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基于决策树的机器学习算法用于预测急性辐射食道炎.

Mostafa Alizade-Harakiyan¹, Amin Khodaei², Ali Yousefi³

¹Department of Radiation Oncology, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.

Biochemistry and biophysics reports

|April 15, 2025

概括

一个新的决策树模型准确地预测了癌症患者的辐射诱导食道炎. 该工具有助于优化放射治疗和个性化患者风险评估,以获得更好的结果.

关键词:

决策树分类器决定树分类器机器学习是机器学习.预测建模的预测建模.辐射引起的食道炎.辐射疗法辐射疗法治疗计划治疗计划

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科学领域:

辐射瘤学辐射瘤学
医疗信息学医疗信息学
临床决策支持临床决策支持

背景情况:

辐射诱导的食道炎是胸部和部癌症治疗中的一个重大挑战.
它会对患者的生活质量产生负面影响,并可能限制治疗疗效.
预测和管理食道炎对于有效的化学放射治疗至关重要.

研究的目的:

开发和验证基于决策树的模型,用于预测急性食道炎等级.
为了确定辐射诱导食道炎的关键临床和剂量测量预测因素.
为放射治疗规划提供个性化风险评估工具.

主要方法:

分析了100名接受胸部和部放射治疗的患者的数据.
利用了33个特征,包括人口,临床和剂量参数.
实现了对二进制 (等级≥2与<2) 和多类 (等级1,2,3) 分类的决策树分类器.

主要成果:

二元分类模型在预测食道炎方面实现了97%的准确性.
多类模型在预测特定的食道炎等级方面表现出98%的准确性.
确定的主要预测因素包括V40,V60 (分别接收40 Gy和60 Gy的体积) 和平均食道剂量.

结论:

决策树模型准确地预测了辐射诱导的食道炎等级,具有很高的解释性.
这种方法促进了治疗优化和个性化风险评估在放射瘤学.
该模型作为临床决策支持的有希望的工具,增强放射治疗规划.