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通过基于机器学习的Cox比例危险模型预测肺结核瘤的预后框架
Haixin Chen1, Yanyan Xu1, Haowen Lin1
1The First Clinical College, Guangdong Medical University, Zhanjiang, Guangdong, 524023, China.
Journal of cancer research and clinical oncology
|July 25, 2024
概括
一种新的名图准确地预测了肺结网环细胞癌 (LSRCC) 患者的生存率,其表现优于传统模型. 手术干预对LSRCC的整体存活率和癌症特异性存活率产生了积极的影响.
科学领域:
- 在瘤学瘤学.
- 医学统计 医学统计
- 癌症研究 癌症研究
背景情况:
- 标签环细胞癌 (SRCC) 是一种罕见的肺癌亚型.
- 现有的肺癌存活率名录对于SRCC的预测是不够的.
- 对于SRCC而言,一个特定的诺米克图对于准确的预后评估至关重要.
研究的目的:
- 开发和验证一种用于预测肺结网环细胞癌 (LSRCC) 患者整体存活率 (OS) 的新型名谱.
- 确定影响LSRCC患者存活率的关键预后因素.
- 评估开发的诺莫图的临床实用性和准确性.
主要方法:
- 利用监测,流行病学和最终结果 (SEER) 数据库来获取患者数据.
- 采用单变量和多变量考克斯回归和随机森林分析.
- 开发了一种预测1年,3年和5年的操作系统的nomogram,使用ROC曲线,校准曲线和决策曲线分析 (DCA) 进行验证.
主要成果:
- 八个因素,包括年龄,主要部位,T阶段,N阶段,M阶段,手术,化疗和辐射,具有重要意义.
- 在培训和验证组中,构建的诺姆图表表显示出高预测准确度和强大的预后能力.
- 手术干预对所有瘤阶段的整体存活率 (OS) 和癌症特异性存活率 (CSS) 有积极影响.
结论:
- 开发的诺米图谱在临床上有价值,用于预测LSRCC患者的预后.
- 手术对LSRCC患者是有效的,无论瘤的阶段如何.
- 与机器学习模型相比,考克斯的比例危险 (CPH) 模型表现出更高的有效性.
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