Background and objectives: The aim of this study is to determine the best reference values of the optimal evaluation indexes that identify different disease types. Disease identification was conducted using the YUV quantitative analysis of autofluorescence bronchoscopy (AFB) images in the target areas. Furthermore, this study discusses the significance of AFB in the diagnosis of the central-type lung cancer.
Methods: A biopsy was conducted for cases that showed pathologic changes under either autofluorescence or white-light bronchoscopy. Moreover, MATLAB was used to carry out the quantitative analyses of lesion in multi-color spaces from AFB images. The cases were divided into different groups according to the pathological diagnosis of normal bronchial mucosa, inflammation, low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive cancer. SPSS 11.5 was used to process the data for statistical analysis.
Results: The Y values were different and statistically different between invasive cancer and LGD (P<0.001) and invasive cancer and inflammation (P=0.040), respectively. The U values between invasive cancer and the other groups were statistically different (P<0.050). Similarly, the V values between invasive cancer and LGD and inflammation and normal bronchial mucosa were different. Lastly, the V values between normal bronchial mucosa and HGD and inflammation and normal bronchial mucosa were different.
Conclusions: The YUV values in the AFB effectively identified benign and malignant diseases and were proven to be effective scientific bases for the accurate AFB diagnosis of lung cancer.
背景与目的 通过对不同病理类型的自荧光气管镜（autofluorescence bronchoscope, AFB）图像目标区域的YUV定量分析，确定区分不同疾病类型的最佳判别指标，探讨AFB在中央型支气管肺癌诊断中的价值。方法 对研究对象进行白光气管镜+AFB检查，二者在镜下存在异常者行活检。并对荧光图像显示病变部位通过MATLAB图像测量软件进行YUV定量分析。根据正常支气管粘膜、炎症、低级别上皮样瘤变、高级别上皮样瘤变、浸润性癌的病理结果分组。研究各组与YUV值间的关系，所得数据采用SPSS 11.5软件进行统计学处理。结果 Y值在浸润性癌和LGD组间存在统计学差异（P=0.040），在浸润性癌和炎症组也存在明显统计学差异（P<0.001）。其他的各组间无统计学差异。U值在浸润性癌和HGD、LGD、炎症、正常支气管粘膜组之间存在统计学差异（P<0.050），能较好鉴别正常粘膜及恶性病变。V值在浸润性癌和LGD组（P=0.003）、炎症组（P<0.001）、正常支气管粘膜组（P<0.001）存在统计学差异，能有效鉴别浸润性癌及良性疾病。V值在正常支气管粘膜组与HGD组（P=0.001）、炎症组（P=0.004）间比较也具有统计学差异。结论 利用YUV色彩空间系统针对支气管和肺良恶性疾病鉴别有一定临床应用价值，为临床气管镜诊断肺癌及癌前病变提供有效科学依据。