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在患上上皮质卵巢癌的患者中随机生存森林模型:基于SEER数据库和单一数据中心数据的研究

Luwei Wei¹, Guowei Chen¹, Huiying Liang¹

¹Department of Gynecology, Liuzhou Workers' Hospital Liuzhou 545001, Guangxi, China.

American journal of cancer research

|March 14, 2025

概括

这项研究开发了一种随机生存森林模型,以预测上皮卵巢癌 (EOC) 的生存率. 该模型准确地识别了关键的风险因素,帮助个性化治疗策略改善患者的治疗结果.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
流行病学流行病学

背景情况:

卵巢上皮癌 (EOC) 是一个重大的全球健康挑战.
准确的预后模型对于量身定制患者管理和提高生存率至关重要.

研究的目的:

开发和验证一个强大的预后模型,用于表皮性卵巢癌 (EOC) 的生存预测.
确定影响EOC患者结果的独立风险因素.
评估随机生存森林模型与名图相比的临床实用性.

主要方法:

来自SEER数据库的1,780名上皮卵巢癌 (EOC) 患者的临床数据的回顾性分析.
开发一个随机生存森林模型和基于已识别的预后因素的nomogram.
使用140名EOC患者的数据对预后模型进行外部验证.

主要成果:

EOC的主要独立风险因素包括晚年 (≥75岁),组织学等级差,特定的组织学类型 (清细胞,癌),晚期T和M阶段,低于最佳的手术条件和缺乏化疗.
随机生存森林模型显示出高预测准确性,AUC为1年,3年和5年的生存率达到0.848,0.859和0.890在训练组和0.992,0.795和0.883在测试组.
诺莫格拉姆模型也表现良好,在训练组中,1年,3年和5年生存率的AUC为0.789,0.803和0.838,在测试组中为0.926,0.748和0.836.

结论:

开发的随机生存森林模型是预测上皮卵巢癌 (EOC) 存活率的宝贵工具.
该模型识别高风险患者的能力使得个性化后续和治疗策略成为可能.
该模型的实施可能会提高EOC患者的长期结果.

关键词:

卵巢瘤是发生在卵巢中的.在 SEER 数据库中,我们可以看到预后预后预后随机生存森林随机生存森林

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