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使用Boruta和Lasso机器学习算法,识别与乳腺癌远程转移相关的最佳生物标志物

Jia-Ning Qin¹, Wen-Bin Dai², Wen-Hai Zhang³

¹Department of Radiation Oncology, The First Affiliated Hospital of Guangxi Medical University, No.6 Shuangyong Road, Nanning, 530000, Guangxi, China.

|August 14, 2025

概括

五个关键生物标志物,包括晚期肺癌炎症指数 (ALI) 和全身炎症反应指数 (SIRI),有效预测乳腺癌患者的远程转移风险. 这些指标有助于个性化治疗和改善结果.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
生物标志物发现发现

背景情况:

远程转移显著影响乳腺癌患者的治疗结果.
确定可靠的转移风险生物标志物对于有效的治疗策略至关重要.

研究的目的:

确定乳腺癌中远程转移的最佳营养和炎症生物标志物.
为了利用机器学习算法 (Boruta和LASSO) 进行生物标志物选择.

主要方法:

使用Boruta和LASSO算法进行初始查和生物标志物的优化.
采用多变量逻辑回归和受限立方线函数来评估风险.
使用接收器操作特征 (ROC) 曲线分析评估预测性能.

主要成果:

确定了五个关键指标:ALI,SIRI,MLR,AGR和GNRI.
增加的SIRI和MLR与增加的远程转移风险相关.
更高的ALI,AGR和GNRI与降低风险相关,AUC约为0.65.

结论:

ALI,SIRI,MLR,AGR和GNRI作为乳腺癌中远程转移风险的有效生物标志物.
这些生物标志物可以增强临床风险分层和个性化治疗计划.
将其纳入临床实践可能会改善患者的治疗结果.

关键词:

生物标志物生物标志物波鲁塔 (Boruta) 是一个波鲁塔 (Boruta)乳腺癌乳腺癌乳腺癌遥远的转移遥远的转移拉索·拉索 (Lasso) 是一个

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