Diagnostic role of liquid-based cytology of bronchial lavage fluid in addition to bronchial brushing specimens in lung cancer

2020 ◽  
pp. 030089162096021
Author(s):  
Chao Cao ◽  
Xuechan Yu ◽  
Tingting Zhu ◽  
Qingwen Jiang ◽  
Yiting Li ◽  
...  

Background: Liquid-based cytology (LBC) tests, including the liquid-based thin layer method, have demonstrated the highest potential for reducing false-negatives and improving sample quality. Method: This study aimed to evaluate the diagnostic role of LBC of bronchial brushing specimens in lung cancer. A total of 249 patients were analyzed in our study, involving 155 patients with combined bronchial brushing and bronchoalveolar lavage (BAL) and 94 patients with BAL alone. Results: The sensitivity in the combined bronchial brushing and BAL group was 61.4% in the diagnosis of lung cancer, which is much higher than with BAL alone. Rates of positive predictive values and negative predictive values in the combined group compared with the BALF alone group were 98.6% vs 100% and 47.6% vs 37.4%, respectively. Sensitivity in the BALF alone group was 12.5% in bronchoscopically invisible pulmonary lesions and as high as 52.1% in the combined group. Conclusion: The results from our study demonstrated that LBC of brushing samples could be used as an important complement of bronchoscopy and could have the potential to be widely applied.

2020 ◽  
Author(s):  
Zaoxiu Hu ◽  
Yonghe Zhao ◽  
Yanlong Yang ◽  
Zhenghai Shen ◽  
Yunchao Huang

Abstract Objective: Recent studies indicated sputum miRNAs may provide a promising approach for non-small cell lung cancer (NSCLC) diagnosis. But some results were still inconsistent. So, we performed meta-analysis to evaluate the diagnostic role of sputum miRNAs for the detection of NSCLC.Methods: Eligible studies that estimated the diagnostic accuracy of sputum miRNAs in NSCLC were searched in Pubmed, Embase and Web of Science and Chinese National Knowledge Infrastructure (CNKI). Data from the eligible studies were collected and pooled; sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratios, weighted symmetric summary ROC curve and the area under the curve (AUC) were calculated by bi-variate random effects model. The between-study heterogeneity was evaluated by Q test and I2 statistics.Results: 30 studies from 16 articles were included for analysis. The overall analysis yielded the sensitivity of 0.77 (95% CI: 0.73–0.81) and specificity of 0.87 (95% CI: 0.83–0.90), with an area under the SROC curve (AUC) of 0.89 (95% CI: 0.86–0.91). Subgroup analysis revealed the diagnostic accuracy in multiple miRNAs studies was higher than single miRNA (the sensitivity, specifcity and an AUC of multiple miRNAs were 0.76, 0.88 and 0.90; and for single miRNA, it was 0.74, 0.74, and 0.80). The diagnostic performance in early stage NSCLC was also very high (the sensitivity, specifcity and an AUC of stage I/II was 0.76, 0.88 and 0.91; and for stage I, it was 0.79, 0.85, and 0.87). We also found miR-210, miR-21, miR-31 and miR-126-3p might serve as potential biomarkers for lung cancer.Conclusion: Sputum miRNAs was useful noninvasive biomarkers for NSCLC diagnosis.


2010 ◽  
Vol 74 (1) ◽  
pp. 231-235 ◽  
Author(s):  
Ningbo Liu ◽  
Li Ma ◽  
Wei Zhou ◽  
Qingsong Pang ◽  
Man Hu ◽  
...  

BMC Cancer ◽  
2018 ◽  
Vol 18 (1) ◽  
Author(s):  
Francesca Andriani ◽  
Elena Landoni ◽  
Mavis Mensah ◽  
Federica Facchinetti ◽  
Rosalba Miceli ◽  
...  

Oncology ◽  
1983 ◽  
Vol 40 (3) ◽  
pp. 177-180 ◽  
Author(s):  
Corrado Gallo Curcio ◽  
Massimo Rinaldi ◽  
Riccardo Tonachella ◽  
Raffaele Perrone Donnorso

Cancer ◽  
2015 ◽  
Vol 121 (S17) ◽  
pp. 3113-3121 ◽  
Author(s):  
Da-Wei Yang ◽  
Yong Zhang ◽  
Qun-Ying Hong ◽  
Jie Hu ◽  
Chun Li ◽  
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CHEST Journal ◽  
2000 ◽  
Vol 117 (2) ◽  
pp. 339-345 ◽  
Author(s):  
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Nib Soehendra ◽  
Parupudi V.J. Sriram ◽  
Lars Schirrow ◽  
Andreas Meyer ◽  
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2017 ◽  
Vol 66 (2) ◽  
pp. 307-312
Author(s):  
Manar Ahmed Abdel Rahman ◽  
Nadia Abdel Moneim Nada ◽  
Khaled Refaat Zalata ◽  
Mohammad Khairy El Badrawy ◽  
Iman Mohammed El Salkh ◽  
...  

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