Evidence map›Paper›PMID 28377340›Full record

ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2017

[Combined use of thin-section CT and

Yun-Yan Ren, You-Cai Li, Hu-Bing Wu, Quan-Shi Wang, Yan-Jiang Han, Wen-Lan Zhou, Hong-Sheng Li, Zhen Wang, Mohammed Shah Alam Mohammed Shah Alam

Abstract read
In one paragraph

Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2017. 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. Article
  2. [Value of preoperative localization techniques for solitary pulmonary nodules in singleport thoracoscopic surgery].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2020
    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

9 authors.

Yun-Yan RenNanfang PET Center, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China.E-mail: 13268283980@163.com.
You-Cai Li
Hu-Bing Wu
Quan-Shi Wang
Yan-Jiang Han
Wen-Lan Zhou
Hong-Sheng Li
Zhen Wang
Mohammed Shah Alam Mohammed Shah Alam

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveTo investigate whether fluorine-18 fluorodeoxyglucose positron emission tomography/computed tomography (

methodsA total of 267 patients underwent examinations with

resultsThin-section CT provided greater diagnostic information for SPNs in 84.2% of the patients. Compared with Criterion 1, the diagnosis based on Criterion 2 significantly increased the diagnostic sensitivity (80.4% vs 91%, P<0.01) and accuracy (76.4% vs 87.2%, P<0.01) for lung cancer. The lesion size and the CT features including lobulation, air bronchogram, and feeding vessel, but not SUVmax, were all helpful for characterizing non-solid SPNs. Thin-section CT rectified diagnostic errors in 50% (20/40) of the cancerous lesions, which had been diagnosed as benign by PET due to their low metabolism. For non-solid SPNs, Criterion 2 showed a significantly higher diagnostic sensitivity than Criterion 1 (90.0% vs 40.0%, P=0.000) but their diagnostic specificity were comparable (75.2% vs 58.3%, P=0.667). For solid nodules, the use of thin-section CT resulted in no significant improvement in the diagnostic performance (P<0.05).

conclusionThe combination of PET/CT and thin-section CT creates a synergistic effect for the characterization of SPNs, especially non-solid nodules.

Indexed as

Positron Emission Tomography Computed TomographyFluorodeoxyglucose F18HumansLung NeoplasmsRadiopharmaceuticalsSensitivity and SpecificitySolitary Pulmonary NoduleFluorodeoxyglucose F18Radiopharmaceuticals

Identifiers

PMID28377340
PMCPMC6780437

What OpenQuestion holds

Textmetadata
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.