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Monte Carlo in the mechanistic modelling of the FLASH effect: a review.
Phys Med Biol
January 2025
School of Physics, Mathematics & Computing, University of Western Australia, Crawley, Western Australia, Australia.
FLASH radiotherapy employs ultra-high dose rates of>40Gy s, which may reduce normal tissue complication as compared to conventional dose rate treatments, while still ensuring the same level of tumour control. The potential benefit this can offer to patients has been the cause of great interest within the radiation oncology community, but this has not translated to a direct understanding of the FLASH effect. The oxygen depletion and inter-track interaction hypotheses are currently the leading explanations as to the mechanisms behind FLASH, but these are still not well understood, with many questions remaining about the exact underpinnings of FLASH and the treatment parameters required to optimally induce it.
View Article and Find Full Text PDFCurrent views on mechanisms of the FLASH effect in cancer radiotherapy.
Natl Sci Rev
October 2024
State Key Laboratory of Nuclear Physics and Technology, School of Physics, Peking University, Beijing 100871, China.
FLASH radiotherapy (FLASH-RT) is a new modality of radiotherapy that delivers doses with ultra-high dose rates. The FLASH effect was defined as the ability of FLASH-RT to suppress tumor growth while sparing normal tissues. Although the FLASH effect has been proven to be valid in various models by different modalities of irradiation and clinical trials of FLASH-RT have achieved promising initial success, the exact underlying mechanism is still unclear.
View Article and Find Full Text PDFCharacterization of the response of radiochromic film to quasi-monoenergetic x rays through a cross-calibration with image plates.
Rev Sci Instrum
September 2024
Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA.
Radiochromic film (RCF) and image plates (IPs) are both commonly used detectors in diagnostics fielded at inertial confinement fusion (ICF) and high-energy-density physics (HEDP) research facilities. Due to the intense x-ray background in all ICF/HEDP experiments, accurately calibrating the optical density of RCF as a function of x-ray dose, and the photostimulated luminescence per photon of IPs as a function of x-ray energy, is necessary for interpreting experimental results. Various measurements of the sensitivity curve of different IPs to x rays have been performed [Izumi et al.
View Article and Find Full Text PDFDSA Quantitative Analysis and Predictive Modeling of Obliteration in Cerebral AVM following Stereotactic Radiosurgery.
AJNR Am J Neuroradiol
October 2024
From the Department of Radiology (M.S.J., M.A.M., H.K., W.B.), Mayo Clinic, Rochester, Minnesota.
Background And Purpose: Stereotactic radiosurgery is a key treatment modality for cerebral AVMs, particularly for small lesions and those located in eloquent brain regions. Predicting obliteration remains challenging due to evolving treatment paradigms and complex AVM presentations. With digital subtraction angiography (DSA) being the gold standard for outcome evaluation, radiomic approaches offer potential for more objective and detailed analysis.
View Article and Find Full Text PDFA competing risks machine learning study of neutron dose, fractionation, age, and sex effects on mortality in 21,000 mice.
Sci Rep
August 2024
Center for Radiological Research, Columbia University Irving Medical Center, 630 West 168th street, VC-11, New York, NY, 10032, USA.
This study explores the impact of densely-ionizing radiation on non-cancer and cancer diseases, focusing on dose, fractionation, age, and sex effects. Using historical mortality data from approximately 21,000 mice exposed to fission neutrons, we employed random survival forest (RSF), a powerful machine learning algorithm accommodating nonlinear dependencies and interactions, treating cancer and non-cancer outcomes as competing risks. Unlike traditional parametric models, RSF avoids strict assumptions and captures complex data relationships through decision tree ensembles.
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