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Results for "predict pre": 37 found

A Combination of Radiomics and Dosiomics for Gross Tumor Volume Regression in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR)

Authors: Hao Peng, Yajun Yu

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: Personalized ultra-fractionated stereotactic adaptive radiotherapy (PULSAR) is a novel ablative radiation dosing scheme developed by our institution. This study aims to establish a regression...

A Modular Approach to Reversible and Stackable Medical Imaging Translation Models: CBCT-Based Synthetic MRI with Multiple U-Nets in Series (MUNETs)

Authors: Eric Chang, Nguyen Phuong Dang, Andrew Lim, Lauren Lukas, Lijun Ma, Yutaka Natsuaki, Zhengzheng Xu, Hualin Zhang

Affiliation: Radiation Oncology, Keck School of Medicine of USC

Abstract Preview: Purpose: Harnessed the power of AI and Deep Learning (DL), Generalized Neural Network models for medical image transformation are trained to predict target images from reference images, often requirin...

A Two-Layer, Two-Task Prediction Model Based on 3D Imaging and Residual Networks for Mid-Chemoradiation Tumor Response Prediction on FDG PET for La-NSCLC

Authors: Stephen R. Bowen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Jing Sun, Jiajie Wang, Shouyi Wang, Faisal Yaseen, Xiaojing Zhu

Affiliation: Tongji University, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Shanghai University of Electric Power, Fred Hutchinson Cancer Center, University of Texas at Arlington

Abstract Preview: Purpose: Accurate prediction of patient response to radiotherapy plays a crucial role in monitoring disease progression and assessing treatment efficacy, enabling clinicians to develop personalized th...

A Vision-Language Deep Learning Model for Predicting Survival Outcomes in Glioblastoma Patients

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 ...

Automatic Contour Quality Assurance Using Deep-Learning Based Contours

Authors: Laurence Edward Court, Raphael Douglas, David Fuentes, Anuja Jhingran, Barbara Marquez, Raymond Mumme, Christine Peterson, Julianne M. Pollard-Larkin, Surendra Prajapati, Dong Joo Rhee, Thomas J. Whitaker

Affiliation: MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, MD Anderson, Department of Radiation Physics, The University of Texas MD Anderson Cancer Center

Abstract Preview: Purpose: Safe deployment of auto-contouring models requires the inclusion of automated quality assurance (QA). One approach is to use an independent auto-contouring model and compare the contours geom...

Automating Radiographic Sharp Score Prediction in Rheumatoid Arthritis Using Multistage Deep Learning Methods

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...

Big Data in-Vivo Epid Image Prediction for VMAT Radiotherapy

Authors: Casey E. Bojechko, Lance C Moore

Affiliation: University of California, San Diego, University of California San Diego

Abstract Preview: Purpose: EPID images collected during treatment can serve as an in-vivo error detection mechanism. Previous works have shown that comparing in-vivo EPID images to AI-predicted EPID images for IMRT pla...

Biologically Guided Deep Learning for MRI-Based Brain Metastasis Outcome Prediction after Stereotactic Radiosurgery

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...

Clinical Factors Associated with Urinary Morbidity in Men Undergoing Combination Prostate Radiation Therapy

Authors: James Benton, Andrew Dewar, Benjamin C Lee, Ryan Y Lee

Affiliation: Radiotherapy Clinics of Georgia, Urology of Greater Atlanta

Abstract Preview: Purpose:
The purpose of our investigation was to determine if pre-treatment factors could predict urinary morbidity after combination radiation treatment for localized prostate cancer.
Methods:<...

Comparative Analysis of Nine Deep Learning Architectures for Variable Density Grappa 1H Magnetic Resonance Spectroscopy Imaging (MRSI) Reconstruction

Authors: Kimberly Chan, Anke Henning, Mahrshi Jani, Andrew Wright, Xinyu Zhang

Affiliation: Advanced Imaging Research Center (AIRC), UT Southwestern Medical Center

Abstract Preview: Purpose: To evaluate the performance of multiple deep learning architectures for MRSI reconstruction and determine their effectiveness in maintaining high-resolution metabolite mapping while reducing ...

Deeptuning: A Deep Learning Dose Prediction Framework for Interactive Plan Tuning

Authors: Mingli Chen, Huan Amanda Liu, Weiguo Lu, Lin Ma

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, Mayo Clinic

Abstract Preview: Purpose: To reduce the back-and-forth in planning process between physicians and dosimetrists resulting from trade-off exploration, we proposed a novel deep learning framework called DeepTuning.
Me...

Developing an AI-Driven Predictor for Forecasting Treatment Outcomes in Patients with Early-Stage Breast Cancer

Authors: Lucy Jiang, Chengyu Shi

Affiliation: Department of Radiation Oncology, City of Hope Orange County, Amity Regional High School (10th Grade)

Abstract Preview: Purpose: Early-stage breast cancer is common among females, with typically high local tumor control rates (LCR). Brachytherapy is a common way to achieve LCR following surgery. However, the patients m...

