Evidence map›Paper›PMID 36694318›Full record

ReviewCurrent medical imaging2023

Research on Segmentation Technology in Lung Cancer Radiotherapy Based on Deep Learning.

Jun Huang, Tao Liu, Beibei Qian, Zhibo Chen, Ya Wang

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current medical imaging, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

5 authors.

Jun HuangSchool of Computer and Information Engineering, Fuyang Normal University, Fuyang Anhui 236037, China.
Tao LiuSchool of Computer and Information Engineering, Fuyang Normal University, Fuyang Anhui 236037, China.
Beibei QianSchool of Computer and Information Engineering, Fuyang Normal University, Fuyang Anhui 236037, China.
Zhibo ChenSchool of Computer and Information Engineering, Fuyang Normal University, Fuyang Anhui 236037, China.
Ya WangSchool of Computer and Information Engineering, Fuyang Normal University, Fuyang Anhui 236037, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundLung cancer has the highest mortality rate among cancers. Radiation therapy (RT) is one of the most effective therapies for lung cancer. The correct segmentation of lung tumors (LTs) and organs at risk (OARs) is the cornerstone of successful RT.

methodsWe searched four databases for relevant material published in the last 10 years: Web of Science, PubMed, Science Direct, and Google Scholar. The advancement of deep learning-based segmentation technology for lung cancer radiotherapy (DSLC) research was examined from the perspectives of LTs and OARs.

resultsIn this paper, Most of the dice similarity coefficient (DSC) values of LT segmentation in the surveyed literature were above 0.7, whereas the DSC indicators of OAR segmentation were all over 0.8.

conclusionThe contribution of this review is to summarize DSLC research methods and the issues that DSLC faces are discussed, as well as possible viable solutions. The purpose of this review is to encourage collaboration among experts in lung cancer radiotherapy and DL and to promote more research into the use of DL in lung cancer radiotherapy.

Indexed as

Deep LearningLung NeoplasmsHumansImage Processing, Computer-AssistedNeural Networks, ComputerRadiotherapy Planning, Computer-AssistedTechnologyTomography, X-Ray Computeddeep learningimage segmentationLung cancerlung tumorsorgans at riskradiation therapy

Identifiers

PMID36694318
PMCPMC10351083

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.