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Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer

Overview of attention for article published in Radiation Oncology, October 2014
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (65th percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

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4 tweeters

Citations

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93 Dimensions

Readers on

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64 Mendeley
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Title
Assessment of a model based optimization engine for volumetric modulated arc therapy for patients with advanced hepatocellular cancer
Published in
Radiation Oncology, October 2014
DOI 10.1186/s13014-014-0236-0
Pubmed ID
Authors

Antonella Fogliata, Po-Ming Wang, Francesca Belosi, Alessandro Clivio, Giorgia Nicolini, Eugenio Vanetti, Luca Cozzi

Abstract

BackgroundTo evaluate in-silico the performance of a model-based optimization process for volumetric modulated arc therapy (RapidArc) applied to hepatocellular cancer treatments.Patients and methods45 clinically accepted RA plans were selected to train a knowledge-based engine for the prediction of individualized dose-volume constraints. The model was validated on the same plans used for training (closed-loop) and on a set of other 25 plans not used for the training (open-loop). Dose prescription, target size, localization in the liver and arc configuration were highly variable in both sets to appraise the power of generalization of the engine. Quantitative dose volume histogram analysis was performed as well as a pass-fail analysis against a set of 8 clinical dose-volume objectives to appraise the quality of the new plans.ResultsQualitative and quantitative equivalence was observed between the clinical and the test plans. The use of model-based optimization lead to a net improvement in the pass-rate of the clinical objectives compared to the plans originally optimized with standard methods (this pass-rate is the frequency of cases where the objectives are respected vs. the cases where constraints are not fulfilled). The increase in the pass-rate resulted of 2.0%, 0.9% and 0.5% in a closed-loop and two different open-loop validation experiments.ConclusionsA knowledge-based engine for the optimization of RapidArc plans was tested and lead to clinically acceptable plans in the case of hepatocellular cancer radiotherapy. More studies are needed before a broad clinical use.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 64 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 63 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 15 23%
Student > Master 12 19%
Other 7 11%
Student > Bachelor 5 8%
Professor > Associate Professor 5 8%
Other 13 20%
Unknown 7 11%
Readers by discipline Count As %
Physics and Astronomy 20 31%
Medicine and Dentistry 12 19%
Engineering 7 11%
Nursing and Health Professions 4 6%
Pharmacology, Toxicology and Pharmaceutical Science 2 3%
Other 8 13%
Unknown 11 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 October 2014.
All research outputs
#3,269,569
of 7,918,051 outputs
Outputs from Radiation Oncology
#251
of 1,066 outputs
Outputs of similar age
#66,678
of 201,363 outputs
Outputs of similar age from Radiation Oncology
#18
of 84 outputs
Altmetric has tracked 7,918,051 research outputs across all sources so far. This one has received more attention than most of these and is in the 58th percentile.
So far Altmetric has tracked 1,066 research outputs from this source. They receive a mean Attention Score of 2.3. This one has gotten more attention than average, scoring higher than 74% 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 201,363 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 65% of its contemporaries.
We're also able to compare this research output to 84 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.