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: 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: Weiguo Lu, Jax Luo, Xiaoxue Qian, Hua-Chieh Shao, Guoping Xu, 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, Harvard Medical School
Abstract Preview: Purpose:
Semi-supervised segmentation leverages sparse annotation information to learn rich representations from combined labeled and label-less data for segmentation tasks. This study leverages th...
Authors: Deshan Yang, Zhendong Zhang
Affiliation: Duke University, Department of Radiation Oncology, Duke University
Abstract Preview: Purpose:
The evaluation of deformable image registration (DIR) algorithms is crucial for improving accuracy and clinical adoption. However, reliable benchmarks, especially for inter-modality regist...
Authors: Mojtaba Behzadipour, Siyong Kim, Mitchell Polizzi, Richard R. Wargo, Lulin Yuan
Affiliation: VCU Health - Department of Radiology, Virginia Commonwealth University
Abstract Preview: Purpose:
The purpose of this study is to develop a method for generating missing tissue in CT scans of patients with large body sizes, where the field of view (FOV) of the scanner fails to capture ...
Authors: Adayabalam Balajee, Elijah Berberette, Maria Escalona, Dray Gentry, Chester R. Ramsey, Terri Ryan
Affiliation: ORAU, Thompson Proton Center, University of Tennessee
Abstract Preview: Purpose:
Dicentric chromosomes, characterized by two centromeres on a single chromosome, are key biomarkers in biological dosimetry for quantifying ionizing radiation exposure. However, manual dete...
Authors: Shae Gans, Carri K. Glide-Hurst, Mark Pankuch, Chase Ruff, Niek Schreuder, Nicholas R. Summerfield, Yuhao Yan
Affiliation: Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Northwestern Medicine Proton Center, Northwestern Medicine Chicago Proton Center, Leo Cancer Care
Abstract Preview: Purpose: Novel upright patient positioners coupled with diagnostic-quality vertical CT at treatment isocenter introduce a significant opportunity for improved image-guided particle therapy. Treating p...
Authors: Cem Altunbas, Farhang Bayat, Roy Bliley, Rupesh Dotel, Brian Kavanagh, Uttam Pyakurel, Tyler Robin, Ryan Sabounchi
Affiliation: Department of Radiation Oncology, University of Colorado School of Medicine, Taussig Cancer Center, Cleveland Clinic, University of Colorado Anschutz Medical Campus
Abstract Preview: Purpose: Automatic segmentation of anatomical structures in CBCT images is key to enabling dose delivery monitoring and online plan modifications in radiotherapy. However, poor image quality can degra...
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: Zachery Colbert, Matthew Foote, Michael Huo, Mark Pinkham, Prabhakar Ramachandran, Mihir Shanker
Affiliation: Radiation Oncology, Princess Alexandra Hospital, Ipswich Road, Princess Alexandra Hospital
Abstract Preview: Purpose: The study aimed to develop and implement deep learning-based autosegmentation models for the autosegmentation of four key tumor types: brain metastasis, pituitary adenoma, vestibular schwanno...
Authors: Jingyun Chen, Yading Yuan
Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology
Abstract Preview: Purpose: To develop and evaluate the Scale-attention network (SANet) for automated pancreas segmentation on MR images.
Methods: To develop SANet, we extended the classic U-Net design with a dynamic...
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: Mehdi Amini, Minerva Becker, Simina Chiriac, Alexandre Cusin, Dimitrios Daskalou, Ghasem Hajianfar, Sophie Neveu, Marcella Pucci, Yazdan Salimi, Pascal Senn, Habib Zaidi
Affiliation: Geneva University Hospital, Division of Radiology, Diagnostic Department, Geneva University Hospitals, Service of Otorhinolaryngology-Head and Neck Surgery, Department of Clinical Neurosciences, Geneva University Hospitals
Abstract Preview: Purpose: Personalized prediction of vestibular schwannoma (VS) tumour growth is crucial for guiding patient management decisions toward observation versus intervention. This study proposes an automate...
