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基于临床和灰质缩指标的可解释和直观的机器学习方法,用于预测复发性复发性多发性硬化症的残疾进展

Zichun Yan¹, Zhuowei Shi¹, Qiyuan Zhu¹

¹Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.

Academic radiology

|March 1, 2024

概括

使用临床和灰质缩指标的机器学习模型可以预测复发性复发性多发性硬化症 (RRMS) 患者的残疾进展. 这些可解释的模型有助于临床决策,以改善患者管理.

科学领域:

神经成像和机器学习
多发性硬化症研究研究

背景情况:

复发性复发性多发性硬化症 (RRMS) 的特点是不可预测的残疾进展.
对RRMS残疾进展的预测模型对于及时的临床干预和患者管理至关重要.

研究的目的:

评估临床和灰质 (GM) 缩指标在预测RRMS中残疾进展方面的有效性.
开发和提高机器学习模型的可解释性,用于预测RRMS残疾.

主要方法:

对145名具有结构性MRI和扩展残疾状态量表 (EDSS) 数据的RRMS患者的回顾性分析.
使用临床和放射学特征的六个机器学习分类器的培训和验证.
使用部分依赖图 (PDP) 分析和一个闪亮的网络应用程序来解释模型.

主要成果:

结合的临床和放射学特征产生了最好的预测性能 (AUC,0.725-0.950),后勤回归达到0.950.95的AUC.
仅使用放射学模型的模型在外部测试集中显示出相当的性能 (AUC 0.617-0.753).
PDP分析确定了进展的关键预测因素,包括女性性别,较低的GM体积/表面积,较低的SDMT得分,较高的平均曲率,年龄较大,没有疾病修饰疗法 (DMT).

结论:

结合临床和转基因缩数据的可解释机器学习模型可以有效地预测RRMS中的残疾进展.
这些预测工具可以支持医生在临床决策中,并加强患者管理策略.

关键词:

疾病进展疾病进展.灰色物质是一种灰色物质.机器学习是机器学习.多发性硬化症是多发性硬化症.

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