造血干细胞移植治疗多发性骨髓瘤的生存风险预测。

IF 1.8 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Jose María Belmonte, Miguel Blanquer, Gregorio Bernabé, Fernando Jiménez, José Manuel García
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引用次数: 0

摘要

本文研究了生存分析(SA)技术在多发性骨髓瘤(MM)自体造血干细胞移植(aHSCT)后预后预测中的应用。通过利用六个SA模型,我们通过一致性指数(C-index)度量来检验它们的预测能力。除了评估模型的性能外,我们还使用置换和SHAP方法分析了特征的重要性,突出了关键的临床因素,如治疗史、疾病分期、既往疾病进展或复发,作为生存的关键预测因素。研究结果表明,尽管基于c指数的所有模型都表现良好,但详细的研究揭示了每个模型处理数据的方式存在差异。具体来说,Coxnet和Random Survival Forest模型更全面地使用了临床变量,而梯度增强模型似乎依赖于更窄的特征范围,这可能限制了它们区分具有可比较概况的患者的能力。风险预测将患者分为低、中、高风险级别。对于低风险患者,手术显示出积极的结果,而高风险患者预计生存益处有限,建议替代治疗。最后,我们建议未来的研究将这些模型扩展到时间到事件的估计,通过预测患者移植后的预期寿命,考虑他们移植前的临床属性,为决策提供额外的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survival risk prediction in hematopoietic stem cell transplantation for multiple myeloma.

This paper investigates the application of Survival Analysis (SA) techniques to forecast outcomes after autologous Hematopoietic Stem Cell Transplantation (aHSCT) for Multiple Myeloma (MM). By leveraging six SA models, we examine their predictive capabilities, measured through the Concordance Index (C-index) metric. Beyond evaluating model performance, we analyze feature importance using permutation and SHAP methods, highlighting key clinical factors such as treatment history, disease stage, and prior disease progression or relapse as critical predictors of survival. The findings suggest that while all models performed well based on the C-index, a detailed examination revealed variations in how each model processed data. Specifically, the Coxnet and Random Survival Forest models exhibited a more thorough use of clinical variables, whereas the gradient boosting models appeared to rely on a narrower range of features, potentially limiting their ability to differentiate between patients with comparable profiles. Risk predictions categorized patients into low, moderate, and high-risk levels. For lower-risk patients, the procedure showed positive outcomes, while higher-risk individuals were predicted to have limited survival benefits, recommending alternative treatments. Lastly, we propose future research to expand these models into time-to-event estimations, offering additional support for decision-making by predicting patient life expectancy post-transplant, considering their pre-transplant clinical attributes.

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来源期刊
Journal of Integrative Bioinformatics
Journal of Integrative Bioinformatics Medicine-Medicine (all)
CiteScore
3.10
自引率
5.30%
发文量
27
审稿时长
12 weeks
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