晚期卵巢癌细胞减灭术中肠道及上腹部手术学习曲线的研究进展

Research Progress on the Learning Curve of Bowel and Upper Abdominal Surgery in Cytoreductive Surgery for Advanced Ovarian Cancer

摘要: 卵巢癌是女性生殖系统中死亡率最高的恶性肿瘤，约70%的患者确诊时已为晚期。肿瘤细胞减灭术(CRS)是其核心治疗手段。晚期卵巢癌常累及肠道和上腹部，尽管指南建议由妇科肿瘤医生主导此类手术，但国内医疗责任分工和相关法律规定对其参与特定复杂手术的范围有所限制，在一定程度上影响了相关技术经验的积累。手术学习曲线反映外科医生技术水平随经验积累逐步提高的过程。国外已系统探讨了肠道及上腹部手术学习曲线的特点，但国内研究较少。评估方法如CUSUM、RA-CUSUM及RA-SPRT可量化手术时间、并发症及成功率的动态变化，为科学评估提供依据。优化患者选择、固定团队协作及多学科团队(MDT)的支持均被证明能加速学习曲线。未来应加强国际合作，结合多中心、大样本研究，分析不同地区学习曲线特征，并通过结合AI技术优化评估方法，推动政策调整以明确责任分工，助力妇科肿瘤医生团队独立完成复杂手术，提升晚期卵巢癌整体治疗水平。

Abstract: Ovarian cancer is the leading cause of cancer-related mortality in the female reproductive system, with approximately 70% of patients being diagnosed at advanced stages. Cytoreductive surgery (CRS) is the core treatment modality. Advanced ovarian cancer often involves the intestines and upper abdomen. Although guidelines recommend that gynecologic oncologists lead such surgeries, domestic medical responsibility allocation and relevant legal regulations limit their participation in specific complex surgeries, which, to some extent, hinders the accumulation of technical experience. The surgical learning curve reflects the process by which a surgeon’s technical proficiency improves with experience. While the learning curve of bowel and upper abdominal surgeries has been systematically explored internationally, research in this area is limited in China. Evaluation methods such as Cumulative Sum (CUSUM), Risk-Adjusted Cumulative Sum (RA-CUSUM), and Risk-Adjusted Sequential Probability Ratio Test (RA-SPRT) can quantify the dynamic changes in surgical time, complications, and success rates, providing a scientific basis for assessment. Optimizing patient selection, team collaboration, and the support of multidisciplinary teams (MDT) have been proven to accelerate the learning curve. Future efforts should focus on strengthening international cooperation and conducting multi-center, large-sample studies to analyze the learning curve characteristics across different regions. Additionally, integrating AI technology to optimize evaluation methods, along with policy adjustments to clarify responsibility allocation, will help gynecologic oncology teams independently perform complex surgeries, ultimately improving the overall treatment outcomes for advanced ovarian cancer.