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机器学习模型的比较分析,用于预测乳腺癌中新辅助化疗的病理完整反应:一种MRI放射学方法

Alessia D'Anna¹, Carlo Aranzulla², Carlo Carnaghi³

¹Physics and Astronomy Department E. Majorana, University of Catania, Via S. Sofia 64, Catania 95123 Italy.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)

|February 13, 2025

概括

机器学习模型,特别是随机森林和决策树,在预测乳腺癌治疗反应时使用MRI扫描的放射学数据显示出希望. 这些模型可以帮助识别可能达到新辅助化疗病理完整反应的患者.

科学领域:

* 医学成像分析分析
* 机器学习在瘤学中的应用
* 乳腺癌研究研究

背景情况:

*对新辅助化疗 (NAC) 病理完整反应 (pCR) 的准确预测对于量身定制乳腺癌治疗至关重要.
* 动态对比增强磁共振成像 (DCE-MRI) 的放射学分析提供了一种非侵入性方法,用于提取定量特征用于预测建模.

研究的目的:

* 为了比较各种机器学习模型在预测乳腺癌患者pCR与NAC中的有效性.
* 评估来自DCE-MRI的放射学特征的性能,以预测治疗反应.

主要方法:

*分析了55名乳腺癌患者 (18名pCR,37名非pCR) 的队列.
*从DCE-MRI中提取了1446个放射学特征,并使用ComBat.Bat纠正了批量效应.
* 五个机器学习算法 (随机森林,决策树,后勤回归,k-NN,XGB) 通过离开组-退出交叉验证使用AUC,准确性,精度,回忆和F1得分等指标进行了评估.

主要成果:

*随机森林 (RF) 和决策树 (DT) 模型在预测pCR方面表现优异.
* DT 实现了 0.96 ± 0.07 的精度,RF 实现了 0.95 ± 0.05.
*RF和DT产生的曲线下面积 (AUC) 值分别为0.98±0.06和0.94±0.07,表现优于其他评估模型.

结论:

* 放射学与机器学习相结合,特别是射频和DT模型,对于预测乳腺癌中PCR到NAC的显著潜力.
*这项研究强调了这些模型在识别放射学数据中的模式以预测治疗反应方面的有效性.
* 需要对这种方法在各种数据集和临床环境中进行进一步的验证和探索.

关键词:

乳腺癌乳腺癌乳腺癌动态对比增强磁共振成像技术动态对比增强磁共振成像技术新辅助化学疗法新辅助化学疗法病理完整反应的病理完整反应.预测模型的预测模型.无线电学 (Radiomics) 是一种放射学.优化治疗的优化处理.

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