↓ Skip to main content

Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy

Overview of attention for article published in Radiation Oncology, July 2018
Altmetric Badge

Mentioned by

twitter
2 X users

Citations

dimensions_citation
29 Dimensions

Readers on

mendeley
83 Mendeley
Title
Investigating rectal toxicity associated dosimetric features with deformable accumulated rectal surface dose maps for cervical cancer radiotherapy
Published in
Radiation Oncology, July 2018
DOI 10.1186/s13014-018-1068-0
Pubmed ID
Authors

Jiawei Chen, Haibin Chen, Zichun Zhong, Zhuoyu Wang, Brian Hrycushko, Linghong Zhou, Steve Jiang, Kevin Albuquerque, Xuejun Gu, Xin Zhen

Abstract

Better knowledge of the dose-toxicity relationship is essential for safe dose escalation to improve local control in cervical cancer radiotherapy. The conventional dose-toxicity model is based on the dose volume histogram, which is the parameter lacking spatial dose information. To overcome this limit, we explore a comprehensive rectal dose-toxicity model based on both dose volume histogram and dose map features for accurate radiation toxicity prediction. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively studied, including 12 with Grade ≥ 2 rectum toxicity and 30 patients with Grade 0-1 toxicity (non-toxicity patients). The cumulative equivalent 2-Gy rectal surface dose was deformably summed using the deformation vector fields obtained through a recent developed local topology preserved non-rigid point matching algorithm. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectum surface dose map (RSDM). The dose volume parameters (DVPs) were calculated from the 3D rectum surface, while the texture features and the dose geometric parameters (DGPs) were extracted from the 2D RSDM. Representative features further computed from DVPs, textures and DGPs by principle component analysis (PCA) and statistical analysis were respectively fed into a support vector machine equipped with a sequential feature selection procedure. The predictive powers of the representative features were compared with the GEC-ESTRO dosimetric parameters D0.1/1/2cm3. Satisfactory predictive accuracy of sensitivity 74.75 and 84.75%, specificity 72.67 and 79.87%, and area under the receiver operating characteristic curve (AUC) 0.82 and 0.91 were respectively achieved by the PCA features and statistical significant features, which were superior to the D0.1/1/2cm3 (AUC 0.71). The relative area in dose levels of 64Gy, 67Gy, 68Gy, 87Gy, 88Gy and 89Gy, perimeters in dose levels of 89Gy, as well as two texture features were ranked as the important factors that were closely correlated with rectal toxicity. Our extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation.

X Demographics

X Demographics

The data shown below were collected from the profiles of 2 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 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 83 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 15 18%
Student > Ph. D. Student 11 13%
Researcher 10 12%
Student > Bachelor 7 8%
Student > Doctoral Student 5 6%
Other 10 12%
Unknown 25 30%
Readers by discipline Count As %
Medicine and Dentistry 22 27%
Physics and Astronomy 10 12%
Computer Science 7 8%
Nursing and Health Professions 3 4%
Engineering 3 4%
Other 7 8%
Unknown 31 37%
Attention Score in Context

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 08 July 2018.
All research outputs
#18,641,800
of 23,094,276 outputs
Outputs from Radiation Oncology
#1,431
of 2,079 outputs
Outputs of similar age
#252,936
of 327,716 outputs
Outputs of similar age from Radiation Oncology
#22
of 29 outputs
Altmetric has tracked 23,094,276 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% 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 is in the 18th percentile – i.e., 18% 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 327,716 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 12th percentile – i.e., 12% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one is in the 17th percentile – i.e., 17% of its contemporaries scored the same or lower than it.