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.

Deep Neural Networks and Kernel Regression Achieve Comparable Accuracies for Functional Connectivity Prediction of Behavior and Demographics

“…Second, DNNs have been applied as tools to analyze neuroscience data, including lesion and tumor segmentation (Pinto et al, 2016;Havaei et al, 2017;Kamnitsas et al, 2017b;, anatomical segmentation (Wachinger et al, 2018;X. Zhao et al, 2018), image modality/quality transfer (Bahrami et al, 2016;Nie et al, 2017;Blumberg et al, 2018), image registration Dalca et al, 2018), as well as behavioral and disease prediction (Plis et al, 2014;van der Burgh et al, 2017;Vieira et al, 2017;Nguyen et al, 2018).…”

Quantitative imaging of cancer in the postgenomic era: Radio(geno)mics, deep learning, and habitats

“…[79][80][81][82][83][84][85][86] Although there are many hundreds of descriptive features designed by prior knowledge, the current feature sets still may not be optimal for a given task. [104][105][106][107] DL involves abstraction by building networks with >2 processing layers. [87][88] Only recently applied to radiomics, DL has proven to be valuable in both differential diagnosis [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103] and prognosis.…”

“…The first CNN was proposed by LeCun et al in 1998, 109 but its success was limited until the advent of graphic processing units and the development of learning algorithms. [104][105][106][107] Cancer December 15, 2018 Two major factors influencing CNN applications in diagnostic imaging are computational power and the availability of training data. With medical imaging, the input layer during training includes images or subregions of labeled images, which then are convolved in sublayers along with their known classifiers (for example, benign or malignant) to identify those image features that are most related to the classification.…”

“…[87][88] Only recently applied to radiomics, DL has proven to be valuable in both differential diagnosis [89][90][91][92][93][94][95][96][97][98][99][100][101][102][103] and prognosis. [104][105][106][107] DL involves abstraction by building networks with >2 processing layers. 108 For medical images, convolutional neural networks (CNNs) are the most common models because they can accept 2-dimensional (2D) or 3-dimensional (3D) images as input.…”

Deep Learning for Medical Image Analysis: Applications to Computed Tomography and Magnetic Resonance Imaging

“…Adapted with permission However, most of the earlier works present survival prediction into roughly divided groups. Long or short overall survival time, short, medium or long survivors, and future disease activity within two years [39,40,41]. These works have their limitation from its retroscopic nature and impractical categories of survival prediction.…”