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: Katja M. Langen, William Andrew LePain, Robert Muiruri, Vivi Nguyen, Mosa Pasha, Roelf L. Slopsema, Alexander Stanforth, Yinan Wang, Mingyao Zhu
Affiliation: Emory Healthcare, Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University
Abstract Preview: Purpose: Intensity modulated proton therapy (IMPT) treatment planning for craniospinal irradiation (CSI) is complex and requires extensive effort from the planner. This study aims to enhance planning ...
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: Hajar Moradmand, Lei Ren
Affiliation: University of Maryland School of Medicine, University of Maryland
Abstract Preview: Purpose:
The Sharp-van der Heijde (SvH) score is essential for assessing joint damage in rheumatoid arthritis (RA) from radiographic images. However, manual scoring is time-intensive and prone to v...
Authors: Sahaja Acharya, Matthew Ladra, Junghoon Lee, Lina Mekki
Affiliation: Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Department of Biomedical Engineering, Johns Hopkins University
Abstract Preview: Purpose: Multi-parametric MRI (mpMRI) is widely used for deep learning (DL)-based automatic segmentation of brain tumors. While multi-contrast images concatenated as channels are typically input to ne...
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: Haijian Chen, Katja M. Langen, William Andrew LePain, Claire Tran, Mingyao Zhu
Affiliation: Emory Healthcare, Emory University, Georgia Institute of Technology
Abstract Preview: Purpose: To validate the performance of a commercial deep-learning segmentation (DLS) tool for head and neck cancer (HNC) and thoracic and abdominal cancer (TAC) by comparing it to manual segmentation...
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: Jingyun Chen, Yading Yuan
Affiliation: Columbia University Irving Medical Center, Department of Radiation Oncology
Abstract Preview: Purpose: To evaluate centralized and decentralized strategies for federated head and neck tumor segmentation on PET/CT.
Methods: We utilized training data from the HEad and neCK TumOR segmentation ...
Authors: Kota Hirose, Daisuke Kawahara, Jokichi Kawazoe, Yuji Murakami
Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University
Abstract Preview: Purpose: Synthesizing medical images can address the lack of or unscanned medical images, reducing scanner time and costs. However, paired image scarcity remains a challenge for image synthesis. We pr...
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: 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: Fumiaki Komatsu, Shunsuke Moriya, Ryosuke Nakamura, Takeji Sakae, Toshiyuki Terunuma, Tetsuya Tomita
Affiliation: Graduate School of Comprehensive Human Sciences, University of Tsukuba, Institute of Medicine, University of Tsukuba, Proton Medical Research Center, University of Tsukuba, Department of Radiology, University of Tsukuba Hospital
Abstract Preview: Purpose: To develop a deep learning (DL) model capable of accurately tracking lung tumors independent of beam angle variations.
Methods: A thoracic dynamic phantom simulating lung motion in the sup...
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: John Byun, Juan J Cardona, Steven D Chang, Cynthia Fu-Yu Chuang, Xuejun Gu, Yusuke Hori, Hao Jiang, Fred Lam, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang
Affiliation: Department of Radiation Oncology, Stanford University, Department of Neurosurgery, Stanford School of Medicine, 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:
Intraventricular tumors pose significant challenges in neurosurgery due to their complex location. Therefore, brain SRS could be a better treatment option. At our institution, some patient...
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: John Byun, Steven D Chang, Cynthia Fu-Yu Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Xianghua Ye, Kangning Zhang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Neurosurgery, Stanford University, Department of Radiation Oncology, Stanford University, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: Accurate and automated delineation of vestibular schwannoma (VS) volume is crucial for disease management, as both treatment approaches (stereotactic radiosurgery and invasive surgery) and mo...
Authors: Penghao Gao, Zejun Jiang, Huazhong Shu, Linlin Wang, Gongsen Zhang, Jian Zhu
Affiliation: Laboratory of Image Science and Technology, Key Laboratory of Computer Network and Information Integration, Southeast University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Artificial Intelligence Laboratory, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences
Abstract Preview: Purpose: We propose a cascading framework for time-varying anatomical volumetric reconstruction and tumor-tracking, guided by onboard orthogonal-view X-ray projections.
