Background And Purpose: For locally advanced non-small cell lung cancer (LA-NSCLC), intensity-modulated proton therapy (IMPT) can reduce organ at risk (OAR) doses compared to intensity-modulated radiotherapy (IMRT). Deep inspiration breath hold (DIBH) reduces OAR doses compared to free breathing (FB) in IMRT. In IMPT, differences in dose distributions and robustness between DIBH and FB are unclear. In this study, we compare DIBH to FB in IMPT, and IMPT to IMRT.
Materials And Methods: Fortyone LA-NSCLC patients were prospectively included. 4D computed tomography images (4DCTs) and DIBH CTs were acquired for treatment planning and during weeks 1 and 3 of treatment. A new system for automated robust planning was developed and used to generate a FB and a DIBH IMPT plan for each patient. Plans were compared in terms of dose-volume parameters and normal tissue complication probabilities (NTCPs). Dose recalculations on repeat CTs were used to compare inter-fraction plan robustness.
Results: In IMPT, DIBH reduced median lungs D from 9.3 Gy(RBE) to 8.0 Gy(RBE) compared to FB, and radiation pneumonitis NTCP from 10.9 % to 9.4 % ( < 0.001). Inter-fraction plan robustness for DIBH and FB was similar. Median NTCPs for radiation pneumonitis and mortality were around 9 percentage points lower with IMPT than IMRT ( < 0.001). These differences were much larger than between FB and DIBH within each modality.
Conclusion: DIBH IMPT resulted in reduced lung dose and radiation pneumonitis NTCP compared to FB IMPT. Inter-fraction robustness was comparable. OAR doses were far lower in IMPT than IMRT.
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http://dx.doi.org/10.1016/j.phro.2024.100590 | DOI Listing |
AAPS J
January 2025
Laboratory of Pharmaceutical Technology and Biopharmacy, CIRM, University of Liège, 4000, Liège, Belgium.
In addition to the known therapeutic indications for cannabidiol, its administration by inhalation appears to be of great interest. Indeed, there is evidence of cannabidiol's efficacy in several physiological pathways, suggesting its potential for a wide range of applications for both local and systemic pulmonary administration like cancers. Significant advances in pulmonary drug delivery have led to innovative strategies to address the challenges of increasing the respirable fraction of drugs and standardizing inhalable products.
View Article and Find Full Text PDFBrain Res Bull
January 2025
School of Life and Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing 400064, China. Electronic address:
Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resources. Optimizing cognitive load can enhance task performance efficiency by preventing resource waste and overload. Therefore, identifying working memory load is an essential area of research.
View Article and Find Full Text PDFBrief Bioinform
November 2024
School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, No. 800 Dong Chuan Road, Shanghai 200240, China.
Machine learning has emerged as a transformative tool for elucidating cellular heterogeneity in single-cell RNA sequencing. However, a significant challenge lies in the "black box" nature of deep learning models, which obscures the decision-making process and limits interpretability in cell status annotation. In this study, we introduced scGO, a Gene Ontology (GO)-inspired deep learning framework designed to provide interpretable cell status annotation for scRNA-seq data.
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January 2025
Tsinghua University, Beijing, China. Electronic address:
Artificial neural networks (ANNs) can help camera-based remote photoplethysmography (rPPG) in measuring cardiac activity and physiological signals from facial videos, such as pulse wave, heart rate and respiration rate with better accuracy. However, most existing ANN-based methods require substantial computing resources, which poses challenges for effective deployment on mobile devices. Spiking neural networks (SNNs), on the other hand, hold immense potential for energy-efficient deep learning owing to their binary and event-driven architecture.
View Article and Find Full Text PDFSci Rep
January 2025
Faculty of Civil Engineering, Damascus University, Damascus, Syria.
Concrete compressive strength is a critical parameter in construction and structural engineering. Destructive experimental methods that offer a reliable approach to obtaining this property involve time-consuming procedures. Recent advancements in artificial neural networks (ANNs) have shown promise in simplifying this task by estimating it with high accuracy.
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