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
Primary hepatic carcinoma (PHC) is a widespread malignant tumor with high incidence and mortality rates that poses a serious threat to human health worldwide. Surgical treatment remains the most effective treatment option for PHC. However, postoperative infections, including surgical site and pulmonary infections, are among the main complications following surgery.
To extract the texture features of radiomics of patients with PHC using a gray-level co-occurrence matrix to develop a predictive model to aid doctors in clinical decision-making and medical resource allocation for early interventions and treatments.
To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.
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 (RFM); 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.
The RFM algorithm, in combination with sum of squares, inverse difference, mean sum, sum variance, sum entropy, 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 95% confidence intervals of 0.766-0.880 and 0.744-0.858, respectively. The artificial neural network model and generalized linear regression model had prediction efficiency 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 sum of squares, inverse difference, mean sum, sum variance, sum entropy, energy, and entropy. The RFM prediction model in this study based on diffusion-weighted images can better predict and estimate the risk of pulmonary infection in patients undergoing hepatectomy, providing valuable guidance for postoperative management.
Identifying risk factors for postoperative pulmonary infection in patients with PHC 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 PHC, leading to an improved patient prognosis.