Evidence map›Paper›PMID 35224966›Full record

ArticleZhongguo fei ai za zhi = Chinese journal of lung cancer2022

[Research Progress in 3D-reconstruction Based Imaging Analysis 
in Partial Solid Pulmonary Nodule].

Zicheng Liu, He Yang, Hongya Wang, Liang Chen, Quan Zhu

Open access · greenAbstract read
In one paragraph

Article in Zhongguo fei ai za zhi = Chinese journal of lung cancer, 2022. 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
0.3field-weighted citation impact, top 44% of its field
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, 2 citations in OpenAlex.

  1. Article
  2. 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

5 authors at 2 institutions in 1 country.

Zicheng LiuDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
He YangDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Hongya WangDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Liang ChenDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Quan ZhuDepartment of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, China.
Jiangsu Province Hospital · CNNanjing Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The incidence and mortality of lung cancer rank first among all malignant tumors in China. With the popularization of high resolution computed tomography (CT) in clinic, chest CT has become an important means of clinical screening for early lung cancer and reducing the mortality of lung cancer. Imaging findings of early lung adenocarcinoma often show partial solid nodules with ground glass components. With the development of imaging, the relationship between the imaging features of some solid nodules and their prognosis has attracted more and more attention. At the same time, with the development of 3D-reconstruction technology, clinicians can improve the accuracy of diagnosis and treatment of such nodules.This article focuses on the traditional imaging analysis of partial solid nodules and the imaging analysis based on 3D reconstruction, and systematically expounds the advantages and disadvantages of both.
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Indexed as

Adenocarcinoma of LungLung NeoplasmsSolitary Pulmonary NoduleHumansImage Processing, Computer-AssistedTomography, X-Ray ComputedLung neoplasmsPrognosisSolid componentThree-dimensional reconstruction

Identifiers

PMID35224966
PMCPMC8913285
OpenAlexW4214500113

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.