Authors: Ke Colin Huang, Zirui Jiang, Joshua Low, Christopher F. Njeh, Oluwaseyi Oderinde, Yong Yue
Affiliation: Purdue University, Indiana University School of Medicine, Department of Radiation Oncology
Abstract Preview: Purpose: Enhancing the accuracy of tumor response predictions enables the development of tailored therapeutic strategies for patients with breast cancer (BCa). In this study, we developed deep-radiomi...
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: Ramesh Boggula, Lincoln Houghton
Affiliation: Karmanos Cancer Institute, Wayne State University
Abstract Preview: Purpose: To evaluate an approach that selectively applies adaptive re-planning only when needed to reduce clinical workload while maintaining treatment quality. Daily adaptive radiotherapy (ART) has t...
Authors: Grant Evans, Maxwell Arthur Kassel, Charles Shang, Stephen Shang
Affiliation: South Florida Proton Therapy Institute, SFPRF
Abstract Preview: Purpose: Proton pencil beam scanning therapy is particularly sensitive to field translational shifts and beam range variations, which can degradation of dose distribution and compromise the treatment....
Authors: Zachary Buchwald, Chih-Wei Chang, Richard L.J. Qiu, Mojtaba Safari, Hui-Kuo Shu, Lisa Sudmeier, Xiaofeng Yang, David Yu, Xiaohan Yuan
Affiliation: Emory University and Winship Cancer Institute, Emory University, Georgia Institute of Technology, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: This study proposes a novel vision-language model (VLM) to predict survival outcomes in glioblastoma (GBM) patients. By integrating multimodal MRI data and clinical information, the proposed ...
Authors: Wenhua Cao, Hadis Moazami Goudarzi, Madison Emily Grayson, Zongsheng Hu, Gino Lim, Steven Hsesheng Lin, Radhe Mohan
Affiliation: The University of Texas MD Anderson Cancer Center, Department of Industrial Engineering, University of Houston, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
Abstract Preview: Purpose: Proton Arc Therapy (PAT) offers significant potential in treating complex cancer cases by delivering a continuous radiation dose as the gantry rotates. This study aims to investigate the pote...
Authors: Lei Xing, Zixia Zhou
Affiliation: Department of Radiation Oncology, Stanford University, Department of Radiation Oncology, Stanford University, Stanford
Abstract Preview: Purpose: Functional brain imaging techniques, such as functional magnetic resonance imaging (fMRI), generate high-dimensional, dynamic data reflecting complex neural processes. However, extracting rob...
Authors: Steve Braunstein, Angela Jakary, Hui Lin, Bo Liu, Janine Lupo, Tiffany Ngan, Ke Sheng, Nate Tran
Affiliation: Radiation Oncology, University of California San Francisco, Graduate Program in Bioengineering, University of California San Francisco-UC Berkeley, Department of Radiation Oncology, University of California San Francisco, Department of Radiology and Biomedical Imaging, University of California San Francisco, Department of Radiation Oncology, University of California, San Francisco
Abstract Preview: Purpose: Current RT clinical target volumes (CTVs) for Glioblastoma (GBM) employ 2cm isotropic expansions of gross tumor volumes. However, studies showed patients still experience progression beyond t...
Authors: Xianjin Dai, PhD, Zhuoran Jiang, Lei Ren, Lei Xing, Zhendong Zhang
Affiliation: University of Maryland School of Medicine, Department of Radiation Oncology, Stanford University, Duke University, Stanford University
Abstract Preview: Purpose: Unsupervised deep learning has shown great promise in deformable image registration (DIR). These methods update model weights to optimize image similarity without necessitating ground truth d...
Authors: Evan Calabrese, Hangjie Ji, Kyle J. Lafata, Casey Y. Lee, Eugene Vaios, Chunhao Wang, Lana Wang, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Duke Kunshan University, North Carolina State University
Abstract Preview: Purpose: To develop a biologically guided deep learning (DL) model for predicting brain metastasis(BM) local control outcomes following stereotactic radiosurgery (SRS). By integrating pre-SRS MR image...
