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Results for "bd2 bd50": 7 found

Application of the Lymphodose Framework to Brain Tumors: Unveiling the Prognostic Power of Circulating Lymphocyte Doses

Authors: Sophie Bockel, Eric Deutsch, Frederic Dhermain, Ibrahima Diallo, Anh Thu Le, Elaine Limkin, Pauline Maury, Charlotte Robert, Killian Sambourg, Camilla Satragno, Cristina Veres, François de Kermenguy

Affiliation: Gustave Roussy, Département de radiothérapie, Université Paris-Saclay, Gustave Roussy, Inserm U1030, Radiothérapie Moléculaire et Innovation Thérapeutique

Abstract Preview: Purpose:
To study the correlation between the dose to circulating lymphocytes as evaluated by the LymphoDose framework and the incidence of severe radiation-induced lymphopenia (sRIL) in patients t...

Evaluation of Thoracic Direct Dose Calculation Using Truebeam Linac with Hypersight Imaging CBCT Solution

Authors: Theodore Higgins Arsenault, Kenneth W. Gregg, Lauren E Henke, Rojano Kashani, Alex T. Price, Sagar Regmi, Atefeh Rezaei, Runyon C. Woods

Affiliation: University Hospitals Seidman Cancer Center

Abstract Preview: Purpose: To investigate the feasibility and accuracy of using a Hounsfield Unit(HU) calibrated cone-beam computed tomography(CBCT) for direct dose calculation in thoracic treatment settings. In combin...

Impact of Arc Number Variation on VMAT Lattice Radiotherapy Plans

Authors: Minbin Chen, Gang Liu, Manju Liu, Weiwei Sang, Pulin Sun, Mingyuan Ye, Fang-Fang Yin, Lihua Zhang, Haiming Zhu

Affiliation: Jiahui International Hospital, Radiation Oncology, Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Medical Physics Graduate Program, Duke Kunshan University, Duke Kunshan University, The First People's Hospital of Kunshan

Abstract Preview: Purpose: This study aims to evaluate the effects of varying the number of arcs on treatment plans created using Volumetric Modulated Arc Therapy (VMAT) for Lattice radiotherapy (LRT).
Methods: Thre...

Implementing a Knowledge-Based Planning Model for Gastrointestinal (GI) Site-Specific Plans for Photon Radiation Therapy

Authors: Andreea Dimofte, Maksym Sharma, Weibing Yang, Timothy C. Zhu

Affiliation: Department of Radiation Oncology, University of Pennsylvania, University of Pennsylvania

Abstract Preview: Purpose:
To assess the effectiveness and dosimetric impact of utilizing a knowledge-based planning model for GI site-specific plans.
Methods:
Six knowledge-based planning models were develope...

Innovative Biological Adaptive Radiotherapy (BART) Approach for Head-and-Neck Cancer Treatment Interruptions

Authors: Nobuki Imano, Daisuke Kawahara, Akito S Koganezawa, Yuji Murakami, Ikuno Nishibuchi, Takuya Wada

Affiliation: Department of Information and Electronic Engineering, Faculty of Science and Engineering, Teikyo University, Department of Radiation Oncology, Graduate School of Biomedical and Health Sciences, Hiroshima University

Abstract Preview: Purpose: Adaptive radiotherapy (ART) compensates for treatment plans based on anatomical changes while not considering biological effects such as interruptions during treatment. This study aims to dev...

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...

VMAT Machine Parameter Optimization Using Policy Gradient Reinforcement Learning

Authors: Avinash Mudireddy, Nathan Shaffer, Joel J. St-Aubin

Affiliation: University of Iowa

Abstract Preview: Purpose: This work demonstrates preliminary results in training a reinforcement learning (RL) network to perform VMAT machine parameter optimization.
Methods: We implemented a policy gradient RL al...