Authors: Steve Braunstein, Yannet Interian, Hui Lin, Bo Liu, Janine Lupo, Olivier Morin, Benedict Neo
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 Data Science, University of San Francisco, University of San Francisco
Abstract Preview: Purpose: Large Language Models (LLMs) demonstrate strong general text comprehension but remain limited in oncology due to insufficient contextual alignment. We pilot embedding alignment through radiol...
Authors: Dequan Chen, Jason Michael Holmes, Tianming Liu, Wei Liu, Zhengliang Liu, Jiajian Shen, Peilong Wang
Affiliation: Department of Radiology, Mayo Clinic, Department of Radiation Oncology, Mayo Clinic, School of Computing, University of Georgia
Abstract Preview: Purpose:
We present a study to evaluate the performance of large language models (LLMs) in answering radiation oncology physics questions, focusing on the recently released models.
Methods:
A...
Authors: Zachary Buchwald, Zach Eidex, Richard L.J. Qiu, Justin R. Roper, Mojtaba Safari, Hui-Kuo Shu, Xiaofeng Yang, David Yu
Affiliation: Emory University and Winship Cancer Institute, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Gadolinium-based contrast agents (GBCA) are commonly used for patients with gliomas to delineate and characterize the brain tumors using T1-weighted (T1W) MRI. However, there is a rising conc...
Authors: Sean P. Devan, Cory S. Knill, Charles K. Matrosic, Zheng Zhang
Affiliation: University of Michigan
Abstract Preview: Purpose: Physicists troubleshooting machine issues during patient treatments often face high-pressure situations, balancing error codes, resource constraints, and time-sensitive decisions. To streamli...
Authors: Jeremy Christophel, Zhihua Qi
Affiliation: Henry Ford Health
Abstract Preview: Purpose: To demonstrate a method to compare DICOM metadata from clinical scanners with institutional protocols as validation that clinical use matches the master protocol.
Methods: DICOM metadata i...
Authors: Shahed Badiyan, Bin Cai, Tu Dan, Michael Dohopolski, Steve B. Jiang, Deepkumar Mistry, Arnold Pompos, Robert Timmerman, Jing Wang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & 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, University of Texas Southwestern Medical Center, Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose: Biology-guided radiotherapy (BGRT) offers significant potential for personalized and adaptive cancer treatment, with clinically available systems such as SCINTIX from Reflexion now being intr...
Authors: Klea Hoxha, Dylan P. O'Connell, Ricky R Savjani
Affiliation: Department of Radiation Oncology, University of California, Los Angeles, UCLA Radiation Oncology
Abstract Preview: Purpose: Ambiguities in physician simulation orders lead to workflow disruptions during CT simulation. Often, information that could provide
helpful context to simulation therapists and planners is...
Authors: Steve B. Jiang, Mu-Han Lin, Dan Nguyen, Beiqian Qi, Daniel Yang, Ying 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
Abstract Preview: Purpose:
Online adaptive radiotherapy (oART) is a resource-intensive workflow requiring significant time and effort required from clinicians, particularly for the online evaluation of plan quality....
Authors: Arjit K. Baghwala, Sunan Cui, Jessica Fagerstrom, Eric C. Ford, Kristi Rae Gayle Hendrickson, Sharareh Koufigar, Samuel Ming Ho Luk, Bishwambhar Sengupta, Afua A. Yorke
Affiliation: University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Radiation Oncology, University of Vermont Medical Center, University of Washington and Fred Hutchinson Cancer Center, Houston Methodist Hospital
Abstract Preview: Purpose: The global burden of cancer continues to rise, leading to an increased workload in radiation oncology clinics. This surge is not only due to the growing demand for treatment machines and moda...
Authors: Michael Dohopolski, Xuejun Gu, Hao Jiang, Steve B. Jiang, Christopher Kabat, Jingying Lin, Weiguo Lu, Michael Tang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center, Neuralrad LLC, 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: To streamline access to clinical data stored in Oncology Information Systems such as MOSAIQ or ARIA, we developed an AI-powered chatbot capable of querying, summarizing, and interactively ans...