Authors: Yunfei Dong, Dongyang Guo, Zhenyu Yang, Fang-Fang Yin, Zeyu Zhang
Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose:
To develop a Biomechanically Guided Deep Learning Registration Network (BG-DRNet) that improves both accuracy and physiological plausibility in liver image registration. While cone-beam CT...
Authors: Liyuan Chen, Steve Jiang, Chenyang Shen
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center
Abstract Preview: Purpose: Delays in radiation therapy (RT) initiation caused by conventional CT simulation processes can hinder timely treatment delivery and patient outcomes. This study proposes a Virtual Treatment S...
Authors: Sam Beddar, Jason Michael Holmes, Daniel G. Robertson, James J. Sohn, Ethan D. Stolen
Affiliation: Department of Radiation Oncology, Mayo Clinic, MD Anderson Cancer Center, Department of Radiation & Cellular Oncology, University of Chicago, Department of Radiation and Cellular Oncology, University of Chicago
Abstract Preview: Purpose: Camera-based scintillation dosimetry incorporating large volumes have shown promise for fast and comprehensive evaluation of external beam treatment fields. While some efforts have been made ...
Authors: Sam Armstrong, Jamison Louis Brooks, Nicole Johnson, Douglas John Moseley, Cassie Sonnicksen, Erik J. Tryggestad
Affiliation: Mayo Clinic
Abstract Preview: Purpose: To evaluate the feasibility of a shallow learning-based quality assurance (QA) tool designed to assist human reviewers in assessing organ-at-risk (OAR) contours for head and neck radiotherapy...
Authors: Adedamola Adeniyi, Bryan Bednarz, Malick Bio Idrissou, Reinier Hernandez, Ohyun Kwon, Brian W. Miller, Zachary S Morris, Maya Takashima, Jamey Weichert
Affiliation: Departments of Radiation Oncology and Medical Imaging, University of Arizona, Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin - Madison, Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin–Madison, Department of Radiology, School of Medicine and Public Health, University of Wisconsin–Madison, Department of Human Oncology, School of Medicine and Public Health, University of Wisconsin–Madison
Abstract Preview: Purpose: As radiopharmaceutical therapy (RPT) becomes more prevalent in clinical applications, understanding dose-response relationships in the tumor microenvironment (TME) and normal tissues is essen...
Authors: Ming Chao, Hao Guo, Tenzin Kunkyab, Yang Lei, Tian Liu, Kenneth Rosenzweig, Robert Samstein, Junyi Xia, Jiahan Zhang
Affiliation: Icahn School of Medicine at Mount Sinai
Abstract Preview: Purpose:
This study aims to develop and validate an Eclipse Scripting Application Programming Interface (ESAPI)-based planning toolbox that incorporates preset human expertise to improve planning e...
Authors: Xiaoyi Dai, Manju Liu, Weiwei Sang, Pulin Sun, Fan Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Rihui Zhang
Affiliation: Jiahui International Hospital, Radiation Oncology, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose:
Current deep learning-based sparse-view CBCT reconstruction methods are prone to hallucinatory artifacts, as they rely on inferred image details that may not correspond to true anatomical ...
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: Jacob S. Buatti, Kristen A. Duke, Malena Fassnacht, Neil A. Kirby, Parker New, Niko Papanikolaou, Arkajyoti Roy, Yuqing Xia, Michelle de Oliveira
Affiliation: The University of Texas San Antonio, UT Southwestern Medical Center, UT Health San Antonio
Abstract Preview: Purpose:
Quantifying and visualizing uncertainty is critical for building clinical trust in AI-generated dose distributions. This study evaluates Monte Carlo Dropout (MCD), Snapshot Ensemble (SE), ...
Authors: Raghavendra Raghavendra, Kanaparthy Raja Muralidhar, Venkataramanan Ramachandran, Srinivas Srinivas
Affiliation: Karkinos Healthcare
Abstract Preview: Purpose: This study explores the Integrating AI-Generated Auto Contours via Ray Station’s Deep Learning Segmentation into Diverse Treatment Planning Systems.
Methods: The research encompassed a gro...
