Computed tomography-based radiomics to predict early recurrence of hepatocellular carcinoma post-hepatectomy in patients background on cirrhosis.

BACKGROUND: The prognosis for hepatocellular carcinoma (HCC) in the presence of cirrhosis is unfavourable, primarily attributable to the high incidence of recurrence. AIM: To develop a machine learning model for predicting early recurrence (ER) of post-hepatectomy HCC in patients with cirrhosis and to stratify patients' overall survival (OS) based on the predicted risk of recurrence. METHODS: In this retrospective study, 214 HCC patients with cirrhosis who underwent curative hepatectomy were examined. Radiomics feature selection was conducted using the least absolute shrinkage and selection operator and recursive feature elimination methods. Clinical-radiologic features were selected through univariate and multivariate logistic regression analyses. Five machine learning methods were used for model comparison, aiming to identify the optimal model. The model's performance was evaluated using the receiver operating characteristic curve [area under the curve (AUC)], calibration, and decision curve analysis. Additionally, the Kaplan-Meier (K-M) curve was used to evaluate the stratification effect of the model on patient OS. RESULTS: Within this study, the most effective predictive performance for ER of post-hepatectomy HCC in the background of cirrhosis was demonstrated by a model that integrated radiomics features and clinical-radiologic features. In the training cohort, this model attained an AUC of 0.844, while in the validation cohort, it achieved a value of 0.790. The K-M curves illustrated that the combined model not only facilitated risk stratification but also exhibited significant discriminatory ability concerning patients' OS. CONCLUSION: The combined model, integrating both radiomics and clinical-radiologic characteristics, exhibited excellent performance in HCC with cirrhosis. The K-M curves assessing OS revealed statistically significant differences.

Acute murine cytomegalovirus infection boosts cell-type specific response and lipid metabolism changes in the liver of infant mice.

Introduction: Human cytomegalovirus (HCMV) infection in infants can lead to severe diseases, including neonatal hepatitis. The single-cell dimensional changes in immune cells after the initial CMV infection remain elusive, as do the effects of CMV infection on hepatic lipid metabolism. Methods: We employed single-cell RNA-sequencing to investigate the changes in liver cell types and immune responses in infant mice following murine CMV (MCMV) infection. Additionally, we examined alterations in protein expression profiles related to lipid metabolism in hepatocytes and the role of the key transcription factor PPAR-γ in hepatocytes during CMV infection. Results: Our study revealed that MCMV infects most liver cell types in infant mice, leading to an increase in the proportion of proliferating CD8 effector T cells and a subset of Nos2+ monocytes, potentially playing an essential role in early anti-viral responses. Furthermore, MCMV infection resulted in altered protein expression of lipid metabolism in hepatocytes. Knocking down the transcription factor PPAR-γ in hepatocytes effectively inhibited CMV infection. Discussion: Our findings underscore the immune system's response to early-stage MCMV infection and the subsequent impact on hepatic lipid metabolism in infant mice. This research provides new insights into the mechanisms of CMV infection and could pave the way for novel therapeutic strategies.