Authors: Christos Ilioudis, Marios Myronakis, Sotirios Raptis, Kyriaki Theodorou
Affiliation: Medical Physics Department, Medical School, University of Thessaly, Department of Information and Electronic Engineering, International Hellenic University (IHU)
Abstract Preview: Purpose: This study presents a radiomics-driven, machine learning framework developed to predict the possibility of Radiation Pneumonitis (RP), as a side effect of radiation therapy in lung cancer pat...
Authors: Zilei Fu, Yi Guo, Wanli Huo, Hongdong Liu, Laishui Lyu, Zhao Peng, Yaping Qi, Senting Wang
Affiliation: Department of Radiotherapy, cancer center, The First Affiliated Hospital of Fujian Medical University, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Division of lonizing Radiation Metrology, National Institute of Metrology, 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, Department of Oncology, Xiangya Hospital, Central South University
Abstract Preview: Purpose: Medical image boundaries are commonly characterized by smooth gray-level transitions, resulting in pixel-level segmentation errors near these blurred boundaries. To address this, we developed...
Authors: Chuangxin Chu, Haotian Huang, Tianhao Li, Jingyu Lu, Zhenyu Yang, Fang-Fang Yin, Tianyu Zeng, Chulong Zhang, Yujia Zheng
Affiliation: The Hong Kong Polytechnic University, Nanyang Technological University, Australian National University, Medical Physics Graduate Program, Duke Kunshan University, North China University of Technology, Duke Kunshan University
Abstract Preview: Purpose: Deep learning segmentation models, such as U-Net, rely on high-quality image-segmentation pairs for accurate predictions. However, the recent increasing use of generative networks for creatin...
Authors: Kyle J. Lafata, Casey Y. Lee, Xiang Li, Megan K. Russ, Zion Sheng
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose:
Traditional deep learning-based cell segmentation models face limitations, such as the need for extensive training data and retraining when encountering new cell types or domains. This stu...
Authors: Majd Antaki, Rohini Bhatia, Gayoung Kim, Yosef Landman, Junghoon Lee, Akila N. Viswanathan
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Physics and Advanced Development Elekta
Abstract Preview: Purpose: Brachytherapy is a standard radiation therapy approach for cervical cancer, which directly delivers radiation source to the tumor using catheters. Treatment planning requires identification o...
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: Alexander Choi, William Ross Green, Christine Hill-Kayser, Gary D. Kao, Michael LaRiviere, Rafe A. McBeth, Steven Philbrook
Affiliation: Department of Radiation Oncology, University of Pennsylvania
Abstract Preview: Purpose: To validate the potential of clinical deployment of an in-house AI-driven auto-segmentation tool for pediatric craniospinal irradiation (CSI) in proton therapy, with goals of reducing manual ...
Authors: Gayoung Kim, Junghoon Lee
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University
Abstract Preview: Purpose: 3D time-of-flight magnetic resonance angiography (TOF-MRA) is widely used for visualizing cerebrovascular structures. Accurate segmentation of cerebrovascular structures is critical for relia...
Authors: Laila A Gharzai, Bharat B Mittal, Poonam Yadav
Affiliation: Northwestern Feinberg School of Medicine, Northwestern Memorial Hospital, Northwestern University Feinberg School of Medicine, Northwestern University Feinberg School of Medicine
Abstract Preview: Purpose: Multiple studies have shown the increasing role of deep learning in segmenting regions of interest. This work presents the feasibility of auto-segmenting the critical structures for head and ...
Authors: Hamdah Alanazi, Silvia Pella
Affiliation: FAU, Florida Atlantic University
Abstract Preview: Purpose: The appearance of breast cancer in the global list of most common cancers worldwide requires
research for ultimate treatment approaches including radiation therapy to reduce deaths from br...
Authors: Sang Hee Ahn, Nalee Kim, Do Hoon Lim
Affiliation: Samsung Medical Center, Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine
Abstract Preview: Purpose: MRI offers superior soft-tissue contrast, aiding tumor localization and segmentation in radiation therapy, which traditionally relies on oncologists' expertise. This study compares CNN-based ...
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: Minbin Chen, Ke Lu, Kaizhong Shi, Chunhao Wang, Chuan Wu, Zhenyu Yang, Fang-Fang Yin, Jingtong Zhao
Affiliation: The First People's Hospital of Kunshan, Duke University, Medical Physics Graduate Program, Duke Kunshan University, Duke Kunshan University, Department of Radiation Oncology, Duke Kunshan University
Abstract Preview: Purpose: MRI-based automatic detection of brain metastases is often challenged by the small size and subtle nature of metastases. This study aimed to develop a novel deep learning-based brain metastas...
Authors: Jadon Buller, Zhuoran Jiang, Yankun Lang, Lei Ren, Leshan Sun, Liangzhong Xiang, Yifei Xu
Affiliation: University of Maryland School of Medicine, University of California, Irvine, University of California, Stanford University
Abstract Preview: Purpose: Electroacoustic tomography (EAT) and Protoacoustic (PA) imaging are novel modalities for treatment verification of electroporation and proton therapy. However, the limited acquisition angle i...