Authors: Thomas L. Hayes, Nicholas C. Koch, Han Liu, Qingyang (Grace) Shang, Benjamin J. Sintay, Caroline Vanderstraeten, David B. Wiant
Affiliation: Fuse Oncology, Cone Health, Cone Health Cancer Center
Abstract Preview: Purpose:
This study evaluates the accuracy of a deep learning-based automatic breast planning script in predicting beam energy for breast cancer treatments. The script was validated and implemented...
Authors: Pouya Azarbar, Nima Kasraie, Mahsa Shahrbabki Mofrad, Peyman Sheikhzadeh
Affiliation: UT Southwestern Medical Center, Shahid Beheshti University of Medical science, Imam Khomeini Hospital Complex,Tehran University of Medical Sciences, Tehran University of Medical Science
Abstract Preview: Purpose: PET imaging become crucial in diagnosing and managing various diseases, but its key limitation is the lack of detailed anatomical information. Integrating CT-scans with PET images enhances cl...
Authors: Kofi M. Deh, Tamas Jozsa, Tsang-Wei Tu
Affiliation: Cranfield University, Howard University Hospital, Howard University
Abstract Preview: Purpose: To enhance the quality of hyperpolarized (HP) 13C magnetic resonance images by integrating deep learning with perfusion modeling.
Methods: A convolutional neural network (CNN) and a superr...
Authors: Nobuki Imano, Daisuke Kawahara, Akito S Koganezawa, Yuji Murakami, Ikuno Nishibuchi, Takuya Wada
Affiliation: Department of Information and Electronic Engineering, Faculty of Science and Engineering, Teikyo University, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University
Abstract Preview: Purpose: Adaptive radiotherapy (ART) compensates for treatment plans based on anatomical changes while not considering biological effects such as interruptions during treatment. This study aims to dev...
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: Shahed Badiyan, Chien-Yi Liao, Mu-Han Lin, Dan Nguyen, Justin D. Visak, Hui Ju Wang, Brien Timothy Washington, Kenneth Westover, Yuanyuan Zhang
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, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Knowledge-based planning (KBP) plays a crucial role in improving treatment plans by leveraging previous clinical data to guide new cases. KBP is applied to the Ethos 2.0 Intelligent Optimizat...
Authors: Jue Jiang, Aneesh Rangnekar, Shiqin Tan, Harini Veeraraghavan
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Weill Cornell Graduate School of Medical Sciences
Abstract Preview: Purpose: Clinicians often use information from FDG-PET and CT to interpret and delineate gross tumor (GTVp) and nodal (GTVn) volumes for radiotherapy planning in head and neck (HN) cancer patients. He...
Authors: Ali Ajdari, Thomas R. Bortfeld, Christopher Bridge, Gregory Buti, Marcela Giovenco, Fredrik Lofman, Gregory C. Sharp, Helen A Shih, Tugba Yilmaz
Affiliation: Massachusetts General Hospital, RaySearch Laboratories, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Massachusetts General Hospital and Harvard Medical School
Abstract Preview: Purpose: Defining radiation target volumes with accurate integration of the neuroanatomy is one of the major difficulties in designing glioma treatments. We developed a deep learning network for norma...
Authors: Nesrin Dogan, Panagiota Galanakou, Robert Kaderka
Affiliation: University of Miami, Sylvester Comprehensive Cancer Center, University of Miami Sylvester Comprehensive Cancer Center
Abstract Preview: Purpose:
To develop knowledge-based treatment planning (KBP) for volumetric modulated arc therapy (VMAT) in chest wall treatments with regional nodal involvement. Given the challenges posed due to ...
Authors: John Byun, Steven D Chang, Mingli Chen, Cynthia Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Hao Jiang, Mahdieh Kazemimoghadam, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Qingying Wang, Zi Yang, Xianghua Ye, Kangning Zhang
Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford University, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Accurate prediction of pain relief is crucial in determining the clinical effectiveness of Stereotactic body radiotherapy (SBRT) regimen for spine metastases. We propose a deep-learning frame...