Methods: We employe multiple...
Authors: Gregory T. Armstrong, James E. Bates, Christine V. Chung, Lei Dong, Ralph Ermoian, Jie Fu, Christine Hill-Kayser, Rebecca M. Howell, Meena S. Khan, Sharareh Koufigar, John T. Lucas, Thomas E. Merchant, Taylor Meyers, Tucker J. Netherton, Constance A. Owens, Arnold C. Paulino, 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, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology and Winship Cancer Institute, Emory University, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences
Abstract Preview: Purpose: Clinical workflows often rely on auto-segmentation tools trained on adult data, which may exhibit suboptimal performance in pediatric imaging due to inherent anatomical variations and smaller...
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: 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: Mengqi Shen, Meghna Trivedi, Tony J.C. Wang, Andy (Yuanguang) Xu, Yading Yuan
Affiliation: Columbia University Medical Center, Dept of Med Hematology & Oncology, Data Science Institute at Columbia University, Columbia University Irving Medical Center, Department of Radiation Oncology, Columbia University Irving Medical Center
Abstract Preview: Purpose: T2-weighted (T2w) images are critical for identifying pathological changes due to their superior contrast in differentiating tissue types. However, they often lack detailed anatomical resolut...
Authors: Diana Cardona, Casey C. Heirman, William Jeck, Kyle J. Lafata, Xiang Li, Lauren M. Neldner, Jeffrey S. Nelson, Megan K. Russ, Ehsan Samei
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Department of Pathology, Duke University, Clinical Imaging Physics Group, Department of Radiology, Duke University Health System
Abstract Preview: Purpose: Medical physicists have traditionally supported radiation-based medicine, but their expertise can translate to other image-based fields including pathology. As pathology transitions to digita...
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: 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: Ali Ammar, Quan Chen, Yi Rong, Libing Zhu
Affiliation: Mayo Clinic Arizona
Abstract Preview: Purpose: To investigate how defacing algorithms, essential for patient privacy in data sharing, impact AI-based segmentation performance in CT imaging for radiation therapy. This study evaluates wheth...
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: Claus Belka, Stefanie Corradini, George Dedes, Nikolaos Delopoulos, Christopher Kurz, Guillaume Landry, Ahmad Neishabouri, Domagoj Radonic, Adrian Thummerer, Niklas Wahl, Fan Xiao
Affiliation: Department of Radiation Oncology, LMU University Hospital, LMU Munich, Department of Medical Physics, LMU Munich, Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), National Center for Radiation Oncology (NCRO), Heidelberg Institute for Radiation Oncology (HIRO)
Abstract Preview: Purpose: In MR-guided online adaptive radiotherapy, MRI lacks tissue attenuation information necessary for accurate dose calculations. Instead of using deep learning methods to generate synthetic CT i...
Authors: Mingli Chen, Xuejun Gu, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang
Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Department of Radiation Oncology, Stanford University School of Medicine
Abstract Preview: Purpose: This study introduces a novel template-guided deep learning framework for primary gross tumor volume (GTVp) segmentation, addressing challenges posed by diverse tumor types and enabling a uni...
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: Chloe Min Seo Choi, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu
Affiliation: Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: Cardiac substructure irradiation predisposes patients for poor outcomes in thoracic radiation therapy. A deep learning model was developed to segment the cardiac substructures invariant to co...
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: 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: 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: Zong Fan, Fan Lam, Hua Li, Rita Huan-Ting Peng, Yuan Yang
Affiliation: University of Illinois at Urbana Champaign, University of Illinois at Urbana-Champaign, Washington University School of Medicine, University of Illinois Urbana-Champaign
Abstract Preview: Purpose: Accurate lesion segmentation in MRI is critical for early diagnosis, treatment planning, and monitoring disease progression in various neurological disorders. Cross-site MRI data can alleviat...