Development of a Knowledge-Based Planning Model for Optimal Trade-Off Guidance in Locally Advanced Non-Small Cell Lung Cancer

Authors: Ming Chao, Hao Guo, Tenzin Kunkyab, Yang Lei, Tian Liu, Kenneth Rosenzweig, Robert Samstein, James Tam, Junyi Xia, Jiahan Zhang

Affiliation: Icahn School of Medicine at Mount Sinai

Abstract Preview: Purpose:
The aim of the study is to develop a trade-off prediction model to efficiently guide the treatment planning process for patients with stage III non-small cell lung cancer (NSCLC).
Metho...

Enhanced Prediction of Iroc Stereotactic Radiosurgery Phantom Audit Results with Treatment Parameters, Complexity Metrics, DVH, and Dosiomics Using Machine Learning

Authors: Lian Duan, Stephen F. Kry, Hunter S. Mehrens, Christine Peterson, Paige A. Taylor

Affiliation: The University of Texas MD Anderson Cancer Center, UT MD Anderson Cancer Center

Abstract Preview: Purpose: To develop predictive models for IROC SRS head phantom audits and to identify important factors influencing institutional performance.
Methods: The IROC SRS head phantom includes two TLDs ...

Enhanced Prognostic Modeling for Clear Cell Renal Cell Carcinoma Via Multi-Omics Model and Computational Pathology Foundation Model Integration

Authors: James Brugarolas, Meixu Chen, Raquibul Hannan, Payal Kapur, Jing Wang, Kai Wang

Affiliation: Medical Artificial Intelligence and Automation (MAIA) Lab, Department of Radiation Oncology, UT Southwestern Medical Center, UT Southwestern Medical Center, Department of Radiation Oncology, University of Maryland Medical Center

Abstract Preview: Purpose: Accurate prognosis of clear cell renal cell carcinoma (ccRCC) is essential for guiding personalized treatment planning. In this study, we present a multi-modal ensemble model (MMEM) that inte...

Evaluation of Color Display Performance of Pathology Relevant Colors Using the Macbeth Colorchecker

Authors: Diana Cardona, Casey C. Heirman, William Jeck, Kyle J. Lafata, 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: Display image accuracy is critical for digital diagnostic fields, such as radiology and digital pathology. While the AAPM TG-18 test patterns are established for grayscale radiology monitor Q...

Feasibility of Developing a Radiomic Fingerprint to Predict Pulmonary Embolism Clot Types to Aid in Determining Intervention for Intermediate-Risk Patients.

Authors: Lindsay Hammons, Lisa Baumann Kreuziger, Haidy G. Nasief, Matthew Scheidt, Farrell Sean, Antonio Sosa Lozano

Affiliation: Division of Hematology and Oncology, University of Washington, Vascular and Interventional Radiology, Medical college of wisconsin, Department of Radiation Oncology, Medical College of Wisconsin

Abstract Preview: Purpose: Venous thromboembolism, which includes pulmonary embolism (PE), is the third leading cause of acute cardiovascular syndrome behind myocardial infarction and stroke. Current research categoriz...

Fine-Tuning AI-Based Generative Models for Small-Sample Glioma MRI Generation.

Authors: Xiangli Cui, Chunyan Fu, Man Hu, Wanli Huo, Jingyu Liu, Jianguang Zhang, Yingying Zhang, Shanyang Zhao

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, Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, Department of Oncology, Xiangya Hospital, Central South University, 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

Abstract Preview: Purpose: To quantify the impact of fine-tuning strategies for pre-trained AI image generation models on glioma MRI image quality and observer performance, and to determine the optimal fine-tuning conf...

Improve the Risk Prediction of Radiation-Induced Esophagitis in Lung IMRT By an Anisotropic Dose Convolution Neural Network

Authors: Ibtisam Almajnooni, Elisabeth Weiss, Lulin Yuan

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: We developed a deep learning neural network (DLNN) to predict the risk of radiation-induced esophagitis (RE) during lung cancer radiation therapy based on the spatial dose distribution, for t...

Interpretable Deep Learning Predicts Metastasis-Free Survival (MFS) from Conventional Imaging for Oligometastatic Castration-Sensitive Prostate Cancer (omCSPC) Using Multi-Modality PSMA PET and CT Imaging.

Authors: Yufeng Cao, Luigi Marchionni, William Silva Mendes, Cem Onal, Lei Ren, Amit Sawant, Nicole L Simone, Philip Sutera, Phuoc Tran

Affiliation: University of Maryland School of Medicine, 9Department of Radiation Oncology, Thomas Jefferson University, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins Medicine, University of Maryland, Baltimore, Baskent University Faculty of Medicine, Department of Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine, Maryland University Baltimore, 8Department of Pathology and Laboratory Medicine, Weill Cornell Medicine

Abstract Preview: Purpose: This study aims to predict 2-yr Metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) patients treated by metastasis-directed therapy (MDT) by devel...