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: Yongha Gi, Jinju Heo, Jinyoung Hong, Yunhui Jo, Yousun KO, HyeongJin Lim, Sang Yoon PARK, Myonggeun Yoon
Affiliation: Korea University, Institute of Global Health Technology (IGHT), Korea University, Republic of Korea
Abstract Preview: Purpose: To evaluate the effectiveness of the gradient magnitude (GM) feature of the entorhinal cortex, observed in T1 MR images, in dementia classification.
Methods: A total of 1,422 ADNI T1 MR da...
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: Yoonha Eo
Affiliation: Yonsei University
Abstract Preview: Purpose: To develop a fully automatic and unsupervised algorithm for estimating the Exposure Index (EI) of various Regions of Interest in X-ray imaging using advanced foundation models. Traditional EI...
Authors: Michael Dohopolski, Jiaqi Liu, Hao Peng, Robert Timmerman, Zabi Wardak, 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
Abstract Preview: Purpose:
This study introduces a gradient-based radiomics framework to enhance outcome prediction in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR) for brain metastases...
Authors: Seungryong Cho, Donghyeok Choi, Joonil Hwang, Byung-Hee Kang, Jin Sung Kim, Eungman Lee, Younghee Park
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, KAIST, Department of Radiation Oncology, Yonsei Cancer Center, Yonsei University College of Medicine, Ewha Womans University of Medicine
Abstract Preview: Purpose: Radiation therapy (RT) is critical for cancer treatment, but changes in tumor size and shape during therapy challenge precise dose delivery. Adaptive radiation therapy (ART) addresses these v...
Authors: Omar Awad, Alfredo Enrique Echeverria, Issam M. El Naqa, Daniel Allan Hamstra, Yiding Han, Ryan Lafratta, Abdallah Sherif Radwan Mohamed, Piyush Pathak, Zaid Ali Siddiqui, Baozhou Sun, Vincent Ugarte
Affiliation: H. Lee Moffitt Cancer Center, Harris Health, Baylor College of Medicine
Abstract Preview: Purpose:
Accurate detection and segmentation of brain metastases are critical for diagnosis, treatment planning, and follow-up imaging but are challenging due to labor-intensive manual assessments ...
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: Xuezhen Feng, Li-Sheng Geng, Haoze Li, Xi Liu, Tianyu Xiong, Ruijie Yang
Affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, School of Physics, Beihang University, School of Nuclear Science and Technology, University of South China, Department of Radiation Oncology, Peking University Third Hospital
Abstract Preview: Purpose: This study aimed to develop a deep learning-based algorithm for automatically delineate gross tumor volume (GTV) for lung cancer patients, alleviating the workload of radiologists and improvi...
Authors: Mark Anastasio, Hua Li, Zhuchen Shao
Affiliation: Washington University School of Medicine, University of Illinois Urbana-Champaign
Abstract Preview: Purpose: Automated semantic segmentation of cell nuclei in microscopic images is vital for disease diagnosis and tissue microenvironment analysis. However, obtaining large annotated datasets for train...
Authors: Hassan Bagher-Ebadian, Ahmed I Ghanem, Joshua P. Kim, Chengyin Li, Rafi Ibn Sultan, Kundan S Thind, Dongxiao Zhu
Affiliation: Wayne State University, Department of Radiation Oncology, Henry Ford Health-Cancer, Detroit, MI and Alexandria Department of Clinical Oncology, Faculty of Medicine, Alexandria University, Henry Ford Health
Abstract Preview: Purpose: Accurate segmentation of the Left Anterior Descending (LAD) artery in free-breathing 3D treatment planning CT is crucial for radiotherapy but remains challenging due to its small size, comple...
Authors: Manju Liu, Ning Wen, Fuhua Yan, Yanzhao Yang, Zhenyu Yang, Haoran Zhang, Lei Zhang, Yajiao Zhang
Affiliation: Department of Radiology, Ruijin Hospital Shanghai Jiaotong University School of Medicine, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University
Abstract Preview: Purpose: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy where precise segmentation of tumors and adjacent vessels is crucial for effective treatment planning. This study dev...
Authors: Yizheng Chen, Md Tauhidul Islam, Mingjie Li, Lei Xing
Affiliation: Department of Radiation Oncology, Stanford University
Abstract Preview: Purpose:
Biomedical image segmentation (BIS) is a cornerstone of medical physics, enabling accurate delineation of anatomical structures and abnormalities, which is critical for diagnosis, treatmen...
Authors: Samuel Kadoury, Redha Touati
Affiliation: Polytechnique Montréal
Abstract Preview: Purpose:
Generating synthetic CT images from MR acquisitions for radiotherapy planning allows to integrate soft tissue contrast alongside density information stemming from CT, thus improving tumor ...