Authors: Guha Balakrishnan, Osama R. Mawlawi, Yiran Sun, Ashok Veeraraghavan
Affiliation: RICE University, UT MD Anderson Cancer Center
Abstract Preview: Purpose:
Previous deep learning (DL) techniques such as X2CT-GAN [1] has shown great promise in reconstructing realistic CT volume from biplanar X-rays, however they introduce numerous artifacts in...
Authors: Ahad Ollah Ezzati, Xiaoyu Hu, Xun Jia, Youfang Lai, Kai Yang, Yuncheng Zhong
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Massachusetts General Hospital, Johns Hopkins University
Abstract Preview: Purpose: Contrast-enhanced breast cone-beam computed tomography (bCBCT) provides high-resolution, 3D imaging of breast tissues with improved differentiation between normal and abnormal tissues. Curren...
Authors: Todd A Aguilera, Bassel Dawod, Sebastian Diegeler, Eslam Elghonaimy, Purva Gopal, Jiaqi Liu, Hao Peng, Arely Perez Rodriguez, Nina N. Sanford, Robert Timmerman, Megan B Wachsmann, Haozhao Zhang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center
Abstract Preview: Purpose: This study pioneers the integration of CODEX (co-detection by indexing)-based spatial profiling and advanced computational techniques to investigate the tumor immune microenvironment (TIME) i...
Authors: Afrouz Ataei, Xinhui Duan, Mi-Ae Park, Liqiang Ren
Affiliation: Department of Radiology, UT Southwestern Medical Center, UT Southwestern Medical Center, Rush University
Abstract Preview: Purpose:
Photon-counting CT (PCCT) has become commercially available recently, offering significant potential to enhance patient care. However, it also introduces unique challenges. One such challe...
Authors: Stephen R. Bowen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Jiajie Wang, Shouyi Wang, Faisal Yaseen, Han Zhou
Affiliation: Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Fred Hutchinson Cancer Center, University of Texas at Arlington
Abstract Preview: Purpose: Predicting the effects of the spatial-temporal tumor response to chemoradiation can assist in adjusting radiation dose and support clinical decision-making in radiotherapy. A multi-instance l...
Authors: Shreyas Anil, Jason Chan, Arushi Gulati, Yannet Interian, Hui Lin, Benedict Neo, Andrea Park, Bhumika Srinivas
Affiliation: Department of Otolaryngology Head and Neck Surgery, University of California San Francisco, Department of Data Science, University of San Francisco, Department of Radiation Oncology, University of California San Francisco
Abstract Preview: Purpose: Hospital readmission prediction models often rely on structured Electronic Health Record (EHR) data, overlooking critical insights from unstructured clinical notes. This study presents a mult...
Authors: Seungtaek Choi, Laurence Edward Court, Eun Young Han, Yusung Kim, Hunter S. Mehrens, Tucker J. Netherton, Shiqin Su
Affiliation: The University of Texas MD Anderson Cancer Center, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center
Abstract Preview: Purpose: Automated treatment planning is gaining traction for its enhanced consistency and efficiency. A key challenge, however, lies in the inability of neural network dose predictions directly trans...
Authors: Hilary P Bagshaw, Mark K Buyyounouski, Xianjin Dai, PhD, Praveenbalaji Rajendran, Lei Xing, Yong Yang
Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School
Abstract Preview: Purpose: Artificial intelligence (AI)-driven methods have transformed dose prediction, streamlining the automation of radiotherapy treatment planning. However, traditional approaches depend exclusivel...
Authors: Jeremy T. Booth, Martin Andrew Ebert, Robert Finnegan, Annette Haworth, George Hruby, Burhan Javed, Kazi Ridita Mahtaba, Leyla Moghaddasi, Yutong Zhao
Affiliation: Northern Sydney Cancer Centre, Royal North Shore Hospital, The University of Sydney, The University of Western Australia, Genesis Care, Rockhampton Hospital
Abstract Preview: Purpose:
To evaluate the efficacy of an atlas-based tumor control probability (TCP) model in predicting prostate cancer (PCa) recurrence by retrospectively integrating patient-specific primary radi...