Authors: Todd A Aguilera, Gaurav Khatri, Jiaqi Liu, Hao Peng, Nina N. Sanford, Robert Timmerman, Haozhao Zhang
Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, 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:
This study first integrates 3D topological data analysis with radiomics from local advanced rectal cancer T2-weighted MRI to evaluate therapeutic responses and quantify treatment-induced c...
Authors: Si-Wa Chan, Yuan-Yu Lee, Zhi-Ying Li, Jia-Wei Liao, Hui-Yu Cathy Tsai
Affiliation: Department of Radiology, Taichung Veterans General Hospital, Institute of Nuclear Engineering and Science, National Tsing Hua University
Abstract Preview: Purpose: Dense breast tissue reduces the sensitivity of mammography, posing diagnostic challenges, especially for Asian women with high breast density (up to 50%). Current single-modality techniques o...
Authors: Weiguo Lu, 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
Abstract Preview: Purpose:
Neural network-based lesion segmentation remains a significant challenge due to the low contrast between lesions and surrounding tissues (high ambiguity) and the variability of lesion shap...
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: Nobuki Imano, Yuzuha Kadooka, Daisuke Kawahara, Misato Kishi, Yuji Murakami, Shumpei Onishi
Affiliation: Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Graduate School of Biomedical and Health Sciences, Hiroshima University, Department of Neurosurgery, Hiroshima University Hospital
Abstract Preview: Purpose: Radiomics has proven useful in predicting overall survival in glioblastoma (GBM) patients, but consistent molecular correlations remain unidentified, leaving its biological basis unclear. Thi...
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: 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: Jacqueline M. Andreozzi, Tess Armstrong, Shiva Bhandari, John M Bryant, Jessica M Frakes, David J. Gladstone, Sarah E Hoffe, Kujtim Latifi, Arash Naghavi, Steven Nichols, Ibrahim M. Oraiqat, Russell Palm, Gage H. Redler, Stephen A Rosenberg, Joseph Weygand
Affiliation: Department of Radiation Oncology and Applied Science, Dartmouth Health, Thayer School of Engineering, Dartmouth College, Department of Radiation Oncology, Ohio State University, Moffitt Cancer Center, Department of Radiation Oncology, Moffitt Cancer Center
Abstract Preview: Purpose: The MR-linac provides unique opportunities for integrating advanced imaging into radiotherapy workflows, but the lower sensitivity of systems like the 0.35T model can pose challenges for diff...
Authors: Mustafa Bashir, Diana Kadi, Kyle J. Lafata, Jacob A. Macdonald, Mark Martin, Yuqi Wang, Marilyn Yamamoto
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Department of Electrical and Computer Engineering, Duke University, Department of Radiology, Duke Unversity
Abstract Preview: Purpose: To develop a high-throughput, automated-data-interrogation pipeline for integrating imaging and clinical information to identify key determinants of liver volume (LV), enabling population-sca...
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: Zachary Buchwald, Chih-Wei Chang, Zach Eidex, Hui Mao, 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, Emory University School of Medicine
Abstract Preview: Purpose: MRI-guided radiation therapy (MRgRT) benefits significantly from enhanced soft-tissue contrast and spatial resolution, which aid in accurately delineating tumors and organs at risk. Although ...
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: 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: Evan Calabrese, Scott R. Floyd, Kyle J. Lafata, Zachary J. Reitman, Eugene Vaios, Chunhao Wang, Lana Wang, Deshan Yang, Zhenyu Yang, Jingtong Zhao
Affiliation: Duke University, Department of Radiation Oncology, Duke University, Duke Kunshan University
Abstract Preview: Purpose:
This study proposes a novel neural ordinary differential equation (NODE) framework to distinguish post-SRS radionecrosis from recurrence in brain metastases (BMs). By integrating imaging f...
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: George Agrotis, Marios Myronakis, Dimitrios Samaras, Kyriaki Theodorou, Ioannis Tsougos, Vassilios Tzortzis, Maria Vakalopoulou, Alexandros Vamvakas, Aikaterini Vassiou, Marianna Vlychou
Affiliation: Medical Physics Department, Medical School, University of Thessaly, Department of Radiology, University of Thessaly, Netherland Cancer Institute, Department of Urology, University of Thessaly, CentraleSupelec, University Paris-Saclay
Abstract Preview: Purpose: Prostate cancer (PCa) diagnosis remains challenging due to discrepancies in Gleason Scoring (GS) and risks of overdiagnosis and underdiagnosis. Multiparametric MRI (mpMRI), including Apparent...
