Abstract：
BACKGROUND: Robust predictive models of clinical impairment and worsening in multiple sclerosis (MS) are needed to identify patients at risk and optimize treatment strategies.
OBJECTIVE: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal.
METHODS: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance.
RESULTS: The ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ -1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008).
CONCLUSIONS: ML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need.
OBJECTIVE: To evaluate whether machine learning (ML) methods can classify clinical impairment and predict worsening in people with MS (pwMS) and, if so, which combination of clinical and magnetic resonance imaging (MRI) features and ML algorithm is optimal.
METHODS: We used baseline clinical and structural MRI data from two MS cohorts (Berlin: n = 125, Amsterdam: n = 330) to evaluate the capability of five ML models in classifying clinical impairment at baseline and predicting future clinical worsening over a follow-up of 2 and 5 years. Clinical worsening was defined by increases in the Expanded Disability Status Scale (EDSS), Timed 25-Foot Walk Test (T25FW), 9-Hole Peg Test (9HPT), or Symbol Digit Modalities Test (SDMT). Different combinations of clinical and volumetric MRI measures were systematically assessed in predicting clinical outcomes. ML models were evaluated using Monte Carlo cross-validation, area under the curve (AUC), and permutation testing to assess significance.
RESULTS: The ML models significantly determined clinical impairment at baseline for the Amsterdam cohort, but did not reach significance for predicting clinical worsening over a follow-up of 2 and 5 years. High disability (EDSS ≥ 4) was best determined by a support vector machine (SVM) classifier using clinical and global MRI volumes (AUC = 0.83 ± 0.07, p = 0.015). Impaired cognition (SDMT Z-score ≤ -1.5) was best determined by a SVM using regional MRI volumes (thalamus, ventricles, lesions, and hippocampus), reaching an AUC of 0.73 ± 0.04 (p = 0.008).
CONCLUSIONS: ML models could aid in classifying pwMS with clinical impairment and identify relevant biomarkers, but prediction of clinical worsening is an unmet need.
摘要：
背景：需要对多发性硬化症（MS）的临床损害和恶化建立稳健的预测模型，以识别有风险的患者并优化治疗策略。
目的：评估机器学习（ML）方法是否可以对MS（pwMS）患者的临床损害进行分类并预测其恶化，如果是,临床和磁共振成像（MRI）特征和ML算法的组合是最佳的。
方法：我们使用来自两个MS队列（柏林：n=125，阿姆斯特丹：n=330）的基线临床和结构MRI数据来评估5个ML模型在基线时对临床损害进行分类的能力，并在2年和5年的随访中预测未来的临床恶化。临床恶化由扩展残疾状态量表(EDSS)的增加来定义，定时25英尺行走测试(T25FW),9孔钉试验(9HPT),或符号数字模式测试(SDMT)。系统评估临床和体积MRI测量的不同组合以预测临床结果。ML模型使用蒙特卡罗交叉验证进行评估，曲线下面积（AUC），和排列测试来评估显著性。
结果：ML模型在基线时显著确定了阿姆斯特丹队列的临床损害，但在2年和5年的随访中对预测临床恶化没有意义。高度残疾（EDSS≥4）最好通过支持向量机（SVM）分类器使用临床和全局MRI体积（AUC=0.83±0.07，p=0.015）确定。认知受损（SDMTZ评分≤-1.5）最好通过SVM使用区域MRI体积（丘脑，心室,病变,和海马)，达到0.73±0.04的AUC（p=0.008）。
结论：ML模型可以帮助对具有临床损害的pwMS进行分类，并确定相关的生物标志物，但是预测临床恶化是一个未满足的需求。
目的：评估机器学习（ML）方法是否可以对MS（pwMS）患者的临床损害进行分类并预测其恶化，如果是,临床和磁共振成像（MRI）特征和ML算法的组合是最佳的。
方法：我们使用来自两个MS队列（柏林：n=125，阿姆斯特丹：n=330）的基线临床和结构MRI数据来评估5个ML模型在基线时对临床损害进行分类的能力，并在2年和5年的随访中预测未来的临床恶化。临床恶化由扩展残疾状态量表(EDSS)的增加来定义，定时25英尺行走测试(T25FW),9孔钉试验(9HPT),或符号数字模式测试(SDMT)。系统评估临床和体积MRI测量的不同组合以预测临床结果。ML模型使用蒙特卡罗交叉验证进行评估，曲线下面积（AUC），和排列测试来评估显著性。
结果：ML模型在基线时显著确定了阿姆斯特丹队列的临床损害，但在2年和5年的随访中对预测临床恶化没有意义。高度残疾（EDSS≥4）最好通过支持向量机（SVM）分类器使用临床和全局MRI体积（AUC=0.83±0.07，p=0.015）确定。认知受损（SDMTZ评分≤-1.5）最好通过SVM使用区域MRI体积（丘脑，心室,病变,和海马)，达到0.73±0.04的AUC（p=0.008）。
结论：ML模型可以帮助对具有临床损害的pwMS进行分类，并确定相关的生物标志物，但是预测临床恶化是一个未满足的需求。
