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机器学习用于放射治疗后二次癌症的早期预测

Hossein Sadeghi¹, Fatemeh Seif², Soraya Khanmohammadi³

¹Department of Physics, Faculty of Sciences, Arak University, Arak, 38156-8-8349, Iran. H-Sadeghi@araku.ac.ir.

Scientific reports

|October 24, 2025

概括

机器学习使用临床,基因组和剂量数据准确预测放射治疗幸存者的二次癌症. 辐射剂量和患者年龄是癌症患者早期干预的关键因素.

科学领域:

在瘤学瘤学.
放射疗法是一种放射治疗.
机器学习机器学习

背景情况:

二次性癌症 (SCs) 对接受放射治疗 (RT) 的癌症幸存者来说是一个重要的长期风险.
对于早期干预和个性化风险评估,需要准确的预测模型.

研究的目的:

开发和验证用于预测SC发病率的机器学习 (ML) 模型.
整合多样化的患者数据,包括临床,病理和基因组特征.

主要方法:

一个随机森林 (RF) 回归模型是使用1,240名患者的数据开发的.
关键特征包括辐射剂量,暴露时的年龄,组织学和特定基因突变 (例如TP53,BRCA1/2).

主要成果:

射频模型表现出高性能 (MSE = 0.002,R平方 = 0.98).
辐射剂量 (基尼值 = 0.42) 和暴露年龄 (基尼值 = 0.38) 是最有影响力的预测因素.
预测的发病率与流行病学数据一致,例如,乳腺至肺的SCs为15.2/10,000.

结论:

开发的ML模型显示了在个别患者中准确预测SC风险的巨大潜力.
这种工具可以帮助临床环境在癌症幸存者早期检测和个性化风险管理.
强调了ML在个性化瘤学中的实用性,同时建议对高度优化的指标保持谨慎.

关键词:

早期预测预测的早期预测机器学习是机器学习.随机森林回归器随机森林回归器二次性癌症二次性癌症

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