ArticleCancer medicine2016
RGB and HSV quantitative analysis of autofluorescence bronchoscopy used for characterization and identification of bronchopulmonary cancer.
Article in Cancer medicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep learning diagnostics for bladder tumor identification and grade prediction using RGB method.Scientific reports · 2022Article
- A Tuberculous Bronchial Artery Aneurysm with Abnormal Findings on Autofluorescence Imaging Bronchoscopy.Internal medicine (Tokyo, Japan) · 2020Article
- Retraction statement: RGB and HSV quantitative analysis of autofluorescence bronchoscopy used for characterization and identification of bronchopulmonary cancer.Cancer medicine · 2020Article
- A bronchial fibroepithelial polyp with abnormal findings on auto-fluorescence imaging.Respirology case reports · 2017Article
- RGB and HSV quantitative analysis of autofluorescence bronchoscopy used for characterization and identification of bronchopulmonary cancer.Cancer medicine · 2016Article
Corrections and comments
- Retraction · 2020-03-12Duplication of/in Article ·
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Autofluorescence bronchoscopy (AFB) shows good sensitivity in detecting dysplasia and bronchopulmonary cancer. However, the poor specificity of AFB would lead to excessive biopsy. The aim of the study is to establish a more effective quantitative method (optimal identification index and reference value) for characterizing the AFB images within the region of interest and discuss AFB's significance in the diagnosis of central-type lung cancer. A total of 218 suspected lung cancer patients were enrolled in this study. A quantitative analysis based on color space (red, green, blue[RGB] and HSV system) was conducted and the result was compared with the final diagnosis obtained by the pathology of biopsy. Cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade preinvasive (LGD), high-grade preinvasive (HGD), and invasive cancer. Quantitative analyses in multi-color spaces for the lesions showed by AFB images were conducted by software MATLAB. Finally, there is statistical significance among the different groups in some parameter in RGB and HSV system. So, both RGB and HSV quantitative analysis of autofluorescence bronchoscopy are useful to define benign and malignant diseases, which can objectively guide the bronchoscopist in selecting sites for biopsy with good pathologic correlation.
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