Evidence map›Paper›PMID 27436838›Full record

ArticleTechnology in cancer research & treatment2017

Application of Quantitative Autofluorescence Bronchoscopy Image Analysis Method in Identifying Bronchopulmonary Cancer.

Xiaoxuan Zheng, Hongkai Xiong, Yong Li, Baohui Han, Jiayuan Sun

Open access · bronzeAbstract read
In one paragraph

Article in Technology in cancer research & treatment, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.3field-weighted citation impact, top 35% 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

4 citing papers in PubMed, 12 citations in OpenAlex.

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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

5 authors at 2 institutions in 1 country.

Xiaoxuan Zheng1 Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Hongkai Xiong2 Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
Yong Li2 Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai, China.
Baohui Han3 Department of Pulmonary Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Jiayuan Sun1 Department of Endoscopy, Shanghai Chest Hospital, Shanghai Jiao Tong University, Shanghai, China.
Shanghai Jiao Tong University · CNShanghai Chest Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Autofluorescence bronchoscopy shows good sensitivity and poor specificity in detecting dysplasia and cancer of the bronchus. Through quantitative analysis on the target area of autofluorescence bronchoscopy image, determine the optimal identification index and reference value for identifying different types of diseases and explore the value of autofluorescence bronchoscopy in diagnosis of lung cancer. Patients with 1 or more preinvasive bronchial lesions were enrolled and followed up by white-light bronchoscope and autofluorescence bronchoscopy. Color space quantitative image analysis was conducted on the lesion shown in the autofluorescence image using MATLAB image measurement software. A retrospective analysis was conducted on 218 cases with 1208 biopsies. One hundred seventy-three cases were diagnosed as positive, which included 151 true-positive cases and 22 false-positive cases. White-light bronchoscope associated with autofluorescence bronchoscopy was able to differentiate between benign and malignant lesion with a high sensitivity, specificity, positive predictive value, and negative predictive value (92.1%, 59.3%, 87.3%, and 71.1%, respectively). Taking 1.485 as the cutoff value of receiver operating characteristic of red-to-green value to differentiate benign and malignant diseases, the diagnostic sensitivity reached 82.3% and the specificity reached 80.5%. U values could differentiate invasive carcinoma and other groups well. Quantitative image analysis method of autofluorescence bronchoscopy provided effective scientific basis for the diagnosis of lung cancer and precancerous lesions.

Indexed as

BronchoscopyCarcinoma, Squamous CellHumansLung NeoplasmsOptical ImagingRetrospective StudiesROC Curveautofluorescence bronchoscopydiagnosislung cancermedical image processingwhite-light bronchoscopy

Identifiers

PMID27436838
PMCPMC5616066
OpenAlexW2491139922

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.