Exploring the value of pleural fluid biomarkers for complementary pleural effusion disease examination. (October 2021)

• Record Type: Journal Article
• Authors: Thi Huyen, Pham; Li, Meiyu; Li, Lei; Ma, Sike; Zhao, Yan; Yan, Jing; Wang, Zhexiang; Zhao, Meng; Sun, Xuguo
• Is Part Of: Computational biology and chemistry. Volume 94(2021)
• Publication Date: 2021-10
• Subjects: MPE malignant pleural effusion -- PPE parapneumonic effusion -- CHF congestive heart failure -- SG specific gravity -- ADA adenosine deaminase -- LDH lactate dehydrogenase -- TP total protein -- GLU glucose -- CYFR21–1 cytokeratin 19 fragment -- CEA carcinoembryonic antigen -- NSE neuron-specific enolase -- WBC white blood cell -- MNC mononuclear cell -- PNC polymorphonuclear cell -- MTC mesothelial cell -- ROC receiver operating characteristic -- EWM entropy weight method; Pleural effusion -- Biomarkers -- Lung cancer -- Pneumonia -- Deep learning; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2021.107559
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: Objective: Pleural fluid biomarkers are beneficial for the complementary diagnosis of pleural effusion etiologies. This study focuses on the multidimensional evaluation of deep learning to investigate the pleural effusion biomarkers value and the diagnostic utility of combining these markers, in distinguishing pleural effusion etiologies. Methods: Pleural effusion were divided into three groups according to the diagnosis and treatment guidelines: malignant pleural effusion (MPE), parapneumonic effusion (PPE), and congestive heart failure (CHF). First, the value of the biomarker was analyzed by a receiver operating characteristic (ROC) curve. Then by utilizing deep learning and entropy weight method (EWM), the clinical value of biomarkers was computed multidimensionally for complementary diagnosis of pleural effusion diseases. Results: There were significant differences in the six biomarkers, TP, ADA, CEA, CYFRA211, NSE, MNC% (p < 0.05) and no significant differences in three physical characteristics including color, transparency, specific gravity and six other biomarkers such as WBC, PNC%, MTC%, pH level, GLU, LDH (p > 0.05) among the three pleural effusion groups. The comprehensive test of pleural fluid biomarkers based on deep learning is of high accuracy. The clinical value of cytomorphology biomarkers WBC, MNC %, PNC %, MTC % was higher among pleural fluid biomarkers. Conclusion: The clinical value of multi-dimensional analysis of biomarkers by deep learning and entropy weight method is different from the ROC curve analysis. It is suggested that during the clinical examination process, more attention should be paid to the cell morphology biomarkers, but the physical properties of the pleural fluid are less clinical significance. Graphical Abstract: ga1 Highlights: The high information weight of white blood cells and cell morphological classification was explored for the first time. The model based on deep learning was built to distinguish pleural effusion disease. The idea could have an efficient in terms of time-saving, easy-to-use, point-of-care and low-cost distribution. The method can be used not only in clinics but also door-to-door screening in remote areas.