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The purpose of this study was to evaluate the dosimetric uncertainty in 4D dose calculation using three temporal probability distributions: uniform distribution, sinusoidal distribution, and patient-specific distribution derived from the patient respiratory trace. Temporal probability, defined as the fraction of time a patient spends in each respiratory amplitude, was evaluated in nine lung cancer patients. Four-dimensional computed tomography (4D CT), along with deformable image registration, was used to compute 4D dose incorporating the patient's respiratory motion. First, the dose of each of 10 phase CTs was computed using the same planning parameters as those used in 3D treatment planning based on the breath-hold CT. Next, deformable image registration was used to deform the dose of each phase CT to the breath-hold CT using the deformation map between the phase CT and the breath-hold CT. Finally, the 4D dose was computed by summing the deformed phase doses using their corresponding temporal probabilities. In this study, 4D dose calculated from the patient-specific temporal probability distribution was used as the ground truth. The dosimetric evaluation matrix included: 1) 3D gamma analysis, 2) mean tumor dose (MTD), 3) mean lung dose (MLD), and 4) lung V20. For seven out of nine patients, both uniform and sinusoidal temporal probability dose distributions were found to have an average gamma passing rate > 95% for both the lung and PTV regions. Compared with 4D dose calculated using the patient respiratory trace, doses using uniform and sinusoidal distribution showed a percentage difference on average of -0.1% ± 0.6% and -0.2% ± 0.4% in MTD, -0.2% ± 1.9% and -0.2% ± 1.3% in MLD, 0.09% ± 2.8% and -0.07% ± 1.8% in lung V20, -0.1% ± 2.0% and 0.08% ± 1.34% in lung V10, 0.47% ± 1.8% and 0.19% ± 1.3% in lung V5, respectively. We concluded that four-dimensional dose computed using either a uniform or sinusoidal temporal probability distribution can approximate four-dimensional dose computed using the patient-specific respiratory trace.
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http://dx.doi.org/10.1120/jacmp.v16i6.5517 | DOI Listing |
PLoS One
September 2025
School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, Hunan, China.
Knowledge tracing can reveal students' level of knowledge in relation to their learning performance. Recently, plenty of machine learning algorithms have been proposed to exploit to implement knowledge tracing and have achieved promising outcomes. However, most of the previous approaches were unable to cope with long sequence time-series prediction, which is more valuable than short sequence prediction that is extensively utilized in current knowledge-tracing studies.
View Article and Find Full Text PDFPLoS One
September 2025
Biaoxin Science & Technology (Beijing) Co., Ltd, Beijing, China.
This study examines China's national standard development from 2001 to 2023. Using machine splitting and location assignment technology, the Dagum Gini coefficient and its decomposition methods, and traditional and spatial Markov chain estimation methods, we identify the spatiotemporal disparities and dynamic transition characteristics of the contribution levels to national standard development across China's eight comprehensive economic zones. The findings provide a reference for promoting regional coordinated sustainable development and high-quality economic transformation.
View Article and Find Full Text PDFJ Viral Hepat
October 2025
Infectious Disease Epidemiology Group, Weill Cornell Medicine-Qatar, Cornell University, Doha, Qatar.
Hepatitis B virus (HBV) infection is a global health challenge, with the World Health Organization (WHO) targeting its elimination by 2030. Jordan lacks sufficient data on HBV epidemiology, including prevalence, incidence and clearance. This study addresses these gaps through a retrospective analysis of HBV testing data from 40,268 individuals collected at Biolab Diagnostic Laboratories (2010-2024).
View Article and Find Full Text PDFPLoS One
September 2025
College of Intelligent Science and Control Engineering, Jinling Institute of Technology, Nanjing, China.
Traffic congestion frequently occurs in the drop-off zones of large integrated passenger hubs, posing significant challenges to the efficient utilization of lane space. This study develops a First-In-First-Out (FIFO) taxi drop-off decision-making model, incorporating both static and dynamic Logit frameworks grounded in panel data analysis. The model accounts for heterogeneity across vehicles, temporal variations, and spatial factors influencing drop-off decisions.
View Article and Find Full Text PDFPLoS One
September 2025
Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, Fujian, China.
Background: Head and neck cancer (HNC) is a significant global health concern with rising incidence and mortality in certain regions. This study aimed to evaluate the global burden and temporal trends of HNC from 1990 to 2021 and to project its future burden through 2030.
Methods: Data were obtained from the Global Burden of Disease (GBD) 2021 study.