Objective:To investigate the prognostic value of 18F-FDG PET/CT-based habitat radiomics combined with stacking ensemble learning model in overall survival (OS) of patients with hepatocellular carcinoma (HCC). Methods:A total of 136 HCC patients (114 males, 22 females, age (55.3±10.4) years) who underwent 18F-FDG PET/CT before treatment between January 2018 and January 2023 were retrospectively analyzed. Eighty-five cases from Tianjin First Central Hospital and 51 cases from Tianjin Medical University Cancer Institute and Hospital were used as a training cohort and an external validation cohort, respectively. The tumor volume of interest (VOI) was delineated on PET and CT images, and a total of 4 habitats were segmented by using the Otsu algorithm, including PET high ∩ CT low, PET low ∩ CT low, PET high ∩ CT high, and PET low ∩ CT high. After the feature selection, a total of 36 stacking ensemble learning models were established, and the optimal model was selected based on the calculated concordance index (C-index). Moreover, a combined model was developed by integrating the optimal model with clinical information. The predictive efficacy of those models was assessed by time-dependent ROC curves. Results:The model based on PET high ∩ CT high habitat radiomics features with multilayer perceptron (MLP) classifier had the highest C-index (0.770) in the external validation cohort, and it was regarded as the optimal radiomics model. The combined model incorporating this model with clinical information achieved an improved C-index of 0.815 in the external validation cohort. The combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.919, 0.900, and 0.862 in predicting the 1-year, 2-year, and 3-year OS, respectively. Conclusions:18F-FDG PET/CT-based habitat analysis outperforms traditional radiomics in OS prediction for HCC patients. By integrating the optimal habitat model with the clinical model, the combined model is able to improve the predictive efficacy.

Lung transplantation is the only effective treatment for end-stage lung disease. Acute kidney injury is a common complication after lung transplantation, which is related to the occurrence of chronic kidney disease and increased postoperative fatality. The factors and mechanisms affecting the occurrence of acute kidney injury are very complex. Clinically, it has been found that various risk factors during the perioperative period of lung transplantation may lead to the occurrence of acute kidney injury, including preoperative, intraoperative and postoperative factors. Early diagnosis of acute kidney injury after lung transplantation and timely intervention are of great significance to improving patient prognosis. Therefore, this article reviews the definition of acute kidney injury, non-invasive assessment, risk factors, prognosis, and clinical management of acute kidney injury after lung transplantation, aiming to provide a reference for the diagnosis and treatment of acute kidney injury after lung transplantation in clinical practice and to improve the survival rate of lung transplant recipients.

Lung transplantation is an effective treatment for various end-stage lung diseases. Optimizing perioperative fluid management can reduce the incidence of postoperative pulmonary edema and improve the prognosis of lung transplant recipients. Excessive fluid administration may lead to pulmonary edema, ischemia-reperfusion injury of the transplant lung, and increased cardiac burden, which can induce heart failure. On the other hand, overly strict fluid restriction may lead to hypovolemia, affecting tissue perfusion and causing organ dysfunction. Therefore, precise regulation of fluid balance is crucial for the postoperative recovery of lung transplant recipients. This article reviews the physiological characteristics of lung transplant recipients, types of infused fluids, fluid therapy regimens, and hemodynamic monitoring, aiming to elucidate the particularities of perioperative fluid management in lung transplantation and provide new ideas and directions for individualized fluid management.

Objective:To investigate the prognostic value of 18F-FDG PET/CT-based habitat radiomics combined with stacking ensemble learning model in overall survival (OS) of patients with hepatocellular carcinoma (HCC). Methods:A total of 136 HCC patients (114 males, 22 females, age (55.3±10.4) years) who underwent 18F-FDG PET/CT before treatment between January 2018 and January 2023 were retrospectively analyzed. Eighty-five cases from Tianjin First Central Hospital and 51 cases from Tianjin Medical University Cancer Institute and Hospital were used as a training cohort and an external validation cohort, respectively. The tumor volume of interest (VOI) was delineated on PET and CT images, and a total of 4 habitats were segmented by using the Otsu algorithm, including PET high ∩ CT low, PET low ∩ CT low, PET high ∩ CT high, and PET low ∩ CT high. After the feature selection, a total of 36 stacking ensemble learning models were established, and the optimal model was selected based on the calculated concordance index (C-index). Moreover, a combined model was developed by integrating the optimal model with clinical information. The predictive efficacy of those models was assessed by time-dependent ROC curves. Results:The model based on PET high ∩ CT high habitat radiomics features with multilayer perceptron (MLP) classifier had the highest C-index (0.770) in the external validation cohort, and it was regarded as the optimal radiomics model. The combined model incorporating this model with clinical information achieved an improved C-index of 0.815 in the external validation cohort. The combined model outperformed the other models for OS prediction, with a time-dependent AUC of 0.919, 0.900, and 0.862 in predicting the 1-year, 2-year, and 3-year OS, respectively. Conclusions:18F-FDG PET/CT-based habitat analysis outperforms traditional radiomics in OS prediction for HCC patients. By integrating the optimal habitat model with the clinical model, the combined model is able to improve the predictive efficacy.