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预测乳腺辐射吸收剂量胸部CT检查使用机器学习技术

Sevgi Ünal¹, Remzi Gürfidan², Merve Gürsoy Bulut¹

¹Department of Radiology, Ataturk Training and Research Hospital, Izmir Katip Celebi University, Izmir 35150, Türkiye.

Tomography (Ann Arbor, Mich.)

|December 24, 2025

概括

机器学习准确地从胸部CT扫描中估计乳腺辐射剂量. 这种个性化的方法有助于监测和优化辐射暴露,提高患者的安全.

科学领域:

医疗成像医学成像
放射学放射学是一门学科.
医疗保健中的机器学习

背景情况:

乳房在胸部CT扫描期间对辐射敏感,增加癌症风险.
准确,针对患者的剂量估计对于辐射安全至关重要.
这项研究探讨了机器学习用于个性化的乳腺辐射剂量预测.

研究的目的:

用机器学习 (ML) 个性化预测方法估计胸部CT期间的乳腺辐射剂量.
开发和评估ML模型,以预测患者特定的乳腺辐射剂量.

主要方法:

追溯分析653名接受乳房镜和胸部CT的女性患者.
使用的人口和解剖学数据 (BMI,乳腺厚度) 和剂量长度产品 (DLP).
实现并比较了五种ML算法:CatBoost,梯度提升,额外树木,AdaBoost和随机森林.

主要成果:

通过粒子优化 (CatBoostPSO) 优化的CatBoost算法显示出最佳性能.
CatBoostPSO获得了最低的MSE (0.3795),MAE (0.3846) 和MAPE (4.37%),其中最高的R2 (0.9875).
组合和优化模型有效预测了乳腺辐射剂量.

结论:

一个机器学习框架为胸部CT提供了快速,准确的乳腺辐射剂量估计.
这种针对患者的方法支持个性化的辐射剂量监测和优化.
这种方法提高了临床实践中的辐射安全.

关键词:

乳腺特异性的辐射剂量胸部计算机断层扫描 (CT)机器学习是机器学习.总的 DLP 的时间.

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