Department of Chemoradiotherapy, Tangshan People's Hospital, Tangshan, P.R. China.
Nuclear Medicine Laboratory, Tangshan People's Hospital, Tangshan, P.R. China.
Biosci Rep. 2020 Apr 30;40(4). doi: 10.1042/BSR20200299.
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.
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.
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.
The diagnosis model constructed in the present study is of certain value for the differential diagnosis of ARP and IP.
本研究旨在构建基于多个参数的急性放射性肺炎（ARP）和感染性肺炎早期鉴别诊断模型。
本研究纳入了唐山人民医院放射化疗科收治的 152 例放射性肺炎（91 例）和感染性肺炎（61 例）患者。收集了放射物理学参数、影像学特征、血清学指标等数据作为自变量，以 ARP 为因变量。采用单因素逻辑回归和 Spearman 相关分析筛选自变量，用多因素逻辑回归拟合变量构建回归模型预测 ARP。
单因素分析显示，病变与 V20 区域（PRLV）、降钙素原（PCT）、C 反应蛋白（CRP）、平均肺剂量（MLD）和肺体积接受≥20 Gy（V20）之间的位置关系与 ARP 相关，而计划靶区（PTV）剂量与 ARP 相关。多因素分析显示，PRLV、PCT、白细胞（WBC）和 MLD 是独立的诊断因素。在此基础上建立了列线图模型。模型的曲线下面积（AUC）为 0.849，明显优于单个指标，模型的灵敏度和特异性均较高（分别为 82.4%和 82.0%）。模型预测结果与实际诊断结果高度一致。决策曲线分析（DCA）表明该模型具有较好的阳性净获益。
本研究构建的诊断模型对 ARP 和 IP 的鉴别诊断具有一定的应用价值。
