预测细胞癌脊髓转移中的生存结果:对现有的预后系统的多中心评估
Zhehuang Li1, Feng Wei2, Jinxin Hu3
1Department of Musculoskeletal Oncology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, Zhengzhou, China.
概括
对于细胞癌脊髓转移 (RCC-SM) 的现有生存预测模型显示了可变的准确性. 骨瘤学研究小组 (SORG) 的机器学习 (ML) 模型和修订的卡塔吉里得分分别在短期和长期生存方面表现最好,但需要重新校准.
科学领域:
- 在瘤学瘤学.
- 脊柱外科手术 脊柱外科手术
- 生物统计学 生物统计学
背景情况:
- 生存预测模型对于管理脊髓转移患者至关重要.
- 细胞癌脊髓转移 (RCC-SM) 带来了独特的临床挑战.
- 准确的预后有助于优化RCC-SM的治疗策略.
研究的目的:
- 为了外部验证RCC-SM生存的已建立的预后评分系统.
- 用多中心的中国数据评估六个评分系统的性能.
主要方法:
- 来自中国三个中心的103名RCC-SM患者的后续外部验证 (2015-2023年).
- 对六个预后系统的评估:托米塔,修订的托库哈希,修订的卡塔吉里,NE Sms,SORG 诺米克和SORG ML 模型.
- 通过AUC,校准图表和Brier分数评估的生存结果 (90天,180天,1年).
主要成果:
- 在SORG ML模型中,90天存活率的歧视率最高 (AUC:0.765).
- 修订后的Tokuhashi和修订后的Katagiri在180天 (AUC:0.754) 和1年 (AUC:0.806) 的存活率方面表现最好.
- 大多数模型低估了生存率,特别是在高危人群中;确定了关键的预后因素.
结论:
- 现有的RCC-SM预后模型具有不同的预测能力.
- SORG ML和修订的卡塔吉里模型提供了最好的短期和长期预测,但需要对东亚人口进行重新校准.
- 未来的模型应该整合动态的治疗反应和生物标志物,以提高准确性.
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