MRI Radiomics-Based Machine Learning Model for Predicting BNCT Treatment Response in Glioblastoma

Authors: Huang Chi-Shiuan, Wu Chih-Chun, Hui-Yu Cathy Tsai, Chen Yan-Han, Chen Yi-Wei, Pan Yi-Ying

Affiliation: Institute of Nuclear Engineering and Science, National Tsing Hua University, Taipei Veterans General Hospital, Tri-Service General Hospital

Abstract Preview: Purpose:
This study aims to develop and validate a machine learning (ML) model based on MRI-derived radiomic features to predict progressive disease (PD) in glioblastoma (GBM) patients four months ...

Mitigating Data-Driven Uncertainty in Machine Learning-Based Radiotherapy Outcome Prediction

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...

Muilt-Instance Learning Model with 2D and 3D Features Representation and Transformer-Based Prediction for FDG PET Tumor Chemoradiation Response of La-NSCLC

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...

Multi-Path Deep Learning Model for Predicting Post-Radiotherapy Functional Liver Imaging in Patients with Hepatocellular Carcinoma

Authors: Smith Apisarnthanarax, Stephen R. Bowen, Sunan Cui, Jie Fu, Clemens Grassberger, Yulun He, Yejin Kim, Matthew J. Nyflot, Sharon Pai

Affiliation: Department of Radiation Oncology, University of Washington and Fred Hutchinson Cancer Center, Department of Radiation Oncology, University of Washington, University of Washington, Department of Radiation Oncology, Fred Hutchinson Cancer Center, University of Washington, Department of Physics, University of Washington, University of Washington and Fred Hutchinson Cancer Center

Abstract Preview: Purpose: 99mTc-sulfur colloid SPECT imaging enables quantitative assessment of voxel-wise liver function in patients with hepatocellular carcinoma (HCC). Accurately predicting post-radiotherapy (RT) l...

PET Imaging and Novel Cardiac Radiomics to Predict Pre-Radiotherapy Cardiac Conditions for Lung Cancer Patients Undergoing Radiotherapy.

Authors: Wookjin Choi, Michael Dichmann, Adam Dicker, Nilanjan Haldar, Yingcui Jia, Nicole L Simone, Eugene Storozynsky, Yevgeniy Vinogradskiy, Maria Werner-Wasik

Affiliation: Thomas Jefferson University, 9Department of Radiation Oncology, Thomas Jefferson University

Abstract Preview: Purpose: Cardiotoxicity remains a significant limitation for lung cancer patients treated with radiotherapy. Pre-radiotherapy cardiac conditions increase the probability of patients developing cardiot...

Posterior-Mean Diffusion Model for Realistic PET Image Reconstruction

Authors: Osama R. Mawlawi, Yiran Sun

Affiliation: RICE University, UT MD Anderson Cancer Center

Abstract Preview: Purpose: Conventional PET reconstruction methods often produce noisy images with artifacts due to data/model mismatches and inconsistencies. Recently, deep learning-based conditional denoising diffusi...

Preclinical Assessment of 68ga-Dotatate As a Surrogate for 177lu-Dotatate Dose Distribution in Radionuclide Therapy

Authors: Payman Hejazi, Faraz Kalantari, Milad Peer-Firozjaei, Romina Pourhashem, Ehsan Tajikmansoury

Affiliation: Department of Radiobiology and Medical Physics, Babol University of Medical Sciences, Department of Radiation Oncology, University of Arkansas for Medical Sciences (UAMS), Department of Medical Physics, Semnan University of Medical Sciences, Department of Medical Physics, Semnan University of Medical Sciences, Semnan, Iran, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine

Abstract Preview: Purpose:
Pretreatment biodistribution imaging play a crucial role in optimizing tumors and organ absorbed doses estimation in radionuclide therapy. Surrogating radionuclides for more efficient trea...

Predicting Hormone Receptor Status in Breast Cancer Using Mammographic and Sonographic Data and Machine Learning Models

Authors: Zahra Bagherpour, Manijeh Beigi, Pedram Fadavi, Faraz Kalantari, Moghadaseh Khaleghibizaki, Hengameh Nazari, Mojtaba Safari, Sepideh Soltani

Affiliation: Department of Radiation Oncology, School of Medicine, Iran University of Medical Sciences, Department of Radiation Oncology, School of Medicine, Emory University and Winship Cancer Institute, Department of Radiation Oncology, Iran University of Medical Sciences, University of Arkansas for medical sciences, Department of Radiation physics, The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences

Abstract Preview: Purpose: This study aims to evaluate whether readily available mammographic and sonographic data, combined with machine learning (ML) models, can predict critical molecular factors (ER, PR, HER2) in b...

