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Results for "support frameworks": 24 found

A Causal Machine Learning Analysis of Dosimetric and Clinical Predictors of Osteoradionecrosis in Head and Neck Cancer Radiotherapy

Authors: Jingyuan Chen, Sheng Li, Tianming Liu, Wei Liu, Zhengliang Liu, Zhong Liu, Daniel Ma, Samir H. Patel, Guangya Wang, Yunze Yang

Affiliation: University of Miami, Mayo Clinic, School of Data Science, University of Virginia, School of Computing, University of Georgia, Department of Radiation Oncology, Mayo Clinic, Institute of Western China Economic Research, Southwestern University of Finance and Economics

Abstract Preview: Purpose:
Traditional patient outcome analyses relied heavily on conventional statistical models that primarily elucidate correlation rather than causal relationships. In this study, we aim to ident...

A Multi-Agent Approach for Fully Automated Nephrometry Feature Extraction in CT

Authors: Matthew S Brown, Joshua Genender, John M. Hoffman, Gabriel Melendez-Corres, Muhammad W. Wahi-Anwar

Affiliation: David Geffen School of Medicine at UCLA, UCLA Department of Radiology

Abstract Preview: Purpose: Renal lesions are evaluated using scoring systems based on visual assessments and manual measurements. The purpose of this work is to develop a multi-agent system for automated anatomic landm...

A Novel Non-Measured and DVH-Based IMRT QA Framework with Machine Learning for Instant Classification of Susceptible Lung SBRT VMAT Plans

Authors: Chuan He, Anh H. Le, Iris Z. Wang

Affiliation: Roswell Park Comprehensive Cancer Center, Cedars-Sinai

Abstract Preview: Purpose: To develop a non-measured and DVH-based (NMDB) IMRT QA framework integrating machine learning (ML) to classify lung SBRT VMAT plans prone to delivery errors
Methods: 560 Eclipse AcurosXB l...

Advanced Modeling of Singlet Oxygen Distribution in Pleural Cavity Photodynamic Therapy Using Validated Geometric Standardization

Authors: Hongjing Sun, Timothy C. Zhu

Affiliation: University of Pennsylvania

Abstract Preview: Purpose: This study aims to develop a model of singlet oxygen distribution in pleural photodynamic therapy (PDT) by combining standardized anatomical coordinates with CT-validated geometry reconstruct...

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...

Breastplan-Helper: Publicly Shared Source Scripting Toolset to Assist in 3D Breast Planning

Authors: Ryan Clark, Anthony Magliari, Luis Felipe Oliveira E Silva, Yang Sheng, Jason R Vickress, Qingrong Jackie Wu, Giulianne Rivelli Rodrigues Zaratim

Affiliation: Verspeeten Family Cancer Center, Office of Medical Affairs, Varian, A Siemens Healthineers Company, Confiar Radioterapia, Duke University Medical Center

Abstract Preview: Purpose: Develop a toolset to replace several of the tedious steps required to manually generate 3D breast treatment plans using static gantry forward planned tangent beams.

Methods: Eclipse Sc...

Compressed Sensing Enhanced Radiomic Feature Selection for 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 new treatment paradigm pioneered by our institution. But the early decision-making process in PULSAR is challe...

Deep Learning-Based Ventricular Auto-Segmentation for Dosimetric Analysis in Intraventricular Tumor SRS

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...

Enhancing Adaptive Radiotherapy Segmentation with a 3D Unet Framework and Prior Fraction Information

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 ...

Establishing Radiotherapy in Malawi through an International Medical Physics Collaboration

Authors: Ruth Afanador, Daniela Branco, John M Bryant, John Campbell, Clement Chaphuka, Samuel A. Einstein, David B. Flint, Jeffrey R. Kemp, Mussa Kumwembe, Daniel J Mollura, Joseph Weygand

Affiliation: RAD-AID International, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology and Applied Science, Dartmouth Health, UNC Health, Malawi National Cancer Center, Kamuzu Central Hospital, Penn State College of Medicine, Sutter Health, New York University, University of California San Diego / California Protons, Department of Radiation Oncology, Moffitt Cancer Center

Abstract Preview: Purpose: Malawi, a landlocked country in southeastern Africa with a population of over 20 million, ranks among the world’s least-developed nations and has the fourth-lowest gross domestic product per ...

From AI Towards Decision Support Frameworks in Radiotherapy: Moving Models in into Clinical Support Tools

Authors: Sanne van Dijk

Affiliation: UMC-Groningen

Abstract Preview: N/A...

G4BraggReflection: Pioneering Wave-Particle Simulations for Convergent Radiotherapy and Beyond

Authors: Dirk A Bartkoski, Michael Kleckner, Dongyeon Lee, Reza Reiazi, Mohammad Reza Salehpour

Affiliation: The University of Texas MD Anderson Cancer Center, Convergent-RnR

Abstract Preview: Purpose:
To develop and validate G4BraggReflection, a novel physics process within Geant4, designed to incorporate diffraction properties resulting in Bragg reflection into Monte Carlo simulations....

Gradient-Based Radiomics for Outcome Prediction and Decision-Making in Personalized Ultra-Fractionated Stereotactic Adaptive Radiotherapy (PULSAR): A Preliminary Study

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...

