脊髓瘤与脊髓瘤的长期生存预测风险计算器:全国范围的分析分析
Abdul Karim Ghaith1,2, Xinlan Yang1,2, Abdel-Hameed Al-Mistarehi1,2
1Department of Neurosurgery, Johns Hopkins University School of Medicine, 600 N. Wolfe Street/Meyer 5-181, Baltimore, MD, 21287, USA.
Journal of neuro-oncology
|April 28, 2025
概括
脊椎脊髓瘤患者的长期存活率比冠状动脉瘤患者要好. 机器学习模型预测生存率,有助于为这些罕见的脊髓瘤提供个性化治疗.
科学领域:
- 在瘤学瘤学.
- 生物统计学 生物统计学
- 医疗信息学 医疗信息学
背景情况:
- 脊髓瘤和脊髓瘤是罕见的,具有攻击性的脊髓骨瘤.
- 这些瘤对常规化疗和辐射有抗性,这给治疗带来了挑战.
研究的目的:
- 比较脊髓瘤和脊髓瘤的临床特征,治疗策略和结果.
- 开发一种机器学习模型,用于个性化的生存预测.
主要方法:
- 来自国家癌症数据库 (NCDB) (2004-2017) 3175名患者的回顾性分析.
- 卡普兰-梅尔和加权日志等级分析的整体存活 (OS).
- 训练了12个机器学习/深度学习模型来预测10年的操作系统,使用AUC,Brier分数和C指数进行评估.
主要成果:
- 冠状瘤患者年龄较大,瘤较小,在呈现时转移较少.
- 冠状瘤患者的10年生存率优于其他患者 (p < 0.0001).
- 机器学习模型,特别是DeepSurv和集合方法,显示出强大的预测性能 (AUC>0.80,C指数=0.83).
结论:
- 脊柱性脊瘤和脊柱性脊瘤表现出不同的临床特征和结果,脊髓性脊瘤的生存率更好.
- 总体切除 (GTR) 和个性化放射治疗对于优化结果至关重要.
- 基于机器学习的预测模型为个性化治疗策略提供了有价值的工具,需要外部验证.
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