Intensity Modulated Radiation Therapy (IMRT) is a complex treatment modality that requires pre-treatment patient-specific quality control (QC) in order to assess a correct treatment delivery. The aim of this work is to investigate pre-treatment patient-specific per-field QCs performed with an on-board EPID at the gantry angle of 0° and at the treatment ones, and to asses if measurements executed at 0° are able to guarantee a correct treatment. Ten patients with prostate cancer were evaluated. Two "verification" plans were created for each patient in order to calculate the dose at the EPID surface: one with all fields positioned at 0° and one with all fields at the actual treatment angles. EPID's mechanical shifts due to gravity effects were always taken into account and corrected. 0 and no-0 plans were compared using a gamma-index method (3%, 3 mm). The gamma index was found dependent on gantry angles but the difference between 0 and no-0 samples was small (-0.3% mean value) and the criteria of acceptability of the gamma method was always satisfied for every field delivered at angles different from 0. Therefore patient-specific pre-treatment QCs should be done at treatments angles, but, if periodical quality assurance is performed on dynamic MLC for different gantry angles, this requirement was shown not strictly mandatory and pre-treatment IMRTQC can be reasonably executed at 0° angles too.
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http://dx.doi.org/10.1016/j.ejmp.2012.01.002 | DOI Listing |
Front Oncol
January 2025
Department of Radiation Oncology, Jiangxi Cancer Hospital & Institute, Jiangxi Clinical Research Center for Cancer, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, China.
Objectives: Implementing pre-treatment patient-specific quality assurance (prePSQA) for cancer patients is a necessary but time-consuming task, imposing a significant workload on medical physicists. Currently, the prediction methods used for prePSQA fall under the category of supervised learning, limiting their generalization ability and resulting in poor performance on new data. In the context of this work, the limitation of traditional supervised models was broken by proposing a conditional generation method utilizing unsupervised diffusion model.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA 30308, United States of America.
This study aims to develop a digital twin (DT) framework to achieve adaptive proton prostate stereotactic body radiation therapy (SBRT) with fast treatment plan selection and patient-specific clinical target volume (CTV) setup uncertainty. Prostate SBRT has emerged as a leading option for external beam radiotherapy due to its effectiveness and reduced treatment duration. However, interfractional anatomy variations can impact treatment outcomes.
View Article and Find Full Text PDFPhys Med Biol
January 2025
The Medical Artificial Intelligence and Automation (MAIA) Laboratory, Department of Radiation Oncology, University of Texas Southwestern Medical Center, Dallas, TX 75390, United States of America.
Real-time cone-beam computed tomography (CBCT) provides instantaneous visualization of patient anatomy for image guidance, motion tracking, and online treatment adaptation in radiotherapy. While many real-time imaging and motion tracking methods leveraged patient-specific prior information to alleviate under-sampling challenges and meet the temporal constraint (<500 ms), the prior information can be outdated and introduce biases, thus compromising the imaging and motion tracking accuracy. To address this challenge, we developed a frameworkynamicconstruction andotionstimation (DREME) for real-time CBCT imaging and motion estimation, without relying on patient-specific prior knowledge.
View Article and Find Full Text PDFRadiother Oncol
December 2024
Department of Radiation Oncology, The Netherlands Cancer Institute, Plesmanlaan 121 1066CX Amsterdam, the Netherlands. Electronic address:
Background & Purpose: Deep learning (DL) based auto-segmentation has shown to be beneficial for online adaptive radiotherapy (OART). However, auto-segmentation of clinical target volumes (CTV) is complex, as clinical interpretations are crucial in their definition. The resulting variation between clinicians and institutes hampers the generalizability of DL networks.
View Article and Find Full Text PDFJ Radiat Res
December 2024
Department of Radiology, University of Yamanashi School of Medicine, 1110 Shimokato, Chuo-shi, Yamanashi 409-3898, Japan.
The current research on staffing models is primarily focused on conventional external photon beam therapy, which predominantly involves using linear accelerators. This emphasizes the need for comprehensive studies to understand better and define specific particle therapy facilities' staffing requirements. In a 2022 survey of 25 particle therapy facilities in Japan with an 84% response rate, significant insights were obtained regarding workload distribution, defined as the product of personnel count and task time (person-minutes), for patient-related tasks and equipment quality assurance and quality control (QA/QC).
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