Early differential diagnosis model for acute radiation pneumonitis based on multiple parameters. Issue 4 (17th April 2020)

• Record Type: Journal Article
• Authors: Wang, Zhiwu; Wu, Qiong; Dong, Liang; Fu, Haoyu; Liu, Qiwei
• Is Part Of: Bioscience reports. Volume 40:Issue 4(2020)
• Publication Date: 2020-04-17
• Subjects: Diagnosis model -- differential diagnosis -- infectious pneumonia -- radiation pneumonitis -- radiotherapy; Molecular biology -- Periodicals; Cytology -- Periodicals; 572.8
• DOI: 10.1042/BSR20200299
• Languages: English
• ISSNs: 0144-8463

Abstract

Abstract: Objective: The present study aimed to construct a diagnosis model for the early differentiation of acute radiation pneumonitis (ARP) and infectious pneumonitis based on multiple parameters. Methods: The present study included data of 152 patients admitted to the Department of Radiochemotherapy, Tangshan People's Hospital, who developed ARP (91 patients) or infectious pneumonia (IP; 61 patients) after radiotherapy. The radiophysical parameters, imaging characteristics, serological indicators, and other data were collected as independent variables, and ARP was considered as a dependent variable. Logistics univariate analysis and Spearman correlation analysis were used for selecting independent variables. Logistics multivariate analysis was used to fit the variables into the regression model to predict ARP. Results: The univariate analysis showed that the positional relation between lesions and V20 area (PRLV), procalcitonin (PCT), C-reactive protein (CRP), mean lung dose (MLD), and lung volume receiving ≥20 Gy (V20) correlated with ARP while the planning target volume (PTV) dose marginally correlated with ARP. The multivariate analysis showed that the PRLV, PCT, white blood cell (WBC), and MLD were independent diagnostic factors. The nomogram was drawn on the basis of the logistics regression model. The area under the curve (AUC) of the model was 0.849, which was significantly better than that of a single indicator and the sensitivity and specificity of the model were high (82.4 and 82.0%, respectively). These results predicted by the model were highly consistent with the actual diagnostic results. The decision curve analysis (DCA) demonstrated a satisfactory positive net benefit of the model. Conclusion: The diagnosis model constructed in the present study is of certain value for the differential diagnosis of ARP and IP.