系统的机器学习方法用于脑动脉瘤特征选择和破裂状态分类的分类
Lior A Kofman1, Calvin G Ludwig1, Emal Lesha1
1Department of Neurosurgery, Tufts Medical Center and Tufts University School of Medicine, Boston, MA 02111, USA.
概括
机器学习模型准确地使用形态和位置特征对内动脉瘤破裂风险进行分类. 这些模型,特别是神经网络,为改善临床评估提供了一个独立于用户的方法.
科学领域:
- 神经外科 神经外科
- 放射学 放射学是一门学科.
- 数据科学数据科学数据科学
背景情况:
- 目前的内动脉瘤破裂风险评估依赖于对形态因素的手动测量.
- 机器学习 (ML) 显示了改善动脉瘤破裂风险分层的前景.
- 现有的ML方法经常受到小样本大小或解剖学限制的影响.
研究的目的:
- 开发和验证一种计算工具,用于使用ML对内动脉瘤破裂状态进行分类.
- 评估各种ML分类器在动脉瘤的大型,可概括数据集上的性能.
- 确定导致动脉瘤破裂风险的关键特征.
主要方法:
- 从678个脑动脉瘤中利用3D血管图 (229个破裂,449个未破裂).
- 计算了21个动脉瘤特征,包括形态和位置数据.
- 训练并测试了8个分类器 (7个ML模型和1个GLM) 使用7:3分离,根据AUC,灵敏度和特异性进行评估.
主要成果:
- 在形态特征上的多个自适应回归线 (MARS) 实现了0.797.7的AUC.
- 在形态和位置特征上的神经网络 (NNET) 实现了最高的AUC 0.825.
- 关键预测指标包括面积比,波浪度指数和非球状度指数.
结论:
- 一种系统的,独立于用户的ML方法有效地分类动脉瘤破裂状态.
- 机器学习模型,特别是NNET,的表现优于传统的通用线性模型 (GLM).
- 形态特征,如非球形度指数,波浪度指数和面积比,对于风险评估至关重要.
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