Predicting Prostate Cancer Recurrence Using an Atlas-Based Tumor Control Probability Model

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...

Prediction of Metastasis-Free Survival in Patients with Prostate Adenocarcinoma Using Primary Tumor and Lymph Node Radiomics from Pre-Treatment PSMA PET/CT Scans.

Authors: Ozan Cem Guler, William Silva Mendes, Sangbo Oh, Cem Onal, Lei Ren, Apurva Singh, Phuoc Tran

Affiliation: University of Maryland School of Medicine, Baskent University Faculty of Medicine, Department of Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine

Abstract Preview: Purpose: To predict metastasis-free survival (MFS) for patients with prostate adenocarcinoma treated with androgen deprivation therapy and external radiotherapy using clinical factors and radiomics ex...

Recovery Factor Comparisons for Reirradiation Overlapping the Spinal Cord

Authors: Xinxin Deng, Issam M. El Naqa, Jimm Grimm, Lijun Ma, Vitali Moiseenko, Timothy E. Schultheiss, Gopal Subedi, Wolfgang A. Tomé, Ellen D. Yorke, Albert van der Kogel

Affiliation: Montefiore Medical Center, Wellstar Kennestone Hospital Cancer Center, H. Lee Moffitt Cancer Center, Department of Human Oncology, University of Wisconsin–Madison, Department of Radiation Oncology, Wellstar Health System, Radiation Oncology, Keck School of Medicine of USC, Georgia Institute of Technology, University of California San Diego, City Of Hope National Medical Centre, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: Reirradiation is increasingly utilized in clinical practice but dose-time recovery factors for human subjects remain uncertain. We constructed a reirradiation recovery model for spinal cord e...

SPECT/CT Multimodal Segmentation of Bone Marrow for Theranostic Dosimetry

Authors: Tommaso Frigerio, Joshua Genender, John M. Hoffman, Catherine (Caffi) Meyer

Affiliation: UCLA, David Geffen School of Medicine at UCLA

Abstract Preview: Purpose: Accurate bone marrow segmentation is required for bone marrow dosimetry to monitor for dangers in PSMA-Lu177 radioligand therapy. We introduce a hybrid (AI/semantic knowledge) segmentation pi...

To Investigate the Utility of Magnetic Resonance Imaging (MRI)-Based Radiomics for Predicting Tumor Response and Adverse Effects, Specifically Gastrointestinal (GI) Toxicity, in Cervical Cancer Patients Undergone Radiotherapy.

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...

Uprightvision: A Deep-Learning Toolkit for Transforming Supine Anatomy to Upright

Authors: Ming Dong, Carri K. Glide-Hurst, Behzad Hejrati, Joshua Pan, Yuhao Yan

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: Upright patient positioners and vertical CT reduce tumor motion and stabilize internal anatomy during treatment delivery. Yet, to fully exploit the advantages of upright, translation of stand...

Using Machine Learning to Predict Esophagitis Risk in Lung Cancer Radiotherapy Based on Clinical and Dosimetric Factors

Authors: Ibtisam Almajnooni, Siyong Kim, Nathaniel Miller, Elisabeth Weiss, Lulin Yuan

Affiliation: Virginia Commonwealth University

Abstract Preview: Purpose: Radiation-induced esophagitis (RE) is a common concern in lung cancer IMRT. Recent studies have indicated that the risk of radiation side effects varies greatly with patients’ baseline clinic...

Utilizing Multiple Modalities to Improve Models to Predict Changes in International Prostate Score for Prostate Cancer

Authors: Matthew C Abramowitz, Alan Dal Pra, Rodrigo Delgadillo, Nesrin Dogan, John C. Ford, Kyle R. Padgett, Levent Sensoy, Benjamin Spieler, Matthew T. Studenski, Jace Allen Walker

Affiliation: University of Miami, Department of Radiation Oncology, University of Miami, University of Miami Sylvester Comprehensive Cancer Center, University of Miami School of Medicine

Abstract Preview: Purpose:
Toxicities that affect a patient’s quality-of-life due to prostate cancer (pCa) radiation therapy (RT) are receiving more attention as RT has become increasingly successful in treating pCA...

“See” through Surface: Transforming Surface Imaging into a Real-Time Three-Dimensional Imaging Solution for Intra-Treatment Image Guidance

Authors: Steve B. Jiang, Ruiqi Li, Hua-Chieh Shao, Kenneth Westover, You Zhang, Tingliang Zhuang

Affiliation: 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, Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose:
Respiratory motion is a long-standing challenge for lung SBRT, particularly for centrally-located lung tumors where increased toxicity demands more precise motion management during treatme...