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Target dose conversion modeling from pencil beam (PB) to Monte Carlo (MC) for lung SBRT

Overview of attention for article published in Radiation Oncology, June 2016
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2 tweeters

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14 Dimensions

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22 Mendeley
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Title
Target dose conversion modeling from pencil beam (PB) to Monte Carlo (MC) for lung SBRT
Published in
Radiation Oncology, June 2016
DOI 10.1186/s13014-016-0661-3
Pubmed ID
Authors

Dandan Zheng, Xiaofeng Zhu, Qinghui Zhang, Xiaoying Liang, Weining Zhen, Chi Lin, Vivek Verma, Shuo Wang, Andrew Wahl, Yu Lei, Sumin Zhou, Chi Zhang

Abstract

A challenge preventing routine clinical implementation of Monte Carlo (MC)-based lung SBRT is the difficulty of reinterpreting historical outcome data calculated with inaccurate dose algorithms, because the target dose was found to decrease to varying degrees when recalculated with MC. The large variability was previously found to be affected by factors such as tumour size, location, and lung density, usually through sub-group comparisons. We hereby conducted a pilot study to systematically and quantitatively analyze these patient factors and explore accurate target dose conversion models, so that large-scale historical outcome data can be correlated with more accurate MC dose without recalculation. Twenty-one patients that underwent SBRT for early-stage lung cancer were replanned with 6MV 360° dynamic conformal arcs using pencil-beam (PB) and recalculated with MC. The percent D95 difference (PB-MC) was calculated for the PTV and GTV. Using single linear regression, this difference was correlated with the following quantitative patient indices: maximum tumour diameter (MaxD); PTV and GTV volumes; minimum distance from tumour to soft tissue (dmin); and mean density and standard deviation of the PTV, GTV, PTV margin, lung, and 2 mm, 15 mm, 50 mm shells outside the PTV. Multiple linear regression and artificial neural network (ANN) were employed to model multiple factors and improve dose conversion accuracy. Single linear regression with PTV D95 deficiency identified the strongest correlation on mean-density (location) indices, weaker on lung density, and the weakest on size indices, with the following R(2) values in decreasing orders: shell2mm (0.71), PTV (0.68), PTV margin (0.65), shell15mm (0.62), shell50mm (0.49), lung (0.40), dmin (0.22), GTV (0.19), MaxD (0.17), PTV volume (0.15), and GTV volume (0.08). A multiple linear regression model yielded the significance factor of 3.0E-7 using two independent features: mean density of shell2mm (P = 1.6E-7) and PTV volume (P = 0.006). A 4-feature ANN model slightly improved the modeling accuracy. Quantifiable density features were proposed, replacing simple central/peripheral location designation, which showed strong correlations with PB-to-MC target dose conversion magnitude, followed by lung density and target size. Density in the immediate outer and inner areas of the PTV showed the strongest correlations. A multiple linear regression model with one such feature and PTV volume established a high significance factor, improving dose conversion accuracy.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Belgium 1 5%
Unknown 21 95%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 27%
Student > Master 4 18%
Other 3 14%
Student > Ph. D. Student 2 9%
Professor 1 5%
Other 3 14%
Unknown 3 14%
Readers by discipline Count As %
Medicine and Dentistry 8 36%
Physics and Astronomy 6 27%
Computer Science 1 5%
Unknown 7 32%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 19 June 2016.
All research outputs
#5,721,000
of 7,917,108 outputs
Outputs from Radiation Oncology
#757
of 1,066 outputs
Outputs of similar age
#168,755
of 262,522 outputs
Outputs of similar age from Radiation Oncology
#23
of 39 outputs
Altmetric has tracked 7,917,108 research outputs across all sources so far. This one is in the 24th percentile – i.e., 24% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,066 research outputs from this source. They receive a mean Attention Score of 2.3. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 262,522 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 29th percentile – i.e., 29% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 39 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.