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: 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: Wookjin Choi, James M. Lamb, David Romanofski, David H. Thomas, Yevgeniy Vinogradskiy
Affiliation: Drexel, Department of Radiation Oncology, University of California, Los Angeles, Thomas Jefferson University
Abstract Preview: Purpose: To develop an intelligent Black Box Recorder for radiation therapy (RT) that monitors patient treatments using a vision language model.
Methods: The system captures synchronized screen rec...
Authors: Xiangli Cui, Zilei Fu, Man Hu, Wanli Huo, Xiaoqing Wu, Jianguang Zhang, Yingying Zhang
Affiliation: Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, the Zhejiang-New Zealand Joint Vision-Based Intelligent Metrology Laboratory, College of Information Engineering, China Jiliang University, Departments of Radiation Oncology, Zibo Wanjie Cancer Hospital, Department of Oncology, Xiangya Hospital, Central South University, Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences
Abstract Preview: Purpose:
Using Stable Diffusion to generate images of the knee in different disease states can enrich the medical imaging database and inject new vitality into the field of medical imaging analysis...
Authors: Jin Sung Kim, Chanwoong Lee, Young Hun Yoon
Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine
Abstract Preview: Purpose: Chest contrast-enhanced CT (CECT) serves as a valuable tool for cardiac imaging, but its lack of detailed anatomical visualization limits its utility in segmentation tasks. While CECT offers ...
Authors: Andrew R. Godley, Steve B. Jiang, Mu-Han Lin, Austen Matthew Maniscalco, Dan Nguyen, Yang Kyun Park
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, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
Preparing DICOM datasets for research and education is challenging due to the complexity of the format and the necessity for patient-specific handling. Existing workflows demand substantia...
Authors: Ali Ajdari, Alice Bondi, Thomas R. Bortfeld, Gregory Buti, Xinru Chen, Zhongxing Liao, Antony John Lomax, Ting Xu
Affiliation: The University of Texas MD Anderson Cancer Center, Department Of Radiation Oncology, Massachusetts General Hospital (MGH), Massachusetts General Hospital & Harvard Medical School, Paul Scherrer Institut, ETH Zurich
Abstract Preview: Title: Addressing Imaging and Biomarker-driven Uncertainty in Machine Learning-based Radiotherapy Outcome Prediction
Alice Bondi, Gregory Buti, Antony Lomax, Thomas Bortfeld, Xinru Chen, Ting Xu, Z...
Authors: Ming Dong, Carri K. Glide-Hurst, Qisheng He, Anudeep Kumar, Alex Singleton Kuo, Joshua Pan, Chase Ruff, Nicholas R. Summerfield
Affiliation: Department of Computer Science, Wayne State University, Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Department of Human Oncology, University of Wisconsin-Madison
Abstract Preview: Purpose: Recent evidence highlights the importance of incorporating cardiac substructures (CS) into treatment planning for thoracic cancers, however current segmentation methods are limited to a singl...
Authors: Tyler J Bradshaw, Sharon M Castellino, Steve Y Cho, David Hodgson, Bradford S Hoppe, Kara M Kelly, Andrea Lo, Sarah Milgrom, Xin Tie
Affiliation: Department of Radiation Oncology, University of Toronto, Department of Radiology, University of Wisconsin, University of Colorado Anschutz, Department of Medical Physics, University of Wisconsin, Department of Radiation Oncology, Mayo Clinic, Department of Radiation Oncology, BC Cancer, Vancouver Center, Department of Radiology, University of Wisconsin - Madison, Roswell Park Comprehensive Cancer Center, Emory University School of Medicine
Abstract Preview: Purpose: Clinical target volume (CTV) delineation for involved-site radiation therapy (ISRT) in Hodgkin lymphoma (HL) is time-consuming due to the need to analyze multi-time-point PET/CT scans co-regi...
Authors: Mingli Chen, Xuejun Gu, Hao Jiang, Mahdieh Kazemimoghadam, Weiguo Lu, Qingying Wang, Kangning Zhang
Affiliation: 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:
Deep learning-based automatic medical image segmentation is increasingly employed in clinical practice, significantly reducing manual workload. However, verifying segmentation results rema...
