乌罗预测:基于真实数据的机器学习模型,用于预测癌复发 (UroCCR-120)
Gaëlle Margue1,2, Loïc Ferrer3, Guillaume Etchepare3
1Bordeaux University Hospital, Urology department, Bordeaux, France. gaelle.margue@chu-bordeaux.fr.
NPJ precision oncology
|February 23, 2024
概括
机器学习模型准确地预测了手术后细胞癌 (RCC) 患者的无疾病生存率. 这种方法改进了癌护理中个性化随访和治疗决策的传统得分.
科学领域:
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
- 生物统计学 生物统计学
背景情况:
- 细胞癌 (RCC) 通常在局部阶段被诊断出来,手术作为主要治疗方法.
- 目前对RCC的预后得分具有中等的预测准确性,使手术后的随访和辅助疗法选择复杂化.
- 准确预测无病生存期 (DFS) 对于优化RCC手术后患者管理至关重要.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,用于预测手术治疗RCC患者的个体术后DFS.
- 将开发的ML模型的性能与已建立的预后得分进行比较.
- 根据预测的DFS,将患者分为不同的预后组.
主要方法:
- 利用来自法国癌研究网络数据库 (UroCCR) 的现实世界前性数据.
- 分析了3372名经过手术治疗的RCC患者的队列,随机分配了训练或测试组的地点.
- 训练并评估了各种ML模型,包括Cox比例危险 (PH) 模型与24个变量,以预测DFS.
主要成果:
- 最好的ML模型,一个考克斯PH模型与24个变量,实现了0.81 (95% CI 0.77-0.85) 的IAUC的DFS预测.
- 与传统的风险评分相比,ML模型显示出优异的预测性能.
- 该模型有效地处理了不完整的预测数据,并将患者分层分为四个分辨良好的预后组 (iAUC = 0.79; 95% CI 0.74-0.83).
结论:
- 机器学习应用于现实世界的前性数据,为本地化或本地先进的RCC提供准确的个人DFS预测.
- 开发的ML模型在预测癌患者手术后的结果方面优于传统的预后得分.
- 这种方法支持个性化的后续策略和关于RCC幸存者的辅助治疗的知情决策.
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