Evidence map›Paper›PMID 31077263›Full record

SynthesisMedical science monitor : international medical journal of experimental and clinical research2019

Diagnostic Performance of Perfusion Computed Tomography for Differentiating Lung Cancer from Benign Lesions: A Meta-Analysis.

Cuiqing Huang, Jianye Liang, Xueping Lei, Xi Xu, Zeyu Xiao, Liangping Luo

Abstract readMeta-AnalysisSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Medical science monitor : international medical journal of experimental and clinical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 5 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

16 citing papers in PubMed, 5 syntheses or guidelines pooled it.

  1. Pooled it
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  8. Review
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  15. Computed Tomography Perfusion Imaging Quality Affected by Different Input Arteries in Patients of Internal Carotid Artery Stenosis.Medical science monitor : international medical journal of experimental and clinical research · 2019
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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

6 authors.

Cuiqing HuangMedical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland).
Jianye LiangMedical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland).
Xueping LeiKey Laboratory of Molecular Target and Clinical Pharmacology, School of Pharmaceutical Sciences and Fifth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China (mainland).
Xi XuMedical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland).
Zeyu XiaoMedical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland).
Liangping LuoMedical Imaging Center, The First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, China (mainland).

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND Numerous studies have explored diagnosis of pulmonary nodules using perfusion computed tomography (CT); however, findings were not always consistent between studies. Th e present study aimed to summarize evidence on the diagnostic value of perfusion CT for distinguishing between lung cancer and benign lesions. MATERIAL AND METHODS We performed a systematic literature search on lung cancer and benign pulmonary lesions performed with perfusion CT. The searches were undertaken in English or Chinese language in Medline, PubMed, Embase, Cochrane Library, Web of Science, and China National Knowledge Infrastructure database from Jan 2010 to Nov 2018. Standardized mean differences (SMDs) and 95% confidence intervals (CIs) of blood volume (BV), blood flow (BF), mean transit time (MTT), and permeability surface (PS) were calculated using Review Manager 5.3. Publication bias, sensitivity, specificity, and the area under the curve (AUC) were calculated using Stata12.0. RESULTS Fourteen studies comprising 1032 malignant and 447 benign pulmonary lesions were analyzed. Lung cancer had higher BV, BF, MTT, and PS values than benign lesions. SMDs and 95% CIs of BV, BF, MTT, and PS were 2.29 (1.43, 3.16), 0.50 (0.14, 0.86), 0.55 (0.39, 0.72), and 1.21 (0.87, 1.56), respectively. AUC values of BV and PS were 0.92 (0.90, 0.94) and 0.83 (0.80, 0.86), respectively. CONCLUSIONS CT perfusion imaging is a valuable technique for the diagnosis of pulmonary nodules. Lung cancer had higher perfusion and permeability than benign lesions. The evidence suggests blood volume is the best surrogate marker for characterizing the blood supply, while permeability surface has a high specificity in quantifying the vascular permeability.

Indexed as

AdultAgedAged, 80 and overArea Under CurveChinaFemaleHumansLungLung NeoplasmsMaleMiddle AgedPerfusionPerfusion ImagingPublication BiasSensitivity and SpecificityTomography, X-Ray Computed

Identifiers

PMID31077263
PMCPMC6526743

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.