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Results for "cases classified": 13 found

A Multi-Criteria Optimization Method Based on Reinforcement Learning and Adaptive Boosting in Radiation Therapy

Authors: Liqin HU, Tao He, Jing JIA, Pengcheng LONG, Wei Meng, Yang Yuan

Affiliation: SuperAccuracy Science & Technology Co. Ltd.

Abstract Preview: Purpose: A multi-criteria optimization method based on reinforcement learning and adaptive boosting(RLAB MCO) has been developed to enhance radiotherapy plan quality by offering reasonable and effecti...

A Retrospective Analysis of Usnrc Event Notification Reports

Authors: Yanique N. Dunn, Marissa Joyce Vaccarelli

Affiliation: Northwell, Hofstra University Medical Physics Program

Abstract Preview: Purpose: The U.S. Nuclear Regulatory Commission (USNRC) Event Notification Reports provide detailed information on nuclear-related events, including medical and non-medical incidents. This study evalu...

Adaptive Proton Flash Therapy through Iterative Modular Pin Recycling

Authors: Zachary Diamond, Pretesh Patel, Sibo Tian, Xiaofeng Yang, David Yu, Ahmal Jawad Zafar, Jun Zhou

Affiliation: Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose:
We propose a method to optimize adaptive proton FLASH therapy (ADP-FLASH) using modularized pin-ridge filters (pRFs) by recycling module pins from the initial plan, reducing pRF adjustment...

An Optimal-Mass-Transport-Based Mathematical Model Applied to Brain DCE-MRI to Differentiate Brain Metastases Recurrence from Radiation Necrosis

Authors: Aditya P. Apte, Xinan Chen, Joseph O. Deasy, Ramesh Paudyal, Kyung Peck, Amita Shukla-Dave, Nathaniel Swinburne, Robert J. Young

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

Abstract Preview: Purpose: We apply our novel formulation of unbalanced-regularized-optimal-mass-transport (urOMT) theory to brain DCE-MRI data to quantify and visualize the behaviors of fluid flows in post-treatment f...

Automated Case Prioritization in Breast Radiation Therapy Peer Review Rounds

Authors: Leigh A. Conroy, Thomas G Purdie, Christy Wong

Affiliation: Department of Medical Biophysics, University of Toronto, Princess Margaret Cancer Centre

Abstract Preview: Purpose: To develop a novel machine learning (ML) algorithm to evaluate and rank breast radiation therapy (RT) treatment plans based on treatment complexity for prioritization in multidisciplinary pee...

Automated Framework for Predicting Tumour Growth in Vestibular Schwannomas Using Contrast-Enhanced T1-Weighted MRI

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...

Binary Classification of Lymphedema in 3DCRT Patients Using Machine Learning on 3D Dose Distribution Data

Authors: Jee Suk Chang, Hojin Kim, Jin Sung Kim, Jaehyun Seok

Affiliation: Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Integrative Medicine

Abstract Preview: Purpose: This study aims to leverage 3D dose distribution data to develop a machine learning model capable of accurately predicting lymphedema occurrence in patients undergoing 3D conformal radiation ...

Demographic Attributes of the Train-Test Sets and Their Impact on AI Performance: Medical Imaging Applications

Authors: Maryellen L. Giger, Fahd Hatoum, Robert Tomek, Heather M. Whitney

Affiliation: The University of Chicago

Abstract Preview: Purpose: To assess the importance of applying stratified sampling across demographic attributes (including age, sex, race, and ethnicity) when constructing training and testing datasets for ML-based d...

Early Imaging Identification of Osteoradionecrosis and Classification Using the Novel Clinrad System

Authors: Serageldin Attia, Zayne Belal, Cem Dede, Clifton David Fuller, Andrew Hope, Laia Humbert Vidan, Kate Hutcheson, Zaphanlene Kaffey, Stephen Y. Lai, Abdallah Mohamed, Amy Moreno, Jillian Rigert, Erin Watson

Affiliation: Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center; The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences, Department of Head and Neck Surgery, The University of Texas MD Anderson Cancer Center, Princess Margaret Cancer Centre, University Health Network, 610 University Ave., The University of Texas MD Anderson Cancer Center, UT MD Anderson, Princess Margaret Cancer Centre, UT MD Anderson Cancer Center, Hospital of the University of Pennsylvania, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Department of Head and Neck Surgery, Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, The University of Texas MD Anderson Cancer Center, Department of Radiation Oncology

Abstract Preview: Purpose: Osteoradionecrosis (ORN) of the jaw is a debilitating radiation-induced toxicity lacking standardized classification criteria or treatment guidelines. Early identification of tissue injury co...

Evaluating the Impact of Contour Variability on the Effectiveness of Deep Learning Features in Head and Neck Imaging

Authors: Hania A. Al-Hallaq, Xuxin Chen, Anees H. Dhabaan, Elahheh (Ella) Salari, Xiaofeng Yang

Affiliation: Emory University, Department of Radiation Oncology and Winship Cancer Institute, Emory University

Abstract Preview: Purpose:
Radiomics image analysis could lead to the development of predictive signatures and personalized radiotherapy treatments. However, variations in delineation are known to affect hand-crafte...

LLM-Enhanced Multi-Modal Framework for Predicting Pain Relief of Stereotactic Body Radiotherapy for Spine Metastases Using Clinical Factors and Imaging Reports

Authors: John Byun, Steven D Chang, Mingli Chen, Cynthia Chuang, Xuejun Gu, Melanie Hayden Gephart, Yusuke Hori, Hao Jiang, Mahdieh Kazemimoghadam, Fred Lam, Gordon Li, Lianli Liu, Weiguo Lu, David Park, Erqi Pollom, Elham Rahimy, Deyaaldeen Abu Reesh, Scott Soltys, Gregory Szalkowski, Lei Wang, Qingying Wang, Zi Yang, Xianghua Ye, Kangning Zhang

Affiliation: Department of Radiation Oncology, Stanford University, 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: Accurate prediction of pain relief is crucial in determining the clinical effectiveness of Stereotactic body radiotherapy (SBRT) regimen for spine metastases. We propose a deep-learning frame...

Multi-Omics-Based Prognostic Prediction for Locally Advanced Hypopharyngeal Cancer Treated with Postoperative Chemoradiotherapy: A Dual-Center Study

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...

Multimodal Framework for Predicting Radiation-Induced Severe Acute Esophagitis in Esophageal Cancer

Authors: Yeona Cho, Chloe Min Seo Choi, Joseph O. Deasy, Jue Jiang, Jihun Kim, Jin Sung Kim, Nikhil Mankuzhy, Aneesh Rangnekar, Andreas Rimner, Maria Thor, Harini Veeraraghavan, Abraham Wu

Affiliation: University of Freibrug, Department of Medical Physics, Memorial Sloan Kettering Cancer Center, Department of Radiation Oncology, Yonsei Cancer Center, Heavy Ion Therapy Research Institute, Yonsei University College of Medicine, Department of Radiation Oncology, Gangnam Severance Hospital, Yonsei University College of Medicine, Memorial Sloan Kettering Cancer Center, Yonsei University

Abstract Preview: Purpose: We hypothesized that combining clinical, imaging, and radiotherapy dose-distribution features could increase predictive model accuracy in radiation-induced severe acute esophagitis (SAE) in e...