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: Blessing Akinro, Soumyanil Banerjee, Ming Dong, Carri K. Glide-Hurst, Prashant Nagpal, Chase Ruff, Nicholas R. Summerfield, Timothy P. Szczykutowicz
Affiliation: Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Departments of Radiology and Medical Physics, University Wisconsin-Madison, Department of Radiology, University of Wisconsin-Madison, Department of Computer Science, Wayne State University, Department of Human Oncology
Abstract Preview: Purpose: Radiation dose to coronary arteries (CAs) during thoracic radiotherapy (RT) is linked to cardiotoxicity. However, precise CA delineation for avoidance is limited by image quality and CA compl...
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...
Authors: Yasaman Anbari, Srinivas Cheenu Kappadath, Benjamin P. Lopez, Armeen Mahvash, Ali Yousefi
Affiliation: University of Houston, UT MD Anderson Cancer Center
Abstract Preview: Purpose: Patient-demographics-model-based liver volumetry is well-established for determining the future liver remnant following hepatectomy. We used gold-standard CT liver segmentation to validate th...
Authors: Brian M. Anderson, Shiva K. Das, Meagan Foster, Anirudh Karunaker, Lawrence B. Marks, Lukasz Mazur, Michael Repka
Affiliation: UNC Chapel HIll, University of North Carolina at Chapel Hill, UNC School of Medicine, University of North Carolina
Abstract Preview: Purpose: Development of a peer review segmentation check system to identify deviations in physician contours of standard risk pelvic lymph nodes in patients receiving radiation therapy for prostate an...
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: 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: Lando S. Bosma, Victoria Brennan, Nicolas Cote, ChengCheng Gui, Nima Hassan Rezaeian, Jue Jiang, Sudharsan Madhavan, Josiah Simeth, Neelam Tyagi, Harini Veeraraghavan, Michael J Zelefsky
Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, NYU Langone Health, University Medical Center Utrecht, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: Deep learning-based deformable image registration (DIR) models often lack robustness when applied to datasets with differing imaging characteristics. We aimed to (1) improve registration netw...
Authors: Ming Dong, Carri K. Glide-Hurst, Joshua Pan, Nicholas R. Summerfield
Affiliation: Department of Computer Science, Wayne State University, Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, Department of Human Oncology, University of Wisconsin-Madison
Abstract Preview: Purpose: Radiation dose to the cardiac nodes is more strongly associated with conduction disorders and arrythmias than whole heart (WH) metrics. However, node segmentation is challenging due to comple...
Authors: Ke Lu, Chunhao Wang, Ruoxu Xia, Zhenyu Yang, Fang-Fang Yin, Chulong Zhang, Lei Zhang, Rihui Zhang, Jingtong Zhao, Haiming Zhu
Affiliation: Duke University, Duke Kunshan University, Medical Physics Graduate Program, Duke Kunshan University, The First People's Hospital of Kunshan
Abstract Preview: Purpose: The human brain’s spherical geometry offers unique opportunities for improving the segmentation of tiny and irregular anatomical structures. We hypothesize that representing the brain in sphe...
Authors: Gong Vincent Hao, Daisuke Kawahara, Jokichi Kawazoe, Yuji Murakami, Ikuno Nishibuchi, Peiying Colleen Ruan, Daguang Xu, Dong Yang
Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University, NVIDIA
Abstract Preview: Purpose:
Accurate tumor segmentation in head and neck cancer is critical for effective treatment planning, but variability in practices across medical facilities poses challenges for standardizatio...
Authors: Petr Bruza, Yao Chen, David J. Gladstone, Lesley A Jarvis, Brian W Pogue, Kimberley S Samkoe, Yucheng Tang, Shiru Wang, Rongxiao Zhang
Affiliation: NVIDIA Corp, Dartmouth College, Thayer School of Engineering, Dartmouth College, Dartmouth Cancer Center, University of Missouri, University of Wisconsin - Madison
Abstract Preview: Purpose: Cherenkov imaging provides real-time visualization of megavoltage radiation beam delivery during radiotherapy. Patient-specific bio-morphological features, such as vasculature, captured in th...
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...