Lu Chao, Xing Zhi-Xiang, Xia Xi-Gang, Long Zhi-Da, Chen Bo, Zhou Peng, Wang Rui
Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China.
World J Gastrointest Oncol. 2023 Jul 15;15(7):1241-1252. doi: 10.4251/wjgo.v15.i7.1241.
There are factors that significantly increase the risk of postoperative pulmonary infections in patients with primary hepatic carcinoma (PHC). Previous reports have shown that over 10% of patients with PHC experience postoperative pulmonary infections. Thus, it is crucial to prioritize the prevention and treatment of postoperative pulmonary infections in patients with PHC.
To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.
We retrospectively collected data from 505 patients who underwent hepatobiliary surgery between January 2015 and February 2023 in the Department of Hepatobiliary and Pancreaticospleen Surgery. Radiomics data were selected for statistical analysis, and clinical pathological parameters and imaging data were included in the screening database as candidate predictive variables. We then developed a pulmonary infection prediction model using three different models: An artificial neural network model; a random forest model; and a generalized linear regression model. Finally, we evaluated the accuracy and robustness of the prediction model using the receiver operating characteristic curve and decision curve analyses.
Among the 505 patients, 86 developed a postoperative pulmonary infection, resulting in an incidence rate of 17.03%. Based on the gray-level co-occurrence matrix, we identified 14 categories of radiomic data for variable screening of pulmonary infection prediction models. Among these, energy, contrast, the sum of squares (SOS), the inverse difference (IND), mean sum (MES), sum variance (SUV), sum entropy (SUE), and entropy were independent risk factors for pulmonary infection after hepatectomy and were listed as candidate variables of machine learning prediction models. The random forest model algorithm, in combination with IND, SOS, MES, SUE, SUV, and entropy, demonstrated the highest prediction efficiency in both the training and internal verification sets, with areas under the curve of 0.823 and 0.801 and a 95% confidence interval of 0.766-0.880 and 0.744-0.858, respectively. The other two types of prediction models had prediction efficiencies between areas under the curve of 0.734 and 0.815 and 95% confidence intervals of 0.677-0.791 and 0.766-0.864, respectively.
Postoperative pulmonary infection in patients undergoing hepatectomy may be related to risk factors such as IND, SOS, MES, SUE, SUV, energy, and entropy. The prediction model in this study based on diffusion-weighted images, especially the random forest model algorithm, can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.
存在一些因素会显著增加原发性肝癌（PHC）患者术后肺部感染的风险。既往报道显示，超过10%的PHC患者会发生术后肺部感染。因此，对PHC患者术后肺部感染的防治进行优先排序至关重要。
确定PHC患者术后肺部感染的危险因素，并建立一个预测模型以辅助术后管理。
我们回顾性收集了2015年1月至2023年2月在肝胆胰脾外科接受肝胆手术的505例患者的数据。选择放射组学数据进行统计分析，并将临床病理参数和影像数据纳入筛查数据库作为候选预测变量。然后，我们使用三种不同的模型建立了肺部感染预测模型：人工神经网络模型；随机森林模型；广义线性回归模型。最后，我们使用受试者工作特征曲线和决策曲线分析评估了预测模型的准确性和稳健性。
在505例患者中，86例发生了术后肺部感染，发生率为17.03%。基于灰度共生矩阵，我们确定了14类放射组学数据用于肺部感染预测模型的变量筛选。其中，能量、对比度、平方和（SOS）、逆差（IND）、均值和（MES）、和方差（SUV）、和熵（SUE）以及熵是肝切除术后肺部感染的独立危险因素，并被列为机器学习预测模型的候选变量。随机森林模型算法结合IND、SOS、MES、SUE、SUV和熵，在训练集和内部验证集中均显示出最高的预测效率，曲线下面积分别为0.823和0.801，95%置信区间分别为0.766 - 0.880和0.744 - 0.858。其他两种类型的预测模型的预测效率在曲线下面积为0.734至0.815之间，95%置信区间分别为0.677 - 0.791和0.766 - 0.864。
肝切除术后患者的肺部感染可能与IND、SOS、MES、SUE、SUV、能量和熵等危险因素有关。本研究基于扩散加权图像的预测模型，尤其是随机森林模型算法，能够更好地预测和评估肝切除术后患者肺部感染的风险，为术后管理提供有价值的指导。
