Evidence map›Paper›PMID 33262698›Full record

ArticleFrontiers in neurorobotics2020

Correlated Skin Surface and Tumor Motion Modeling for Treatment Planning in Robotic Radiosurgery.

Shumei Yu, Pengcheng Hou, Rongchuan Sun, Shaolong Kuang, Fengfeng Zhang, Mingchuan Zhou, Jing Guo, Lining Sun

Abstract read
In one paragraph

Article in Frontiers in neurorobotics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–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

3 citing papers in PubMed.

  1. Review
  2. [Prediction of respiratory motion based on sequential embedding combined with relational embedding].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2022
    Article
  3. Article
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

8 authors.

Shumei YuSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou, China.
Pengcheng HouSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou, China.
Rongchuan SunSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou, China.
Shaolong KuangSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou, China.
Fengfeng ZhangSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou, China.
Mingchuan ZhouComputer Aided Medical Procedures, Technical University of Munich, Munich, Germany.
Jing GuoSchool of Automation, Guangdong University of Technology, Guangzhou, China.
Lining SunSchool of Mechanical and Electrical Engineering, Soochow University, Suzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In robotic radiosurgery, motion tracking is crucial for accurate treatment planning of tumor inside the thoracic or abdominal cavity. Currently, motion characterization for respiration tracking mainly focuses on markers that are placed on the surface of human chest. Nevertheless, limited markers are not capable of expressing the comprehensive motion feature of the human chest and abdomen. In this paper, we proposed a method of respiratory motion characterization based on the voxel modeling of the thoracoabdominal torso. Point cloud data from depth cameras were used to achieve three-dimensional modeling of the chest and abdomen surface during respiration, and a dimensionality reduction algorithm was proposed to extract respiratory features from the established voxel model. Finally, experimental results including the accuracy of voxel model and correlation coefficient were compared to validate the feasibility of the proposed method, which provides enhanced accuracy of target motion correlation than traditional methods that utilized external markers.

Indexed as

correlation modelrespiratory motion characterizationsurface modelingtumor trackingvoxel model

Identifiers

PMID33262698
PMCPMC7688455

What OpenQuestion holds

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LicenceCC BY
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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.