Search Submissions πŸ”Ž

Results for "reduce workload": 14 found

A Feasible, Extendable, and Low-Cost Web-Based Application to Minimize the Second-Check Workload

Authors: William N. Duggar, Li Yuan

Affiliation: University of Mississippi Medical Center

Abstract Preview: Purpose:
Radiation Oncology departments typically utilize various systems from different vendors. Ensuring the integrity and correctness of data during transfers between these systems is essential ...

A Method to Reduce Workload in Adaptive Radiotherapy

Authors: Ramesh Boggula, Lincoln Houghton

Affiliation: Karmanos Cancer Institute, Wayne State University

Abstract Preview: Purpose: To evaluate an approach that selectively applies adaptive re-planning only when needed to reduce clinical workload while maintaining treatment quality. Daily adaptive radiotherapy (ART) has t...

An Explainable Classifier for Enhancing the Quality Assurance of Digital Breast Tomosynthesis Phantom Images

Authors: Hui-Shan Jian, Yu-Ying Lin

Affiliation: Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou

Abstract Preview: Purpose: The image quality assurance of mammographic images is crucial for correct diagnosis. To develop and validate an explainable deep-learning classifier for phantom image quality assessment of di...

Automated Plan Optimization Using an Adaptive Objective Function Template in Prostate Cancer

Authors: Michael Joseph Dance, Shiva K. Das, John Dooley, David V. Fried, Spencer Lynch, Michael Repka, Shivani Sud, Neil Ari Wijetunga

Affiliation: University of North Carolina

Abstract Preview: Purpose: The growing complexity of radiation therapy treatment planning presents challenges in maintaining efficient clinical workflows while ensuring plan quality. This study evaluates the use of an ...

Commissioning of an AI-Assisted Tool for Enhancing Post-Radiosurgery Follow-up in Multiple Brain Metastases Patients

Authors: Rex Carden, Carlos E. Cardenas, Ho-hsin Rita Chang, John B Fiveash, Heinzman A. Katherine, Yogesh Kumar, Gaurav Nitin Rathi, Richard A. Popple, Kayla Lewis Steed

Affiliation: University of Alabama at Birmingham

Abstract Preview: Purpose: Brain metastases (BMs) often require multiple radiotherapy (RT) courses as new lesions appear. Comparing follow-up imaging with prior RT plans is time-intensive. We developed an AI tool that ...

Deep-Learning Convolutional Neural Network-Based Breast Cancer Localization for Mammographic Images: A Study on Simulated and Clinical Images

Authors: Xiaoyu Duan, Xiang Li, Wenbo Wan, Lei Zhang, Yiwen Zhang

Affiliation: Duke University, Medical Physics Graduate Program, Duke Kunshan University

Abstract Preview: Purpose: Breast screening has been proved to reduce breast cancer mortality by early detection and treatment for patients. Mammography is the most common and widely used technique for breast cancer sc...

Improving Operational Efficiency in Head and Neck Proton Therapy through Adaptation Analysis

Authors: Laura Buchanan, Samantha G. Hedrick, Stephen L. Mahan, Isabella Pfeiffer, Chester R. Ramsey

Affiliation: Thompson Proton Center

Abstract Preview: Purpose: Proton therapy plans can be highly sensitive to both anatomical changes and positional errors, making the acquisition of Quality Assurance CT (QACT) images a routine clinical practice during ...

In Vivo Dosimetry for Cied Management – Is It Really Needed?

Authors: Anthony J. Doemer, Aharon Feldman, Stephen J. Gardner, Brett M. Miller, Benjamin Movsas, Farzan Siddiqui, Chadd Smith, Kundan S Thind, Kyle Verdecchia

Affiliation: Henry Ford Health

Abstract Preview: Purpose: To evaluate the efficacy of calculation-only approach for CIED risk-level assessment.
Methods: A total of 86 patients were included in this retrospective analysis. For each patient, in viv...

