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Monte Carlo simulations in radiotherapy dosimetry

Overview of attention for article published in Radiation Oncology, June 2018
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282 Mendeley
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Title
Monte Carlo simulations in radiotherapy dosimetry
Published in
Radiation Oncology, June 2018
DOI 10.1186/s13014-018-1065-3
Pubmed ID
Authors

Pedro Andreo

Abstract

The use of the Monte Carlo (MC) method in radiotherapy dosimetry has increased almost exponentially in the last decades. Its widespread use in the field has converted this computer simulation technique in a common tool for reference and treatment planning dosimetry calculations. This work reviews the different MC calculations made on dosimetric quantities, like stopping-power ratios and perturbation correction factors required for reference ionization chamber dosimetry, as well as the fully realistic MC simulations currently available on clinical accelerators, detectors and patient treatment planning. Issues are raised that include the necessity for consistency in the data throughout the entire dosimetry chain in reference dosimetry, and how Bragg-Gray theory breaks down for small photon fields. Both aspects are less critical for MC treatment planning applications, but there are important constraints like tissue characterization and its patient-to-patient variability, which together with the conversion between dose-to-water and dose-to-tissue, are analysed in detail. Although these constraints are common to all methods and algorithms used in different types of treatment planning systems, they make uncertainties involved in MC treatment planning to still remain "uncertain".

X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 282 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 282 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 33 12%
Student > Master 33 12%
Researcher 31 11%
Student > Bachelor 19 7%
Other 13 5%
Other 35 12%
Unknown 118 42%
Readers by discipline Count As %
Physics and Astronomy 86 30%
Medicine and Dentistry 28 10%
Engineering 15 5%
Nursing and Health Professions 9 3%
Computer Science 4 1%
Other 12 4%
Unknown 128 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 June 2018.
All research outputs
#14,355,501
of 23,092,602 outputs
Outputs from Radiation Oncology
#808
of 2,079 outputs
Outputs of similar age
#185,459
of 329,163 outputs
Outputs of similar age from Radiation Oncology
#16
of 33 outputs
Altmetric has tracked 23,092,602 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,079 research outputs from this source. They receive a mean Attention Score of 2.7. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 329,163 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 43rd percentile – i.e., 43% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.