Published online Jul 15, 2023. doi: 10.4251/wjgo.v15.i7.1241
Peer-review started: May 1, 2023
First decision: May 11, 2023
Revised: May 14, 2023
Accepted: June 12, 2023
Article in press: June 12, 2023
Published online: July 15, 2023
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 pul
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.
Core Tip: Identifying risk factors for postoperative pulmonary infection in patients with primary hepatic carcinoma can improve the level of prevention and clinical treatment, ultimately reducing or even avoiding the occurrence of postoperative infection complications, reducing treatment time and costs, and improving patient efficacy and prognosis. The prediction model developed in our study provides valuable guidance for clinicians in predicting the risk of pulmonary infection and effectively preventing, diagnosing, and treating postoperative infection in patients with primary hepatic carcinoma, leading to an improved patient prognosis.