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基于机器学习算法和细胞因子,开发和验证乳腺癌肺转移的诺莫格拉姆模型

Zhaoyi Li¹, Hao Miao², Wei Bao³

¹Department of Radiotherapy, The Second Affiliated Hospital of Xuzhou Medical University, Meijian Road 32, Xuzhou, 221000, China.

|April 14, 2025

概括

这项研究开发了一种机器学习模型,使用细胞因子预测乳腺癌肺转移 (BCLM) 风险. 诺米图有效地识别高风险患者,帮助临床决策.

科学领域:

在瘤学瘤学.
生物统计学生物统计学
计算生物学计算生物学

背景情况:

精确识别乳腺癌肺转移 (BCLM) 是一个挑战,需要改进风险预测模型.
细胞因子在乳腺癌 (BC) 转移到肺部中的作用需要进一步阐明.

研究的目的:

使用机器学习算法识别BCLM的关键风险因素.
根据已识别的因素,包括细胞因子,开发可靠的诺米克图来预测BCLM风险.

主要方法:

一项对326名BC患者的回顾性研究,分为培训 (70%) 和验证 (30%) 队列.
机器学习模型 (LASSO,XGBoost,RF) 用于识别风险因素.
构建了一个名图,并对歧视,校准和临床实用性进行验证.

主要成果:

开发了一种包含内分泌疗法,hsCRP,IL6,IFN-ɑ和TNF-ɑ的诺莫图,以预测5年和10年的BCLM风险.
该模型表现出强大的区分能力和预测和观察结果之间的良好一致性.
决策曲线分析证实了名图的临床实用性.

结论:

机器学习衍生名录有效地识别了患有肺转移风险较高的BC患者.
该工具为BCLM管理中的临床决策提供了宝贵的支持.

关键词:

乳腺癌乳腺癌乳腺癌细胞因子细胞因子机器学习机器学习转移转移转移转移转移这个名字叫做Nomogram.

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