预测软组织肉瘤患者的五年死亡率
Teja Yeramosu1, Waleed Ahmad1, Azhar Bashir2
1School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA.
The bone & joint journal
|May 31, 2023
概括
瘤大小,M阶段和组织学亚型是软组织肉瘤 (STS) 患者五年癌症相关死亡率的关键预测因素. 机器学习模型可以准确预测存活率,帮助肢体和干部STS的治疗决策.
科学领域:
- 在瘤学瘤学.
- 机器学习在医学中的应用
- 生物统计学 生物统计学
背景情况:
- 软组织肉瘤 (STS) 是一种罕见的恶性瘤,预后可变.
- 确定预测癌症相关死亡率的因素对于患者管理至关重要.
- 现有的预测模型可能无法完全捕捉到STS结果的复杂性.
研究的目的:
- 确定与四肢和干部STS五年癌症相关死亡率相关的关键因素.
- 开发和验证用于预测这种死亡率的机器学习 (ML) 算法.
- 为改善风险分层和治疗规划提供工具.
主要方法:
- 从SEER数据库 (2004-2017) 中分析人口统计,临床病理和治疗数据.
- 多变量后勤回归用于识别显著的死亡预测因素.
- 使用AUC,校准和决策曲线分析开发和比较各种ML模型 (例如随机森林).
- 在机构数据集上对表现最好的模型进行外部验证.
主要成果:
- 分析了13,646名STS患者;35.9%的患者经历了五年癌症相关死亡率.
- 随机森林模型表现出卓越的性能.
- 瘤大小是最重要的预测因素,其次是M阶段,组织学亚型,年龄和外科切除.
- 通过外部验证,AUC达到0.752.
结论:
- 确定了用于预测STS死亡率的临床重要变量.
- 一个经过验证的ML模型为癌症相关死亡率提供了良好的准确性和可预测性.
- 这些发现可以帮助骨科瘤学家对患者进行风险分层并优化治疗策略.
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