预测经典霍奇金淋巴瘤中第一次复发的绝对风险,包括当代治疗效果
Shahin Roshani1, Flora E van Leeuwen1, Sara Rossetti2
1Department of Epidemiology, Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX Amsterdam, The Netherlands.
Cancers
|September 13, 2025
概括
这项研究为霍奇金淋巴瘤 (HL) 患者开发了预测模型,包括治疗细节,以准确估计进展,复发或死亡 (PRD) 的风险. 这些模型改善了个性化HL治疗策略的风险评估.
科学领域:
- 血液学 血液学 血液学
- 在瘤学瘤学.
- 生物统计学 生物统计学
背景情况:
- 个性化风险预测对于优化霍奇金淋巴瘤 (HL) 治疗策略至关重要.
- 目前的模型往往没有整合治疗效果,限制了它们的精度.
研究的目的:
- 在古典HL患者中开发和验证进展,复发或死亡 (PRD) 的绝对风险的预测模型.
- 评估将治疗信息纳入PRD风险预测模型的附加值.
主要方法:
- 利用了2343名荷兰古典HL患者 (2008-2018) 的数据,并对1675名丹麦患者 (2000-2018) 进行了验证.
- 采用了Cox比例危险模型,使用时间变化的系数来预测5年绝对PRD风险.
- 包括预后因素和治疗细节 (例如,ABVD,BEACOPP,放射治疗).
主要成果:
- 在早期HL中,性别,白细胞和淋巴细胞数量是显著的预测因素. >4个循环的ABVD或ABVD加放射治疗降低了复发风险.
- 在晚期HL中,年龄,白蛋白和白细胞计数预测了PRD风险. 贝科普预测的PRD风险低于ABVD.
- 在外部验证中,结合治疗信息改善了早期阶段 (0.63到0.71,p=0.04) 和高级阶段 (0.59到0.62,p=0.33) 的5年IPCW AUC.
结论:
- 开发了精确校准的模型,并进行了合理的歧视,以预测经典HL中绝对PRD风险.
- 模型有效地整合了治疗前的预后因素和治疗效应,以改善风险预测.
- 这些模型可以帮助衡量个人HL患者的治疗益处与风险.
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