Since the mid-1980s, there has been little progress in improving survival of patients diagnosed with osteosarcoma. Survival prediction models play a key role in clinical decision-making, guiding healthcare professionals in tailoring treatment strategies based on individual patient risks. The increasing interest of the medical community in using machine learning (ML) for predicting survival has sparked an ongoing debate on the value of ML techniques versus more traditional statistical modelling (SM) approaches. This study investigates the use of SM versus ML methods in predicting overall survival (OS) using osteosarcoma data from the EURAMOS-1 clinical trial (NCT00134030). The well-established Cox proportional hazard model is compared to the extended Cox model that includes time-varying effects, and to the ML methods random survival forests and survival neural networks. The impact of eight variables on OS predictions is explored. Results are compared on different model performance metrics, variable importance, and patient-specific predictions. The article provides comprehensive insights to aid healthcare researchers in evaluating diverse survival prediction models for low-dimensional clinical data.
自20世纪80年代中期以来，骨肉瘤确诊患者的生存率改善进展甚微。生存预测模型在临床决策中发挥着关键作用，可指导医疗专业人员根据患者个体风险制定个性化治疗策略。医学界对利用机器学习（ML）预测生存率的兴趣日益增长，引发了关于ML技术与传统统计建模（SM）方法价值的持续讨论。本研究基于EURAMOS-1临床试验（NCT00134030）的骨肉瘤数据，探讨SM与ML方法在预测总生存期（OS）中的应用。研究将成熟的Cox比例风险模型与包含时变效应的扩展Cox模型，以及随机生存森林和生存神经网络两种ML方法进行比较。探讨了八个变量对OS预测的影响，并从模型性能指标、变量重要性和患者特异性预测三个维度对比结果。本文为医疗研究者评估低维临床数据的多样化生存预测模型提供了全面参考。
肿瘤(癌症)患者之家