Evidence map›Paper›PMID 29980214›Full record

ArticleRadiation oncology (London, England)2018

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

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

Abstract read
In one paragraph

Article in Radiation oncology (London, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
18citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

18 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. A review of artificial intelligence in brachytherapy.Journal of applied clinical medical physics · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Jiawei ChenSchool of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
Haibin ChenSchool of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China.
Zichun ZhongDepartment of Computer Science, Wayne State University, Detroit, MI, 48202, USA.
Zhuoyu WangDepartment of Epidemiology, Biostatistics and Occupational Health, McGill University, 805 Sherbrooke Street West, Montreal, Quebec, H3A 0G4, Canada.
Brian HrycushkoDepartment of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA.
Linghong ZhouSchool of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China. smart@smu.edu.cn.
Steve JiangDepartment of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA.
Kevin AlbuquerqueDepartment of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA.
Xuejun GuDepartment of Radiation Oncology, The University of Texas, Southwestern Medical Center, Dallas, TX, 75390, USA. Xuejun.Gu@utsouthwestern.edu.
Xin ZhenSchool of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, Guangdong, China. xinzhen@smu.edu.cn.ORCID http://orcid.org/0000-0002-5037-2801

Funding

National Natural Science Foundation of China 81728016 and 81571771National Science Foundation ACI-1657364Natural Science Foundation of Guangdong Province 2015A030313302Science and Technology Planning Project of Guangdong Province 2015A020214013Varian Medical Systems OTD-109235
6 · The paper itself

Abstract

backgroundBetter 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.

methodsForty-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 D

resultsSatisfactory 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 D

conclusionsOur extensive experimental results have demonstrated the feasibility of the proposed scheme. A future large patient cohort study is still needed for model validation.

Indexed as

Radiotherapy DosageAlgorithmsArea Under CurveBrachytherapyFemaleHumansMaleOrgans at RiskPrincipal Component AnalysisRadiation InjuriesRadiometryRadiotherapy, Intensity-ModulatedRectumRetrospective StudiesSensitivity and SpecificityUterine Cervical NeoplasmsCervical cancerDeformable registrationDose accumulationMachine learningRectum toxicity prediction

Identifiers

PMID29980214
PMCPMC6035458

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.