Incorporating Physicians’ Contouring Style into Auto-Segmentation of Clinical Target Volume for Post-Operative Prostate Cancer Radiotherapy Using a Language Encoder

Authors: Steve B. Jiang, Chien-Yi Liao, Dan Nguyen, Daniel Yang, Hengrui Zhao

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

Abstract Preview: Purpose:
Post-operative radiotherapy for prostate cancer requires precise contouring of the clinical target volume (CTV) to account for microscopic disease that is invisible in the image. However, ...

Integrating Large Kernel Attention Mechanism into Deep Learning Model for Automatic and Auccrate Segmentation of Gross Tumor Volume in Lung Cancer Patients

Authors: Xuezhen Feng, Li-Sheng Geng, Haoze Li, Xi Liu, Tianyu Xiong, Ruijie Yang

Affiliation: Department of Health Technology and Informatics, The Hong Kong Polytechnic University, School of Physics, Beihang University, School of Nuclear Science and Technology, University of South China, Department of Radiation Oncology, Peking University Third Hospital

Abstract Preview: Purpose: This study aimed to develop a deep learning-based algorithm for automatically delineate gross tumor volume (GTV) for lung cancer patients, alleviating the workload of radiologists and improvi...

Pilot Clinical Implementation of Auto-Planning Using Multicriteria Optimization for Pelvis Radiotherapy

Authors: Shifeng Chen, Erica Fisler, Arun Gopal, Mariana Guerrero, Jason Hendershot, Kai Huang, Eric Kusmaul, Adam Schrum, Megan Steinberg, Kai Wang

Affiliation: University of Maryland School of Medicine, Department Radiation Oncology, Kaufman Cancer Center, Upper Chesapeake Medical Center, University of Maryland Medical System Central Maryland Radiation Oncology, Department of Radiation Oncology, University of Maryland Medical Center, Baltimore Washington Medical Center Radiation Oncology, Department of Radiation Oncology, University of Maryland School of Medicine

Abstract Preview: Purpose:
Our goal is to develop and clinically deploy an automated planning tool using multicriteria optimization (MCO) for VMAT in prostate only, whole pelvis radiotherapy treatments.
Methods:<...

Real-Time Fully Automated IMRT Planning without Optimization Process Using a Two-Step AI Framework

Authors: Daisuke Kawahara, Takaaki Matsuura, Yuji Murakami, Ryunosuke Yanagida

Affiliation: Hiroshima High-Precision Radiotherapy Cancer Center, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima

Abstract Preview: Purpose: In recent years, automation in radiation therapy planning using AI has gained significant attention to reduce the workload of treatment planners. Adaptive Radiation Therapy (ART), as a new fo...

The Feasibility of Ethos-Based Pulsed Reduced Dose-Rate (PRDR) Radiotherapy

Authors: Chia-Lung Chien, Renteng Hou, Wen C. Hsi, Faraz Kalantari, Ganesh Narayanasamy, Zhong Su

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

Abstract Preview: Purpose:
PRDR is an effective re-irradiation treatment for patients who have already reached the tolerance dose to their organs at risk (OARs). While PRDR is implemented on TrueBeam by limiting the...

Towards AI Decision-Support for Online Adaptive Radiotherapy (oART): A Preliminary Study on CBCT-Guided Post-Prostatectomy Oart

Authors: Michael Cummings, Olga M. Dona Lemus, Hana Mekdash, Tyler Moran, Alexander R Podgorsak, Sean M. Tanny, Matthew J. Webster, Lexiang Yang, Dandan Zheng, Yuwei Zhou, Xiaofeng Zhu

Affiliation: Department of Radiation Oncology, University of Rochester, University of Miami, Inova Schar Cancer Institute, University of Rochester

Abstract Preview: Purpose: oART is revolutionizing radiotherapy by allowing treatment plans to be adjusted based on daily imaging, improving targeting precision and potentially enhancing patient outcomes. However, its ...