Retrospective Cohort Study
Copyright ©The Author(s) 2023. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Gastrointest Oncol. Jul 15, 2023; 15(7): 1241-1252
Published online Jul 15, 2023. doi: 10.4251/wjgo.v15.i7.1241
Development and validation of a postoperative pulmonary infection prediction model for patients with primary hepatic carcinoma
Chao Lu, Zhi-Xiang Xing, Xi-Gang Xia, Zhi-Da Long, Bo Chen, Peng Zhou, Rui Wang
Chao Lu, Zhi-Xiang Xing, Xi-Gang Xia, Zhi-Da Long, Bo Chen, Peng Zhou, Rui Wang, Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, Jingzhou 434020, Hubei Province, China
Author contributions: Lu C and Xing ZX contributed equally to this research; Wang R was the guarantor and designed the study; Lu C, Xing ZX, Xia XG, Long ZD, Chen B, and Zhou P participated in the acquisition, analysis, and interpretation of the data and drafted the initial manuscript; Lu C, Xing ZX, and Wang R revised the article critically for important intellectual content.
Institutional review board statement: The study was reviewed and approved by the Institutional Review Board of Jingzhou Hospital (Approval No. 2023-JH019).
Informed consent statement: All personal information of the patients was encrypted to prevent leakage and exempted from informed consent by the above ethics committee.
Conflict-of-interest statement: The authors declare that they have no conflicts of interest.
Data sharing statement: No additional data are available.
STROBE statement: The authors have read the STROBE Statement—checklist of items, and the manuscript was prepared and revised according to the STROBE Statement—checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Rui Wang, MD, Surgical Oncologist, Department of Hepatobiliary & Pancreaticospleen Surgery, Yangtze University, Jing Zhou hospital, No. 26 Chuyuan Road, Jingzhou District, Jingzhou 434020, Hubei Province, China. wangrui_20222022@163.com
Received: May 1, 2023
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
Processing time: 71 Days and 22.8 Hours
ARTICLE HIGHLIGHTS
Research background

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.

Research motivation

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.

Research objectives

To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management.

Research methods

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.

Research results

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.

Research conclusions

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.

Research perspectives

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.