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) 和修订的国际分期系统 (R-ISS) 是多发性骨髓瘤 (MM) 常用的预后评分。这些方法有很大的差距，特别是在中等风险人群中。本研究的目的是利用西班牙骨髓瘤小组开发的三项不同试验的数据来改善新诊断的 MM 患者的风险分层。为此，我们在一组临床、生化和细胞遗传学变量上应用了无监督机器学习聚类技术，并确定了两个存活率显着不同的新型患者集群。这种聚类的预后精度优于 ISS 和 R-ISS 评分，并且似乎对改善 R-ISS 2 患者的风险分层特别有用。此外，与 VTD 相比，在 GEM05 65 年试验中分配到低风险组的患者在接受 VMP 治疗时具有显着的生存获益。总之，我们为新诊断的 MM 描述了一个简单的预后模型，其预测独立于 ISS 和 R-ISS 评分。值得注意的是，该模型对于将 R-ISS 评分为 2 的患者重新分类为 2 个不同的预后亚组特别有用。ISS、R-ISS 和无监督机器学习聚类的结合为改善 MM 风险分层带来了有希望的近似值。该模型对于将 R-ISS 评分为 2 的患者重新分类到 2 个不同的预后亚组中特别有用。ISS、R-ISS 和无监督机器学习聚类的结合为改善 MM 风险分层带来了有希望的近似值。该模型对于将 R-ISS 评分为 2 的患者重新分类到 2 个不同的预后亚组中特别有用。ISS、R-ISS 和无监督机器学习聚类的结合为改善 MM 风险分层带来了有希望的近似值。