Authors: Mark Anastasio, Hua Li, Zhuchen Shao
Affiliation: Washington University School of Medicine, University of Illinois Urbana-Champaign
Abstract Preview: Purpose: Ill-conditioned reconstruction problems in medical imaging, such as those arising from undersampled k-space data in MRI, can result in degraded image quality and clinical task-orientated perf...
Authors: Clint Bahler, Ruchika Reddy Chimmula, Harrison Louis Love, Oluwaseyi Oderinde, Courtney Yong
Affiliation: Purdue University, Department of Urology, Indiana University School of Medicine, Advanced Molecular Imaging in Radiotherapy (AdMIRe) Research Laboratory, School of Health Sciences, Purdue University
Abstract Preview: Purpose: Prostate cancer (PCa) is a common malignancy in men, and predicting biochemical recurrence (BCR) is crucial for guiding treatment decisions. Integrating multimodal data, including clinical, i...
Authors: Yan Dai, Jie Deng, Christopher Kabat, Weiguo Lu, Ying Zhang, Hengrui Zhao
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, Medical Artificial Intelligence and Automation (MAIA) Lab & Department of Radiation Oncology, UT Southwestern Medical Center
Abstract Preview: Purpose:
MRI-guided adaptive radiotherapy (MRgART) using MR-LINAC systems offers significant advantages for liver cancer, enabling superior tumor delineation and online plan adaptation. However, ma...
Authors: Madhava Aryal, James M. Balter, Yue Cao, Daniel T Chang, Kyle Cuneo, Joseph R. Evans, Theodore Lawrence, John Rice, Randall K. Ten Haken, Lise Wei
Affiliation: University of Michigan, Department of Radiation Oncology University of Michigan
Abstract Preview: Purpose: This study aims to identify predictors of global liver function change measured by albumin-bilirubin (ALBI) score following stereotactic body radiation therapy (SBRT) in hepatocellular carcin...
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: Sixue Dong, Chaosu Hu, Weigang Hu, Xiaomin Ou, Jiazhou Wang, Zhen Zhang
Affiliation: Fudan University Shanghai Cancer Center
Abstract Preview: Purpose:
This study aimed to predict the PFS of the patients who were diagnosed with hypopharyngeal cancer and received postoperative chemoradiotherapy by using multi-omics which integrating clinic...
Authors: Eric Aliotta, Michalis Aristophanous, Joseph O. Deasy, Bill Diplas, Milan Grkovski, James Han, Vaios Hatzoglou, Jeho Jeong, Nancy Y Lee, Ramesh Paudyal, Nadeem Riaz, Heiko Schoder, Amita Shukla-Dave
Affiliation: Department of Radiology, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Memorial Sloan Kettering Cancer Center, Department of Medical Physics, Memorial Sloan Kettering Cancer Center
Abstract Preview: Purpose: To forecast radiotherapy treatment response for head and neck cancer (HNC) using multimodality imaging and personalized radiobiological modeling.
Methods: Multi-modality imaging data from ...
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, “Principles of Medical Imaging,” aims to advance imaging technology by integrating artificial intelligence (AI) into medical imaging. It bridges interdisciplinary fields,...
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: Issam M. El Naqa, Kurukulasuriya Ruwani Fernando, Himani Himani, Vivek Kumar, Arun Oinam, Manju Sharma
Affiliation: Panjab University, Moffitt Cancer Center, H. Lee Moffitt Cancer Center, Post Graduate Institute of Medical Sciences, University of California San Francisco
Abstract Preview: Purpose: To investigate the utility of Magnetic Resonance Imaging (MRI)-based radiomics for predicting tumor response and adverse effects, specifically gastrointestinal (GI) toxicity, in cervical canc...
Authors: William Amestoy, Carolina Benjamin, Markus Bredel, Rodrigo Delgadillo, Nesrin Dogan, Michael E Ivan, Ricardo J Komotar, Gregory J. Kubicek, Eric Mellon, Ivaylo B. Mihaylov, Maria Irene Monterroso, Raymond A. Schulz, Ashish Shah, Robert M Starke
Affiliation: University of Miami, Department of Radiation Oncology, University of Miami, University of Miami Sylvester Comprehensive Cancer Center, Varian Medical Systems
Abstract Preview: Purpose: Stereotactic Radiosurgery (SRS) is a widely used treatment modality in radiation oncology, utilizing various technologies such as Gamma Knife (GK), Cyber Knife (CK) and Linac-based SRS with H...