The International Staging System (ISS) and the Revised International Staging System (R-ISS) are commonly used prognostic scores in multiple myeloma (MM). These methods have significant gaps, particularly among intermediate-risk groups. The aim of this study was to improve risk stratification in newly diagnosed MM patients using data from three different trials developed by the Spanish Myeloma Group. For this, we applied an unsupervised machine learning clusterization technique on a set of clinical, biochemical and cytogenetic variables, and we identified two novel clusters of patients with significantly different survival. The prognostic precision of this clusterization was superior to those of ISS and R-ISS scores, and appeared to be particularly useful to improve risk stratification among R-ISS 2 patients. Additionally, patients assigned to the low-risk cluster in the GEM05 over 65 years trial had a significant survival benefit when treated with VMP as compared with VTD. In conclusion, we describe a simple prognostic model for newly diagnosed MM whose predictions are independent of the ISS and R-ISS scores. Notably, the model is particularly useful in order to re-classify R-ISS score 2 patients in 2 different prognostic subgroups. The combination of ISS, R-ISS and unsupervised machine learning clusterization brings a promising approximation to improve MM risk stratification.
国际分期系统（ISS）与修订版国际分期系统（R-ISS）是多发性骨髓瘤（MM）常用的预后评分工具。这些方法存在明显不足，尤其在中危患者群体中。本研究旨在利用西班牙骨髓瘤小组开展的三项不同试验数据，改进新诊断多发性骨髓瘤患者的风险分层。为此，我们采用无监督机器学习聚类技术，对一系列临床、生化和细胞遗传学变量进行分析，鉴定出两个具有显著生存差异的新型患者集群。该聚类方法的预后精度优于ISS和R-ISS评分，尤其在优化R-ISS 2期患者的风险分层方面显示出独特价值。此外，在GEM05超65岁临床试验中，被划分至低风险集群的患者接受VMP方案治疗时，比接受VTD方案治疗者获得显著的生存获益。综上所述，我们构建了一个适用于新诊断多发性骨髓瘤的简易预后模型，其预测独立于ISS和R-ISS评分体系。值得注意的是，该模型能够将R-ISS评分为2期的患者重新划分为两个不同预后的亚组，具有重要临床意义。结合ISS、R-ISS评分与无监督机器学习聚类的联合策略，为完善多发性骨髓瘤风险分层提供了具有前景的新路径。
肿瘤(癌症)患者之家