↓ Skip to main content

Applying a RapidPlan model trained on a technique and orientation to another: a feasibility and dosimetric evaluation

Overview of attention for article published in Radiation Oncology, August 2016
Altmetric Badge

Mentioned by

twitter
1 tweeter

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
42 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Applying a RapidPlan model trained on a technique and orientation to another: a feasibility and dosimetric evaluation
Published in
Radiation Oncology, August 2016
DOI 10.1186/s13014-016-0684-9
Pubmed ID
Authors

Hao Wu, Fan Jiang, Haizhen Yue, Hui Zhang, Kun Wang, Yibao Zhang

Abstract

The development of a dose-volume-histogram (DVH) estimation model for knowledge-based planning is very time-consuming and it could be inefficient if it was only used for similar upcoming cases as supposed. It is clinically desirable to explore and validate other potential applications for a configured model. This study tests the hypothesis that a supine volumetric modulated arc therapy (VMAT) model can optimize intensity modulated radiotherapy (IMRT) plans of other patient setup orientations. Based on RapidPlan, a DVH estimation model was trained using 81 supine VMAT rectal plans and validated on 10 similar cases to ensure the robustness of its designed purpose. Attempts were then made to apply the model to re-optimize the dynamic MLC-sequences of the duplicated IMRT plans from 30 historical patients (20 prone and 10 supine) that were treated with the same prescription as for the model (50.6 and 41.8 Gy to 95 % of PGTV and PTV simultaneously/22 fractions). The performance of knowledge-based re-optimization and the impact of setup orientations were evaluated dosimetrically. The VMAT model validation on similar cases showed comparable target dose distribution and significantly improved organ sparing (by 10.77 ~ 18.65 %) than the original plans. IMRT plans of either setup can be re-optimized using the supine VMAT model, which significantly reduced the dose to the bladder (by 25.88 % from 33.85 ± 2.96 to 25.09 ± 1.32 Gy for D50 %; by 22.77 % from 33.99 ± 2.77 to 26.25 ± 1.22 Gy for mean dose) and femoral head (by 12.27 % from 15.65 ± 3.33 to 13.73 ± 1.43 Gy for D50 %; by 10.09 % from 16.26 ± 2.74 to 14.62 ± 1.10 Gy for mean dose), all P < 0.01. Although the dose homogeneity and PGTV conformity index (CI_PGTV) changed slightly (≤0.01), CI_PTV of IMRT plans was significantly increased (Δ = 0.17, P < 0.01) by the manually defined target-objectives in the VMAT optimizer. The semi-automated IMRT planning increased the global maximum dose and V107 % due to the missing of hot spot suppression by specific manual optimizing or fluence map editing. The Varian RapidPlan model trained on a technique and orientation can be used for another. Knowledge-based planning improves organ sparing and quality consistency, yet the target-objectives defined for VMAT-optimizer should be readapted to IMRT planning, followed by manual hot spot processing.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 42 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Unknown 41 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 19%
Other 6 14%
Researcher 4 10%
Professor 4 10%
Student > Master 4 10%
Other 11 26%
Unknown 5 12%
Readers by discipline Count As %
Physics and Astronomy 9 21%
Medicine and Dentistry 9 21%
Engineering 3 7%
Nursing and Health Professions 3 7%
Psychology 2 5%
Other 5 12%
Unknown 11 26%

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 August 2016.
All research outputs
#7,134,079
of 8,247,986 outputs
Outputs from Radiation Oncology
#977
of 1,084 outputs
Outputs of similar age
#214,396
of 253,918 outputs
Outputs of similar age from Radiation Oncology
#39
of 42 outputs
Altmetric has tracked 8,247,986 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,084 research outputs from this source. They receive a mean Attention Score of 2.2. This one is in the 1st percentile – i.e., 1% 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 253,918 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.