncology, Keck School of Medicine of USC, Wayne State University
Abstract Preview: Purpose: Spatially fractionated radiation therapy (SFRT) allows delivery of therapeutic doses to bulky tumors otherwise limited by dose-volume effects that threaten organ-at-risk tolerances. For negle...
Authors: Keiichi Jingu, Noriyuki Kadoya, Takafumi Komiyama, Takeru Nakajima, Hikaru Nemoto, Hiroshi Onishi, Masahide Saito, Ryota Tozuka
Affiliation: Department of Radiology, University of Yamanashi, Department of Radiation Oncology, Tohoku University Graduate School of Medicine, Department of Advanced Biomedical Imaging, University of Yamanashi
Abstract Preview: Purpose: We evaluated the accuracy of a new AI-based fully automated planning software in stereotactic body radiotherapy (SBRT) for early-stage lung cancer.
Methods: We collected data from 125 pati...
Authors: Jiali Gong, Yi Guo, Chi Han, Wanli Huo, Hongdong Liu, Zhao Peng, Yaping Qi, Zhaojuan Zhang
Affiliation: Department of Radiotherapy, cancer center, The First Affiliated Hospital of Fujian Medical University, Department of Oncology, Xiangya Hospital, Central South University, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, China Jiliang University, Division of lonizing Radiation Metrology, National Institute of Metrology, Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University
Abstract Preview: Purpose: To address overfitting from limited training data in multi-organ segmentation, an efficient transfer learning framework is proposed. It reduces reliance on training samples, enabling a single...
Authors: Daming LI, Jinsen Xie, Zhe Zhang
Affiliation: Peking University Shenzhen Hospital Radiotherapy Department, School of Nuclear Science and Technology, University of South China
Abstract Preview: Purpose: To analyze the actual doses received during radiotherapy for head and neck cancers (HNC) using Velocity, providing insights for adaptive radiotherapy decision-making.
Methods: Thirty-three...
Authors: Bryan Bednarz, Laura Bennett, Abby E. Besemer, Tyler J Bradshaw, Steve Y Cho, John M. Floberg, Joseph Grudzinski, Elissa Khoudary, Michael J. Lawless
Affiliation: Department of Radiology, University of Wisconsin, University of Wisconsin-Madison Department of Medical Physics, Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin - Madison, Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Voximetry, Inc, University of Pennsylvania, Department of Radiology, University of Wisconsin - Madison, Department of Human Oncology, University of Wisconsin-Madison
Abstract Preview: Purpose: To assess the impact of using a standardized immobilization setup for multi-time-point SPECT/CT imaging on radiopharmaceutical therapy (RPT) dosimetry image registration.
Methods: Ten ...
Authors: Yunxiang Li, Weiguo Lu, Xiaoxue Qian, Hua-Chieh Shao, You Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
Curating high-quality, labeled data for medical image segmentation can be challenging and costly, considering the existence of various image domains with differing modalities/protocols. Cr...
Authors: Jared Baggett, Wesley E. Bolch, Lukas M. Carter, Robert Joseph Dawson, Laura Dinwiddie, Gunjan Kayal, Adam Leon Kesner, Harry Marquis, Juan Camilo Ocampo Ramos, Stefan Wehmeier
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, University of Florida, MD Anderson
Abstract Preview: Purpose: Accurate organ dose estimation in computed tomography (CT) has gained attention due to the increasing global utilization of CT imaging. MIRDct, a specialized CT dosimetry software, has recent...
Authors: Klaus Bacher, Louise D'hondt, Jeff Rutten, Gwenny Verfaillie
Affiliation: Ghent University
Abstract Preview: Purpose: Manual organ segmentation is a very time-consuming but necessary process in personalized dosimetry. Automatic segmentation tools may alleviate this task. In this study the impact of automatic...
Authors: Michael Baine, Yang Lei, Yu Lei, Ruirui Liu, Tian Liu, Jing Wang
Affiliation: Icahn School of Medicine at Mount Sinai, University of Nebraska Medical Center
Abstract Preview: Purpose: Accurate 3D deformable registration of MRI and ultrasound (US) is essential for real-time image guidance during high-dose-rate (HDR) prostate brachytherapy. However, MRI-US registration of th...