对眼睛黑色素瘤进行预后性名录的开发:基于SEER人口的研究 (2000-2021)
Miyun Zheng1,2, Maodong Xu1,2, Mengxing You2,3
1Department of Ophthalmology, The First Hospital of Putian City, Putian, China.
Frontiers in medicine
|February 13, 2025
概括
这项研究使用机器学习开发了眼睛黑色素瘤 (OM) 的预后名录. 诺米图准确地预测了生存率,有助于为这种罕见的癌症做出临床决策.
科学领域:
- 眼科医生 眼科 眼科
- 在瘤学瘤学.
- 医疗信息学 医疗信息学
背景情况:
- 眼睛黑色素瘤 (OM) 是一种罕见但具有显著死亡率的侵袭性癌症.
- 准确的预后工具对于有效的临床决策和OM患者管理至关重要.
研究的目的:
- 开发和验证眼睛黑色素瘤 (OM) 的预后名录,以改善生存预测.
- 确定OM患者总生存期 (OS) 的关键独立预后变量.
主要方法:
- 使用单变量和多变量COX比例危险回归来确定预后变量.
- 开发了一个包含13个显著临床病理因素的名图.
- 通过使用ROC曲线,校准图,DCA和10倍交叉验证来评估nomogram性能,并与机器学习模型进行比较.
主要成果:
- 确定了OS的13个独立预后因素,包括年龄,瘤特征和转移.
- 诺米图实现了0.712的一致性指数和0.749 (3年),0.734 (5年) 和0.730 (10年) 的AUC.
- 校准图表显示了密切的一致性,DCA显示了优异的净收益,机器学习模型实现了0.750.0的AUC.
结论:
- 成功开发了一种集成13个OM预后临床病理学变量的强大的名图.
- 诺米图表显示了生存结果的强大预测性能,通过多种统计方法和机器学习进行验证.
- 这种工具可以提高预后预测,并支持眼睛黑色素瘤患者的临床决策.
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