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使用机器学习预测晚期乳腺癌骨转移中的骨相关事件

Hirotaka Miyashita¹, Ken Aoshima²

¹School of Integrative and Global Majors, University of Tsukuba, Tsukuba, Ibaraki, Japan; Division of Medical Oncology, UC San Diego Moores Cancer Center, La Jolla, CA.

Clinical breast cancer

|February 20, 2026

概括

机器学习模型可以预测骨相关事件 (SREs) 在乳腺癌患者骨转移. 关键预测因素包括表现状况,先前的SRE和性酸酶,有助于识别高风险个体.

科学领域:

在瘤学瘤学.
医疗信息学医疗信息学
机器学习机器学习

背景情况:

乳腺癌造成的骨转移显著增加了骨相关事件 (SREs) 的风险.
准确预测SREs对于及时干预和改善患者管理至关重要.
现有的预测模型可能无法充分利用机器学习的潜力来完成这个复杂的任务.

研究的目的:

开发和验证一种机器学习 (ML) 模型,用于预测乳腺癌骨转移患者的SREs.
在这个患者群体中确定与SRE风险相关的关键临床和生化特征.

主要方法:

来自一项临床试验的患者级数据的分析,该临床试验涉及从乳腺癌中发生骨转移的患者,这些患者接受了zoledronic acid.
开发使用7个算法和5个特征集的ML模型,通过Boruta方法选择特征.
模型培训和验证使用十倍交叉验证和四倍外部交叉验证,通过F1分数和SHAP值评估绩效.

主要成果:

使用Boruta选择的6个特征的随机森林模型获得了最高的性能 (F1得分为0.70).
性能状况,SRE史和血清性酸酶被确定为最重要的预测因素.
该模型有效地将患者分为高风险 (到第一个SRE的中位时间:248天) 和低风险 (到第一个SRE的中位时间:867天) 组.

结论:

机器学习模型可以准确预测骨转移性乳腺癌患者的SRE.
开发的模型确定了关键的风险因素,并根据患者经历SREs的可能性对患者进行分层.
这种预测能力可以帮助临床医生对患有骨转移的乳腺癌患者进行风险分层和个性化治疗策略.

关键词:

人工智能的人工智能是人工智能.博鲁塔方法博鲁塔方法度统计数据 (Metastatis) 是一个统计数据.放射治疗疗法放射治疗疗法莎普利的附加式解释

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