用不同的机器学习方法对乳腺癌放射治疗后毒性预测模型的性能进行比较
Maria Giulia Ubeira-Gabellini1, Martina Mori1, Gabriele Palazzo1
1Medical Physics, IRCCS San Raffaele Scientific Institute, 20132 Milan, Italy.
Cancers
|March 13, 2024
概括
评估了机器学习模型,以预测放射治疗 (RT) 毒性. 虽然像LightGBM这样的复杂模型显示出稍微更好的性能,但具有更少功能的简单模型取得了可比的结果.
科学领域:
- 放射治疗研究的研究.
- 机器学习在瘤学中的应用.
- 临床结果预测预测.
背景情况:
- 在放射治疗 (RT) 中预测急性毒性对于患者管理至关重要.
- 机器学习 (ML) 提供了提高毒性预测准确性的潜力.
- 对大型队列进行各种ML模型的评估是必要的,以确定最佳的预测策略.
研究的目的:
- 为了比较不同的ML模型在预测RTOG等级2/3急性毒性的性能.
- 确定最有效的ML模型和有关毒性预测的相关特征.
- 评估复杂的ML模型是否与更简单的模型相比提供更好的预测性能.
主要方法:
- 一组1314名接受RT的患者被分析了急性毒性 (204次事件).
- 使用了25个临床,解剖和剂量测量特征,并应用了特征选择.
- 评估了12种ML方法,采用模型优化和顺序倒向选择;使用了数据平衡技术.
主要成果:
- 在评估的ML模型中,LightGBM表现最好.
- 使用仅三个特征 (LR3) 的物流回归实现了与更复杂模型相比的性能.
- 在测试数据上的最佳模型的曲线下面积 (AUC) 约为0.66.
结论:
- 没有单一的ML模型在预测RT毒性的所有性能指标上都表现出色.
- 更复杂的ML模型通常显示出更好的性能,但具有更少功能的简单模型具有竞争力.
- 这些发现表明,节的模型可以实现强大的预测性能,平衡复杂性和准确性.
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