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.