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Predictive SIRT dosimetry based on a territorial model

Overview of attention for article published in EJNMMI Physics, October 2017
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  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

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2 tweeters

Citations

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7 Dimensions

Readers on

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35 Mendeley
Title
Predictive SIRT dosimetry based on a territorial model
Published in
EJNMMI Physics, October 2017
DOI 10.1186/s40658-017-0192-5
Pubmed ID
Authors

Nadine Spahr, Philipp Schilling, Smita Thoduka, Nasreddin Abolmaali, Andrea Schenk

Abstract

In the planning of selective internal radiation therapy (SIRT) for liver cancer treatment, one major aspect is to determine the prescribed activity and to estimate the resulting absorbed dose inside normal liver and tumor tissue. An optimized partition model for SIRT dosimetry based on arterial liver territories is proposed. This model is dedicated to characterize the variability of dose within the whole liver. For an arbitrary partition, the generalized absorbed dose is derived from the classical partition model. This enables to consider normal liver partitions for each arterial perfusion supply area and one partition for each tumor for activity and dose calculation. The proposed method excludes a margin of 11 mm emitting range around tumor volumes from normal liver to investigate the impact on activity calculation. Activity and dose calculation was performed for five patients using the body-surface-area (BSA) method, the classical and territorial partition model. The territorial model reaches smaller normal liver doses and significant higher tumor doses compared to the classical partition model. The exclusion of a small region around tumors has a significant impact on mean liver dose. Determined tumor activities for the proposed method are higher in all patients when limited by normal liver dose. Activity calculation based on BSA achieves in all cases the lowest amount. The territorial model provides a more local and patient-individual dose distribution in normal liver taking into account arterial supply areas. This proposed arterial liver territory-based partition model may be used for SPECT-independent activity calculation and dose prediction under the condition of an artery-based simulation for particle distribution.

Twitter Demographics

The data shown below were collected from the profiles of 2 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 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 10 29%
Student > Ph. D. Student 5 14%
Professor 3 9%
Other 2 6%
Student > Doctoral Student 2 6%
Other 5 14%
Unknown 8 23%
Readers by discipline Count As %
Medicine and Dentistry 10 29%
Physics and Astronomy 5 14%
Computer Science 2 6%
Agricultural and Biological Sciences 1 3%
Biochemistry, Genetics and Molecular Biology 1 3%
Other 4 11%
Unknown 12 34%

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 November 2017.
All research outputs
#8,738,937
of 14,592,815 outputs
Outputs from EJNMMI Physics
#24
of 77 outputs
Outputs of similar age
#208,556
of 402,271 outputs
Outputs of similar age from EJNMMI Physics
#6
of 13 outputs
Altmetric has tracked 14,592,815 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 77 research outputs from this source. They receive a mean Attention Score of 2.2. This one has gotten more attention than average, scoring higher than 68% 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 402,271 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 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 53% of its contemporaries.