胆囊癌复发后的复发模式和生存预测胆囊癌复发后的生存预测
Giovanni Catalano1, Laura Alaimo2, Odysseas P Chatzipanagiotou3
1Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, United States; Division of General and Hepatobiliary Surgery, University of Verona, Verona, Italy.
概括
胆囊癌复发模式显著影响存活率. 机器学习模型可以预测复发后的生存率 (SAR),有助于识别潜在的再治疗患者.
科学领域:
- 在瘤学瘤学.
- 机器学习在医学中的应用
- 癌症预后 癌症预后
背景情况:
- 胆囊癌 (GBC) 预后不佳,复发模式及其对生存的影响尚不清楚.
- 了解这些模式对于改善患者治疗结果至关重要.
研究的目的:
- 分析胆囊癌治疗后切除的复发模式.
- 开发和验证一种机器学习模型,用于预测GBC患者复发后的生存率 (SAR).
主要方法:
- 利用了348名接受治疗意图GBC切除 (1999-2022) 的患者的国际数据库.
- 开发并验证了一个极端梯度提升机器学习模型来预测SAR.
主要成果:
- 31.6%的患者经历了复发,局部复发是最常见的 (29.1%).
- 复发后的存活时间因部位而异:肺复发最长 (36.0个月),腹腔复发 (8.9个月) 和肝复发 (8.5个月) 最短.
- 该ML模型显示出良好的预测性能 (AUC71.4%),主要预测因素包括ASA分类,局部复发,辅助化疗,AJCC阶段和早期复发 (<12个月).
结论:
- 在GBC复发后的生存率通常很差,除了肺复发.
- 一部分患者可能表现出不那么积极的疾病生物学,导致有利的SAR.
- 基于机器学习的SAR预测可以帮助识别治疗性再切割的候选人.
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