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Results for "hybrid machine": 8 found

A Hybrid Radiomics-Integrated Machine Learning Framework for Early Identification of Potential Radiation Pneumonitis in Lung Cancer Patients

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...

Automated Diagnosis of Pancreatic Cancer Using Both Radiomics and 3D-Convolutional Neural Network

Authors: Beth Bradshaw Ghavidel, Benyamin Khajetash, Yang Lei, Meysam Tavakoli

Affiliation: Icahn School of Medicine at Mount Sinai, Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Emory University, Department of Radiation Oncology, Emory University

Abstract Preview: Purpose: Pancreatic cancer is among the most aggressive types of cancer, with a five-year survival rate of approximately 10%. Recent studies show that radiomics and deep learning (DL)-based methods ar...

Development of Hybrid Shielding Collimator to Reduce Radiation Exposure in Fluoroscopy

Authors: Yona Choi, Kyo-tai Kim, Seungwoo Park, Dung Thi Tran

Affiliation: Gemss Health Care Inc,, Korea Institute of Radiological & Medical Sciences

Abstract Preview: Purpose: We developed shielding materials and a hybrid concept collimator for fluoroscopy to reduce the dose to normal tissues outside the treatment field, which are capable of quantifying dose rate a...

Nausea, Heartburn, K-Edge Imaging: Pepto Bismol As a CT Contrast Agent

Authors: Magdalena Bazalova-Carter, Ross I. Berbeco, James Day, Xinchen Deng, Chelsea Amanda Saffron Dunning, Dianne M. Ferguson, Matthew W. Jacobson, Toby Morris, Marios Myronakis, Jericho Daniel O'Connell, Fides Schwartz, Jainil Shah, Aaron Sodickson

Affiliation: Brigham and Womens Hospital, University of Massachsetts Lowell and Dana-Farber Cancer Institute Boston, Medical Physics Department, Medical School, University of Thessaly, Brigham and Women's Hospital, Harvard Medial School, Dana-Farber Cancer Institute, Department of Radiation Oncology, University of Washington, University of Victoria, Siemens Healthineers, Dana Farber/Brigham and Women's Cancer Center, Brigham and Women's Hospital

Abstract Preview: Purpose: While CT imaging has advanced with improved machine design, we propose further gains can be achieved by enhancing sensitivity to contrast agents. Current CT sensitivity is limited to 1% bismu...

Predicting CBCT-Based Adaptive Radiation Therapy Session Duration Using Machine Learning

Authors: Leslie Harrell, Sanjay Maraboyina, Romy Megahed, Maida Ranjbar, Xenia Ray, Pouya Sabouri

Affiliation: Department of Radiation Oncology, University of Arkansas for Medical Sciences (UAMS), University of California San Diego

Abstract Preview: Purpose: Real-time adaptive radiation therapy (ART) dynamically modifies patients’ treatment plan during delivery to account for anatomical and physiological variations. Addressing ART planning time v...

Predicting Hematologic Toxicity in Advanced Cervical Cancer Patients Using Interpretable Machine Learning Models Based on Radiomics and Dosimetrics

Authors: Qianxi Ni, Qionghui Zhou

Affiliation: The Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University/Hunan Cancer Hospital, the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University

Abstract Preview: Purpose:
This study aims to develop and evaluate interpretable machine learning models that use radiomic and dosimetric features to predict HT in advanced cervical cancer patients.
Methods:
R...

Transformer-Based Proton Dose Prediction with and without Diffusion Process

Authors: Jing Qian, Brandon Reber, David M. Routman, Satomi Shiraishi

Affiliation: Mayo Clinic

Abstract Preview: Purpose: The dose distribution in proton radiotherapy (PRT) is characterized by sharp gradients, posing a challenge for machine learning-based dose prediction. While denoising with diffusion processes...

Using Single-Energy Bragg Peak (SEBP) Flash Combined with Intensity-Modulated Proton Therapy (IMPT) for Flash Treatment in a Clinical Synchrotron-Based Proton System

Authors: Chingyun Cheng, Ben Durkee, Carri K. Glide-Hurst, Minglei Kang, Haibo Lin, Bhudatt R. Paliwal, Charles B. Simone, Zhizhen Wei, Tengda Zhang, Xingyi Zhao

Affiliation: University of Wisconsin, Department of Mechanical Engineering, University of Wisconsin-Madison, Departments of Human Oncology and Medical Physics, University of Wisconsin-Madison, New York Proton Center, Department of Human Oncology, University of Wisconsin School of Medicine and Public Health, Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Department of Human Oncology, University of Wisconsin-Madison

Abstract Preview: Purpose: Transmission Beam (TB), Single-Energy Bragg Peak (SEBP), and Single-Energy Spread-Out Bragg Peak (SESOBP) are primary proton conformal FLASH techniques. However, each comes with significant l...