Image Quality-Based Clinical CT Cohort Selection from Midrc Using a Multi-Institutional Phantom Dataset

Authors: John M. Boone, Andrew M. Hernandez, Paul E. Kinahan, Michael F. McNitt-Gray, Jeffrey H. Siewerdsen, Ali Uneri

Affiliation: University of California, Johns Hopkins Univ, UT MD Anderson Cancer Center, David Geffen School of Medicine at UCLA, University of Washington, UC Davis Health

Abstract Preview: Purpose: Measuring image quality (IQ) in large clinical databases, such as the Medical Imaging and Data Resource Center (MIDRC), is challenging due to the inherent complexity of image content and the ...

Latent Diffusion Model-Driven Semi-Supervised Semantic Segmentation of Cell Nuclei

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...

Monte Carlo Simulation of a Prototype Fluence Field Modulated Cone Beam CT System

Authors: Gregory J Bootsma, Tunok Mondol

Affiliation: University Health Network, Princess Margaret Cancer Center

Abstract Preview: Purpose:
To evaluate the impact of prototype fluence field modulation (FFM) cone-beam CT (CBCT) on dose reduction and image quality using Monte Carlo simulations. This study particularly looks at t...

Multi-Variat, Multi-Model, and Multi-Patient: From Pure Feasibility to Generalizability in Machine Learning Outcome Prediction Model-Based Treatment Plan Optimization

Authors: Martin Frank, Oliver JΓ€kel, Niklas Wahl

Affiliation: Department of Medical Physics in Radiation Oncology, German Cancer Research Center (DKFZ), Karlsruhe Institute of Technology (KIT)

Abstract Preview: Purpose: Machine learning (ML) models on normal tissue complication and tumor control probability ((N)TCP) exploiting e.g. dosiomic and radiomic features are playing an increasingly important role in ...

Pancrea-Seg-Net: A Semi-Supervised Deep Learning Framework for Pancreatic Tumor and Vessel Segmentation

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...

Patient-Specific Treatment Plan Optimization through Intentional Deep Overfit Learning As a Warm Start for Longitudinal Adaptive Radiotherapy

Authors: Wouter Crijns, Frederik Maes, Loes Vandenbroucke, Liesbeth Vandewinckele

Affiliation: Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven; Department of Radiation Oncology, UZ Leuven, Department ESAT/PSI, KU Leuven; Medical Imaging Research Center, UZ Leuven, Department of Oncology, Laboratory of Experimental Radiotherapy, KU Leuven

Abstract Preview: Purpose: To explore intentional deep overfit learning (IDOL) to exploit the initial treatment plan to predict an adaptive radiotherapy plan.
Methods: A conditional generative adversarial network is...

Reliable Markerless Lung Tumor Tracking with Built-in Patient-Specific Quality Assurance

Authors: Weixing Cai, Laura I. Cervino, Qiyong Fan, Yabo Fu, Tianfang Li, Xiang Li, Jean M. Moran, Hai Pham, Pengpeng Zhang

Affiliation: Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Memorial Sloan Kettering Cancer Center

Abstract Preview: Purpose: AAPM Task Group Report 273 emphasizes the importance of rigorous validation to ensure the generalizability and robustness of machine learning-based clinical tools before their implementation ...

Standardizing Hybrid Angio-CT Procedure Terminology: Mapping to Acr Common Lexicon for Future Drl Establishmen

Authors: Manuel M. Arreola, Hugh Davis, Daniella Fabri, Emily L. Marshall, BC Schwarz

Affiliation: University of Florida

Abstract Preview: Purpose:
This study aims to map procedure names, descriptions, and CPT codes from a Hybrid Angio-CT room to the American College of Radiology (ACR) Common Lexicon. This mapping will support expedit...

The Topas Monte Carlo Framework – Status and Outlook after 15 Years of Development

Authors: Alejandro Bertolet, Jorge Naoki Dominguez Kondo, Bruce A. Faddegon, Thongchai Masilela, Isaac Meyer, Victor V. Onecha, Harald Paganetti, Jose A. Ramos-Mendez, Jan PO Schuemann, Wook-Geun Shin

Affiliation: University of California San Francisco, Massachusetts General Hospital and Harvard Medical School, Massachusetts General Hospital

Abstract Preview: Purpose: To present the results of 15 years of developments of the TOPAS TOol for PArticle Simulation framework, and to highlight recent and new developments.

Methods: Fundamental understanding...

Two-Stage Clustering and Auto Machine Learning to Predict Chemoradiation Response in Tumor Subregions on FDG PET for La-NSCLC

Authors: Stephen R. Bowen, Shijun Chen, Chunyan Duan, Daniel S. Hippe, Qiantuo Liu, Qianqian Tong, Jiajie Wang, Shouyi Wang, Faisal Yaseen

Affiliation: The University of Texas at Austin, 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: Tumor subregion clustering and prediction of region-specific response can augment assessments and adaptive treatment decisions. A modeling framework was constructed to predict chemoradiation ...

Unlocking Adaptive Radiotherapy Flexibility: Integrating Ethos Adaptive Therapy and Halcyon IGRT with Scripting Innovations

Authors: Min Geon Choi, Sean J. Domal, Ruiqi Li, Taoran Li, Mu-Han Lin, Yang Kyun Park, David D.M. Parsons, Justin D. Visak

Affiliation: Department of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, UT Southwestern Medical Center, University of Texas Southwestern Medical Center, University of Pennsylvania, Department of Radiation Oncology, UT Southwestern Medical Center

Abstract Preview: Purpose: Ethos X-ray-guided online adaptive radiotherapy (ART) enables precise, daily adaptive treatments but requires significant resources, limiting widespread adoption. Many treatment sites do not ...