Authors: Aditya P. Apte, Joseph O. Deasy, Sharif F. Elguindi, Aditi Iyer, Jue Jiang, Eve Marie LoCastro, Jung Hun Oh, Amita Shukla-Dave, Harini Veeraraghavan
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: We present publicly shareable applications (apps) for AI-based radiotherapy segmentation workflows with pyCERR on Seven Bridges Cancer Genomics Cloud-based platform (CGC-SB)
Methods: Runni...
Authors: Rafe A. McBeth, Ayoola Okuribido, Rodney D. Wiersma
Affiliation: Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, UCLA
Abstract Preview: Purpose: To develop a method for standardizing data collected during quality assurance checks across institutions using language models.
Background: QA procedures and data management can vary widel...
Authors: Rafe A. McBeth, Ayoola Okuribido, Rodney D. Wiersma
Affiliation: Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania, UCLA
Abstract Preview: Purpose: To develop a method for standardizing data collected during quality assurance checks across institutions using language models.
Background: QA procedures and data management can vary widel...
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: Meiyun Cao, Edward L. Clouser, Xiaoning Ding, Jason Michael Holmes, Shaw Hu, Linda L. Lam, Wendy S. Lindholm, Wei Liu, Samir H. Patel, Diego Santos Toesca, Jason Sharp, Sujay A. Vora, Peilong Wang
Affiliation: Department of Radiation Oncology, Mayo Clinic, Mayo Clinic Arizona, George Washington University
Abstract Preview: Purpose: In current clinical workflow of radiation oncology departments, therapists manually summarize CT simulation orders into summaries before the CT simulation for execution. This process signific...
Authors: Adnan Jafar, Xun Jia, Michael B. Roumeliotis
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Johns Hopkins University
Abstract Preview: Purpose: HDR brachytherapy (HDRBT) treatment planning is challenging due to the need for high-quality plans under time pressure, considering anatomy and applicator geometry. This study proposes an exp...
Authors: Xianjin Dai, PhD, Michael Gensheimer, Praveenbalaji Rajendran, Lei Xing, Yong Yang
Affiliation: Department of Radiation Oncology, Stanford University, Massachusetts General Hospital, Harvard Medical School
Abstract Preview: Purpose: Recent advances in the automatic delineation of radiotherapy treatment targets, which incorporate linguistic clinical data extracted by large language models (LLMs) into traditional visual-on...
Authors: Peng Dong, Elizabeth Kidd, Sheng Liu, Thomas R. Niedermayr, Oscar Pastor-Serrano, Lei Xing, Yong Yang, James Zou
Affiliation: Department of Biomedical Data Science, Stanford University, Department of Radiation Oncology, Stanford University, Stanford University
Abstract Preview: Purpose: Radiotherapy treatment planning is a time-consuming and potentially subjective process that requires iterative adjustments of optimization parameters to balance conflicting objectives. In thi...
Authors: Rex A. Cardan, Carlos E. Cardenas, Udbhav S. Ram
Affiliation: The University of Alabama at Birmingham, University of Alabama at Birmingham
Abstract Preview: Purpose: The AAPM TG-263 report provides nomenclature guidelines for target and normal tissue structures used in radiation oncology. Adherence to these guidelines is challenging for targets, as there ...
Authors: Steve B. Jiang, Chien-Yi Liao, Dan Nguyen, Daniel Yang, Hengrui Zhao
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
Post-operative radiotherapy for prostate cancer requires precise contouring of the clinical target volume (CTV) to account for microscopic disease that is invisible in the image. However, ...
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: 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: Hassan Bagher-Ebadian, Anthony J. Doemer, Ryan Hall, Joshua P. Kim, Bing Luo, Benjamin Movsas, Humza Nusrat, Kundan S Thind
Affiliation: Department of Physics, Toronto Metropolitan University, Henry Ford Health
Abstract Preview: Purpose: This study investigates the development and feasibility of local LLM-based agents to automate radiotherapy treatment planning, aiming to improve planning efficiency and consistency, while pre...
