Effects of short-term and perioperative antiviral therapy on prognosis after hepatectomy for hepatitis B virus-related hepatocellular carcinoma
Research Square (Research Square)(2022)
First Affiliated Hospital of Xi’an Jiaotong University
Abstract
Abstract Background: Although the benefits of antiviral therapy against hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) have been proven, researchers have not confirmed the difference in outcomes among patients who receive short-term (at least 24 weeks) or perioperative antiviral therapy after liver hepatectomy for HBV-related HCC. Methods: A retrospective study of patients who underwent hepatectomy for HBV-related HCC at the First Affiliated Hospital of Xi'an Jiaotong University from January 2016 to June 2019 was conducted. Considering the history of antiviral therapy, patients were divided into perioperative (control group, n=108) and short-term antiviral therapy groups (n=108). Results: Baseline clinical and laboratory tests and tumor characteristics were compared between the two groups. The Kaplan–Meier analysis showed a significant difference in overall survival (P<0.0001) and disease-free survival (P=0.035) between the two groups. Multivariate analysis demonstrated that short-term antiviral treatment was independently related to enhanced survival outcome in OS (hazard ratio =0.27; 95% confidence interval= 0.08–0.88, P=0.030). Conclusions: In patients with HBV-related HCC, receiving short-term antiviral therapy for at least 24 weeks resulted in enhanced outcomes.
MoreTranslated text
Key words
perioperative antiviral therapy,hepatocellular carcinoma,hepatectomy,short-term,virus-related
PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Try using models to generate summary,it takes about 60s
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Related Papers
2008
被引用219 | 浏览
2016
被引用162 | 浏览
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
去 AI 文献库 对话