肝囊肿复发机制及术后影像监测路径优化 研究进展

Advances in the Mechanisms of Hepatic Cyst Recurrence and Optimization of Postoperative Imaging Surveillance Pathways

摘要: 目的：本文旨在系统总结肝囊肿复发的主要病理生理机制，评估术后影像监测路径的优化方向，并探讨人工智能(AI)在术后影像随访与复发早期识别中的潜在应用价值。方法：通过检索近10年(2015~2025) PubMed、Scopus及CNKI数据库相关文献，对肝囊肿复发机制、术后影像监测策略及AI辅助诊断研究进行系统性归纳分析，并结合最新的《肝囊肿临床实践指南》进行循证整合。结果：囊壁未彻底切除及上皮细胞再分泌是复发的核心机制；胆道交通和感染可加重液体积聚。MRI优于CT在识别囊壁变化和复发征象方面的敏感性。对于高危患者(如黏液性囊性肿瘤、多囊肝病)，应在术后6~12个月行MRI随访。AI影像分析可自动识别囊壁变化、预测复发风险，准确率超过90%，在术后监测中显示出临床应用潜力。结论：肝囊肿复发由多因素共同作用，应结合病理类型和风险分层制定个体化影像随访方案。AI技术的引入为复发早期预警与精准随访提供了新方向。未来研究应加强复发预测模型和智能监测体系的临床验证。

Abstract: Objective: This study aimed to systematically summarize the major pathophysiological mechanisms of hepatic cyst recurrence, evaluate the optimization of postoperative imaging surveillance pathways, and explore the potential role of artificial intelligence (AI) in postoperative follow-up and early recurrence detection. Methods: Literature published between 2015 and 2025 was retrieved from PubMed, Scopus, and CNKI databases. Studies focusing on hepatic cyst recurrence mechanisms, imaging monitoring strategies, and AI-assisted diagnostics were comprehensively reviewed and integrated with the latest Clinical Practice Guidelines for Hepatic Cysts. Results: Incomplete cyst wall removal and epithelial fluid re-secretion are the main causes of recurrence, while biliary communication and infection further promote fluid accumulation. MRI demonstrates superior sensitivity to CT in detecting cyst wall changes and recurrence signs. For high-risk patients (e.g., mucinous cystic neoplasms, polycystic liver disease), MRI follow-up at 6~12 months postoperatively is recommended. AI-based imaging analysis can automatically identify cyst wall alterations and predict recurrence risk with over 90% accuracy, showing strong clinical potential for postoperative monitoring. Conclusion: Hepatic cyst recurrence results from multiple interacting factors. Individualized imaging follow-up plans based on pathology and risk stratification are essential. The integration of AI offers promising prospects for early recurrence warning and precision follow-up. Future research should focus on developing predictive models and validating intelligent monitoring systems in clinical practice.