Early differential diagnosis model for acute radiation pneumonitis based on multiple parameters.

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.

journal_name

Biosci Rep

journal_title

Bioscience reports

authors

Wang Z,Wu Q,Dong L,Fu H,Liu Q

doi

10.1042/BSR20200299

subject

Has Abstract

pub_date

2020-04-30 00:00:00

issue

4

eissn

0144-8463

issn

1573-4935

pii

222624

journal_volume

40

pub_type

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