title: Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis.
abstract: 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.
authors.affiliation: Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
authors.forename: Hannelore K
authors.initials: HK
authors.lastname: van der Burgh
authors.affiliation: Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
authors.forename: Ruben
authors.initials: R
authors.lastname: Schmidt
authors.affiliation: Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
authors.forename: Henk-Jan
authors.initials: HJ
authors.lastname: Westeneng
authors.affiliation: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
authors.forename: Marcel A
authors.initials: MA
authors.lastname: de Reus
authors.affiliation: Department of Neurology, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
authors.forename: Leonard H
authors.initials: LH
authors.lastname: van den Berg
authors.affiliation: Department of Psychiatry, Brain Center Rudolf Magnus, University Medical Center Utrecht, Heidelberglaan 100, PO Box 85500, 3508 GA, Utrecht, Netherlands.
authors.forename: Martijn P
authors.initials: MP
authors.lastname: van den Heuvel
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000690:Amyotrophic Lateral Sclerosis / Q000145:classification / Q000000981:diagnostic imaging* / Q000401:mortality; D005260:Female; D006801:Humans; D000069550:Machine Learning; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis
keywords: Amyotrophic lateral sclerosis; Deep learning; Neural network; Prediction; Survival; White matter connectivity