van der Burgh Hannelore K, Schmidt Ruben, Westeneng Henk-Jan, de Reus Marcel A, van den Berg Leonard H, van den Heuvel Martijn P
Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
Neuroimage Clin. 2016 Oct 11;13:361-369. doi: 10.1016/j.nicl.2016.10.008. eCollection 2017.
Amyotrophic lateral sclerosis (ALS) is a progressive neuromuscular disease, with large variation in survival between patients. Currently, it remains rather difficult to predict survival based on clinical parameters alone. Here, we set out to use clinical characteristics in combination with MRI data to predict survival of ALS patients using deep learning, a machine learning technique highly effective in a broad range of big-data analyses. A group of 135 ALS patients was included from whom high-resolution diffusion-weighted and T1-weighted images were acquired at the first visit to the outpatient clinic. Next, each of the patients was monitored carefully and survival time to death was recorded. Patients were labeled as short, medium or long survivors, based on their recorded time to death as measured from the time of disease onset. In the deep learning procedure, the total group of 135 patients was split into a training set for deep learning (n = 83 patients), a validation set (n = 20) and an independent evaluation set (n = 32) to evaluate the performance of the obtained deep learning networks. Deep learning based on clinical characteristics predicted survival category correctly in 68.8% of the cases. Deep learning based on MRI predicted 62.5% correctly using structural connectivity and 62.5% using brain morphology data. Notably, when we combined the three sources of information, deep learning prediction accuracy increased to 84.4%. Taken together, our findings show the added value of MRI with respect to predicting survival in ALS, demonstrating the advantage of deep learning in disease prognostication.
肌萎缩侧索硬化症（ALS）是一种进行性神经肌肉疾病，患者之间的生存期差异很大。目前，仅基于临床参数来预测生存期仍然相当困难。在此，我们着手将临床特征与磁共振成像（MRI）数据相结合，利用深度学习来预测ALS患者的生存期，深度学习是一种在广泛的大数据分析中非常有效的机器学习技术。我们纳入了一组135例ALS患者，在他们首次到门诊就诊时采集了高分辨率扩散加权成像和T1加权成像。接下来，对每位患者进行仔细监测，并记录其至死亡的生存时间。根据从疾病发作时起记录的死亡时间，将患者标记为短期、中期或长期存活者。在深度学习过程中，将135例患者的总体分为深度学习训练集（n = 83例患者）、验证集（n = 20例）和独立评估集（n = 32例），以评估所获得的深度学习网络的性能。基于临床特征的深度学习在68.8%的病例中正确预测了生存类别。基于MRI的深度学习，使用结构连接性时正确预测率为62.5%，使用脑形态学数据时正确预测率为62.5%。值得注意的是，当我们将这三种信息来源结合起来时，深度学习预测准确率提高到了84.4%。综上所述，我们的研究结果显示了MRI在预测ALS生存期方面的附加价值，证明了深度学习在疾病预后方面的优势。
