细胞DNA定量分析在肺癌诊断中的应用价值
[Abstract]:Objective: to explore the value of cell DNA quantitative analysis (DNA image cytometry,DNA-ICM) in the diagnosis of lung cancer by comparing with liquid based thin layer cytology (liquid-based cytology,LBC), and to find an effective method for early diagnosis of lung cancer. Methods: a total of 570 lung cancer specimens (including 246 sputum specimens and 226 BALF specimens) were examined by DNA quantitative analysis and liquid-based thin layer cytology. A comparative analysis was made between the fiberoptic bronchoscopic brushes (98 specimens) and non-lung cancer group (sputum (213 specimens), BALF specimens (205 specimens) and fiberoptic bronchoscopy brushes (47 specimens). Results: the sensitivity of DNA-ICM in BALF was 71.2 and the specificity was 100. The sensitivity of LBC in BALF was 49.1, the specificity of 97.6%.DNA-ICM in sputum was 41.9, the specificity of 97.6%.DNA-ICM in sputum was 22.4and the specificity was 98.1. The sensitivity of DNA-ICM in both specimens was higher than that of LBC (P0.01). The sensitivity of DNA-ICM in BALF was 77.5% in 117 patients who collected both BALF and fiberoptic bronchoscopic brushes. The sensitivity of 100%.DNA-ICM in fiberoptic bronchoscopic brushes was 58.8, and the specificity of 100%.DNA-ICM in BALF was higher than that in fiberoptic bronchoscopic brushes (P0.05). In lung cancer patients with different histological types, the sensitivity of DNA-ICM in adenocarcinoma was 59.8%. The sensitivity of 77.4%.DNA-ICM in squamous cell carcinoma was higher than that in adenocarcinoma (P0.01). Conclusion: quantitative analysis of cell DNA is of great value in the diagnosis of lung cancer, both in sputum, bronchoalveolar lavage fluid and bronchofiberscope brush specimen. The sensitivity of quantitative analysis of cell DNA was higher than that of liquid-based cytology, and the rate of missed diagnosis could be reduced to some extent. Combined with LBC can improve the positive diagnosis rate of lung cancer and can be used as a new method for early diagnosis of lung cancer.
【学位授予单位】:安徽医科大学
【学位级别】:硕士
【学位授予年份】:2016
【分类号】:R734.2
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