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BACKGROUND: 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. AIM: To identify the risk factors for postoperative pulmonary infection in patients with PHC and develop a prediction model to aid in postoperative management. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
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BACKGROUND: Postoperative pancreatic fistula (PF) is a serious life-threatening complication after pancreaticoduodenectomy (PD). Our research aimed to develop a machine learning (ML)-aided model for PF risk stratification. AIM: To develop an ML-aided model for PF risk stratification. METHODS: We retrospectively collected 618 patients who underwent PD from two tertiary medical centers between January 2012 and August 2021. We used an ML algorithm to build predictive models, and subject prediction index, that is, decision curve analysis, area under operating characteristic curve (AUC) and clinical impact curve to assess the predictive efficiency of each model. RESULTS: A total of 29 variables were used to build the ML predictive model. Among them, the best predictive model was random forest classifier (RFC), the AUC was [0.897, 95% confidence interval (CI): 0.370-1.424], while the AUC of the artificial neural network, eXtreme gradient boosting, support vector machine, and decision tree were between 0.726 (95%CI: 0.191-1.261) and 0.882 (95%CI: 0.321-1.443). CONCLUSION: Fluctuating serological inflammatory markers and prognostic nutritional index can be used to predict postoperative PF.
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OBJECTIVE: To investigate the effects of lipiodol-hydroxyapatite nanoparticle (lipi-nHAP) on the growth, necrosis, apoptosis, proliferation, and angiogenesis of hepatic tumor. METHODS: Ultrasound-emulsification was used to make lipi-nHAP Eighty New Zealand white rabbits underwent implantation of carcinoma cells of the line VX2 into the left lobe of liver. Two weeks later the rabbits underwent catheterization into the gastroduodenal artery so that, and then the rabbits were randomly divided into four equal groups to receive infusion via the hepatic artery of different drugs: physiological saline (Group A), lipiodol (Group B), adriamycin + lipiodol (Group C), and lipi-nHAP (Group D). Seven and 14 days after the treatment the size of tumor was observed by spiral CT scan, and the volume and growth rate of tumor were calculated. Two weeks after the treatment 8 rabbits from each group were killed and their liver tumors were taken out and the survival rates of remaining rabbits were observed. The necrosis rate of the liver tumor was assessed by measuring the area of the tumor and the necrosis. The apoptotic rate was examined by TUNEL method. Mcrovessel density (MVD) was examined by immunohistochemistry anti-CD31 antibody. Anti-proliferating cell nuclear antigen (PCNA) monoclonal antibody was used to detect the expression of PCNA so as to calculate the proliferation index of the cells. RESULTS: The tumor volume and growth rate of Group D 7 and 14 days after treatment were both significantly lower than those of other groups (all P < 0.05) and the necrosis rate and apoptotic index of Group D were both significantly higher than those of other groups (all P < 0.05). The values of MVD were higher in Groups C and D compared with those of Group A. Compared with those in other groups, the values of MVD and expression level of PCNA were significantly lower in group D (all P < 0.05). The survival time of Group D was longer than those of other groups (all P < 0.05). CONCLUSION: lipi-nHAP can suppress the growth of tumor, increase the tumor's necrosis rate and apoptotic index, inhibit the development of neovascularization, decrease the expression level of PCNA of residual tumor, and prolong the surviving time of the animals with hepatic tumor. It may become an effective embolization material to treat liver cancer.