Authors: Yang Sheng, Qingrong Jackie Wu, Qiuwen Wu, Xin Wu, Dongrong Yang
Affiliation: Duke University Medical Center
Abstract Preview: Purpose:
This study aims to leverage large language model (LLMs) to develop a human-in-the-loop agentic framework, enhancing the efficiency of treatment planning in radiotherapy.
Methods:
A L...
Authors: Wei Liu
Affiliation: Department of Radiation Oncology, Mayo Clinic
Abstract Preview: N/A...
Authors: Junwen Liu, Mengzhen Wang, Ning Wen, Jifeng Xiao, Fuhua Yan, Yanzhao Yang, Xuekun Zhang, Zheyu Zhang
Affiliation: Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai Jiaotong University, The SJTU-Ruijin-UIH Institute for Medical Imaging Technology, Shanghai Jiaotong University Schoo of Medicine
Abstract Preview: Purpose:This study aims to develop and evaluate a large language model (LLM) fine-tuned to generate consistent and accurate impressions from imaging findings. Additionally, the study investigates the ...
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: Ramesh Boggula, Jay W. Burmeister, Michael Joiner
Affiliation: Wayne State University, Karmanos Cancer Center, Gershenson ROC, Wayne State University School of Medicine
Abstract Preview: Purpose: Recent advances in large language models such as ChatGPT offer new possibilities for supplementing traditional teaching methods. In this study, we developed a custom GPT-powered tool freely a...
Authors: Ning Wen, Zheyu Zhang
Affiliation: Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Shanghai Jiaotong University
Abstract Preview: Purpose: The graduate course, โPrinciples of Medical Imaging,โ aims to advance imaging technology by integrating artificial intelligence (AI) into medical imaging. It bridges interdisciplinary fields,...
Authors: Douglas John Moseley, David M. Routman, Satomi Shiraishi, Donald C Smith, Mark R. Waddle
Affiliation: University of Denver, Mayo Clinic
Abstract Preview: Purpose: About 6% of patients treated for the first time in our department have received radiotherapy previously at an outside institution. We aim to provide an automatic quality assurance of identify...
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, University of San Francisco
Abstract Preview: Purpose: As Large Language Models (LLMs) continue to evolve, their ability to analyze Electronic Health Record (EHR) notes for clinical decision support expands. Chain of Thought (COT) reasoning, an e...
Authors: Xiangli Cui, Chi Han, Man Hu, Wanli Huo, Xunan Wang, Jianguang Zhang, Yingying Zhang
Affiliation: Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, Departments of Radiation Oncology, Zibo Wanjie Cancer Hospital, Department of Oncology, Xiangya Hospital, Central South University, China Jiliang University, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, China Jiliang University,
Abstract Preview: Purpose:
Medical image generation has broad application prospects in deep learning, but the model training effect is often limited due to the lack of real image data. This study aims to explore the...
Authors: Claus Belka, Stefanie Corradini, Christopher Kurz, Guillaume Landry, Matteo Maspero, Adrian Thummerer, Erik van der Bijl
Affiliation: Department of Radiation Oncology, LMU University Hospital, LMU Munich, Radboud University Medical Center, UMC Utrecht
Abstract Preview: Purpose: To automatically harmonize non-standardized organ-at-risk (OAR) structure names from multi-lingual, multi-institutional radiotherapy datasets using state-of-the-art open-source reasoning larg...
Authors: Ara Alexandrian, Jessica Ashford, Jean-Guy Belliveau, Allison Dalton, Nathan Dobranski, Krystal M. Kirby, Garrett M. Pitcher, David E. Solis, Hamlet Spears, Angela M. Stam, Sotirios Stathakis, Jason Stevens, Rodney J. Sullivan, Sean Xavier Sullivan, Natalie N. Viscariello
Affiliation: Louisiana State University, Mary Bird Perkins Cancer Center, The University of Alabama at Birmingham, University of Alabama at Birmingham
Abstract Preview: Purpose: To improve retrospective risk analysis in radiation oncology by leveraging Large Language Models (LLMs) to extract richly annotated data from unstructured clinical incident reports.
Method...