PDF(Pubmed)
Abstract：
Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians\' ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow.
摘要：
脊柱转移在癌症患者中非常常见，并且其患病率有望增加。有症状的脊柱转移的手术治疗适用于缓解疼痛，保护或恢复神经功能，和机械稳定性。总体预后是治疗决策的主要驱动因素；然而，临床医生准确预测生存率的能力是有限的。在这篇叙述性评论中，我们首先讨论了用于指导脊柱转移患者治疗决策的NOMS决策框架。鉴于决策取决于预后，在过去的30年中,已经开发了多种评分系统来预测脊柱转移患者的生存率;这些系统主要是使用专家意见或回归模型开发的.尽管这些工具在我们预测预后的能力方面取得了重大进展，它们的效用受到相对缺乏患者特异性生存概率的限制.近年来已经开发了机器学习模型来缩小这一差距。与使用传统统计数据开发的模型相比，采用了更多的功能，据报道，机器学习算法可以预测30天，6周,90天,脊柱转移性疾病的1年死亡率具有出色的辨别力。这些模型经过良好的校准，并已与国内和国际独立队列进行了外部验证。尽管存在假设和认识到的局限性，机器学习方法在脊柱转移性疾病预后预测中的作用可能会增加.
