Accelerated partial breast irradiation (APBI) is an alternative treatment for early-stage breast cancer. This study aimed to evaluate the effectiveness of the virtual bolus (VB) method and robust planning against respiratory motion in volumetric modulated arc therapy (VMAT)-APBI. VMAT plans were generated with 30 Gy in 5 fractions for 16 patients. Four treatment plans were developed and compared: a standard optimization (SO) plan without robust methods, a pseudo-skin flash strategy using a 5 mm VB (with densities of 0.4 and 1.0 g/cm3, VB04 and VB10), and a robust optimization (RO) plan to minimize penalties in worst-case scenarios. The isocenter was shifted 1-5 mm in each translational direction in robust analysis, and perturbed dose calculations were performed. All dose constraints for the target in SO and VB plans were within acceptable limits, but the dose evaluation volume V95% in the RO plan was lower than in other plans (P < 0.05). The clinical target volume V95% of the RO plan was significantly higher than in VB04 and VB10 (P < 0.05). The RO plan showed the best performance for organs at risk, followed by SO and VB plans, which resulted in higher doses. The RO plan exhibited the smallest change (±2%) in dose distribution due to respiratory motion. By contrast, the SO plan lacked robustness owing to absence of sufficient fluence in the air surrounding the planning target volume outside of the skin surface. The RO plan offers superior target coverage, maximum dose, and robustness compared to SO and VB methods.
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Transl Behav Med
January 2025
Section of Infectious Diseases, Department of Medicine, Boston University Chobanian and Avedesian School of Medicine/Boston Medical Center, 801 Massachusetts Avenue, Boston, MA 02118, USA.
Background: The Supplemental Nutrition Assistance Program (SNAP) is an underutilized program. SNAP uptake is limited in Latine households in particular due to concerns about immigration eligibility, even when there are SNAP-eligible household members. Implementation strategies are urgently needed to increase SNAP participation rates among those who are eligible.
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February 2025
Department of Biomedical Sciences, Institute of Health, Jimma University, Jimma, Ethiopia.
The COVID-19 pandemic underscored bacterial resistance as a critical global health issue, exacerbated by the increased use of antibiotics during the crisis. Notwithstanding the pandemic's prevalence, initiatives to address bacterial medication resistance have been inadequate. Although an overall drop in worldwide antibiotic consumption, total usage remains substantial, requiring rigorous regulatory measures and preventive activities to mitigate the emergence of resistance.
View Article and Find Full Text PDFSci Rep
March 2025
Qingdao University of Science and Technology, Qingdao, 266061, China.
Intelligent Transportation Systems (ITSs) have become pivotal in urban traffic management by utilizing traffic flow prediction, which aids in alleviating congestion and facilitating route planning. This study introduces the Linear Attention Based Spatial-Temporal Multi-Graph Convolutional Neural Network (LASTGCN), a novel deep learning model tailored for traffic flow prediction. LASTGCN incorporates a Multifactor Fusion Unit (MFF-unit) to dynamically integrate meteorological factors, an advanced multi-graph convolutional network for spatial correlations, and the Receptance Weighted Key Value (RWKV) block, which employs a linear attention mechanism for efficient processing of historical traffic data.
View Article and Find Full Text PDFCrit Rev Oncol Hematol
March 2025
Division of Gastroenterology, Fondazione IRCCS - Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy. Electronic address:
Colorectal cancer (CRC) is the third most common and second most deadly cancer worldwide. Despite advances in screening and treatment, CRC is heterogeneous and the response to therapy varies significantly, limiting personalized treatment options. Certain molecular biomarkers, including microsatellite instability (MSI), are critical in planning personalized treatment, although only a subset of patients may benefit.
View Article and Find Full Text PDFPhys Med Biol
March 2025
UT Southwestern Medical Center, UT Southwestern Medical Center, 5323 Harry Hines Blvd, Dallas, Dallas, Texas, 75390, UNITED STATES.
Magnetic Resonance Imaging (MRI) is a non-invasive imaging technique that provides high soft tissue contrast, playing a vital role in disease diagnosis and treatment planning. However, due to limitations in imaging hardware, scan time, and patient compliance, the resolution of MRI images is often insufficient. Super-resolution (SR) techniques can enhance MRI resolution, reveal more detailed anatomical information, and improve the identification of complex structures, while also reducing scan time and patient discomfort.
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