Background
Amyotrophic lateral sclerosis is a relentlessly progressive neurodegenerative condition. While the clinical features of ALS are highly heterogeneous, the overall disease trajectory and life expectancy from diagnosis is relatively uniform, making it a template neurodegenerative condition for the development of diagnostic and prognostic biomarkers [
1]. It is widely accepted that a long pre-symptomatic phase precedes the clinical manifestation of ALS [
2] which may be dominated by bulbar or spinal symptoms at onset, but progresses to respiratory failure over time.
Clinical heterogeneity has multiple dimensions in ALS such as site of onset, coexisting cognitive and behavioural deficits, dominance of upper or lower motor neurodegeneration, variability in progression rates and the relatively distinct clinical profile of various ALS genotypes [
3,
4]. All of these variables make accurate individual prognostication particularly challenging. Clinical heterogeneity precludes smaller clinical trials [
5] as a given drug may only be effective in certain ALS phenotypes. Robust and validated prognostic frameworks would enhance patient stratification into clinical trials, and enable the optimised management of individual patients. The planning and timing of supportive interventions such as feeding tube insertion, initiation of non-invasive ventilation, addressing end-of-life decisions and palliative measures could be guided by accurate prognostic markers.
Previous studies of ALS have successfully linked specific demographic and clinical variables to shorter survival; e.g. older age, bulbar or respiratory onset, recent symptom onset prior to diagnosis, significant motor impairment, coexisting executive dysfunction, rapid weight loss [
6‐
9]. Attendance of a multidisciplinary ALS clinic has been linked to a better prognosis [
10].
Magnetic resonance imaging (MRI) has been repeatedly proposed as a diagnostic or prognostic biomarker in ALS. [
11,
12] The core imaging features of ALS related neurodegeneration are well described: degeneration of the precentral gyrus [
13,
14], corpus callosum and corticospinal tract [
15,
16]. Despite numerous descriptive imaging studies in ALS, few studies have successfully translated group-level findings to aid the interpretation of individual data sets. While imaging measures were repeatedly proposed as potential diagnostic biomarkers in ALS, imaging parameters of single anatomical structures led to relatively poor diagnostic classification accuracy [
17‐
20]. Imaging measures in ALS have also been explored as prognostic indicators. Neuronal integrity of the motor cortex has been directly linked to survival [
21] and corticospinal tract fractional anisotropy (FA) was used to predict 3-year survival [
22].
The objective of this study is to develop and test an objective prognostic tool in ALS to predict 18-month survival based on quantitative MRI data. We hypothesised that structural MRI measures enhance prediction accuracy compared to clinical variables alone.
Discussion
The presented study explores the role of MRI measures as prognostic biomarkers in ALS. While diagnostic and monitoring biomarkers have been extensively investigated in ALS, there is a scarcity of prognostic studies. One of the key finding of the study is the more widespread white and grey matter degeneration in ‘short-survivors’ compared to ‘long-survivors’. Figures
5 and
6. Based on these results, we aimed to systematically evaluate the value of structural MRI measures of disease-defining brain regions and clinical indices in predicting the probability of 18-month survival.
Based on the combination of structural brain measures and clinical characteristics, mortality within 18-months was predicted with relatively high accuracy; 79.17%. Moreover, 83.3% of patients were correctly identified as surviving for longer than 18 months following their brain scan, and 75% of the sample was correctly identified as surviving less than 18-months. Applying the regression algorithm to an independent validation sample further supports the validity of these findings. Despite the relatively small sample size of the validation cohort, the algorithm reached 75% accuracy. 83.34% of patients were correctly identified as surviving more than 18-months and 66.67% of patients were correctly identified as surviving less than 18 months. Based on MRI measures alone, the accuracy and sensitivity of the classification was similar, but the patients surviving more than 18 months were less likely to be identified correctly.
Using clinical and demographic measures alone, without MRI indices, prediction accuracy was considerably lower (66.67%). Similarly, the sensitivity and specificity profile of these predictions were inferior to the ones also incorporating MRI measures. These findings underscore the benefit of MRI measures of ALS-associated brain regions in predicting 18-months survival. (Table
3.)
Evaluating misclassified patients based on clinical features alone, the group incorrectly classified surviving less than 18 months was significantly less physically impaired. They had a higher score of the ALSFRS-R. In contrast, patients misclassified as surviving longer than 18 months, had significantly longer disease duration. Adding MRI measure, there was no difference found between misclassified patients, again emphasizing the benefit of this additional information.
Previous studies have linked MRI measures to survival. Two-year survival was predicted using motor cortex spectroscopy with a sensitivity of 67% and a specificity of 83% [
21] and corticospinal tract diffusivity changes were utilised to predict three-year survival with a specificity of 61.5% and accuracy of 71.0%. [
22] In contrast to previous studies, we present a multi-modal approach, assessing cortical thickness alterations in addition to the four most commonly used indices of white matter degeneration. Additionally, we test the generalisability of our classification method in an independent validation sample.
Accurate prognostic markers have a role in clinical management as well as in clinical trials. In the absence of effective disease-modifying therapies, the optimal timing of supportive measures [
36], end-of-life decisions [
37], palliative interventions [
38] is particularly important in ALS. While ALS patients are eager to participate in clinical trials in all stages of the disease, it may be desirable to enrol relatively homogenous patient cohorts soon after their diagnosis, when limited neurodegenerative change has taken place. [
2,
5] It is frequently the case however that heterogeneous patient cohorts are enrolled in clinical trials, encompassing diverse phenotypes in order to rapidly meet the targeted sample size [
39]. In the era of precision medicine, therapeutic strategies and clinical trial designs should be tailored to specific phenotypes [
40,
41] and disease stages [
39]. For example, stem cells therapy is regarded to be less successful in bulbar onset ALS patients, and patients with advanced disease [
42]. Clinical trials of specific phenotypes or homogenous cohorts may have other advantages, such as the inclusion of patients who are likely to progress at a relatively uniform rate. It has been proposed that the inclusion of patients with rapid progression rates may shorten clinical trials [
43].
Using objective, validated and observer-independent prognostic markers, such as MRI measures, may be helpful for patient stratification into clinical trials. It is also important that study end-points, such as survival are independent from demographic factors. [
41].
Limitations and future directions
Our study outlines a prediction method based on single-time point MRI data, which is a snapshot of in vivo pathology at specific moment in the patient’s disease trajectory.
Survival prediction may be more accurate if multiple time-points are included and longitudinal change over time is considered. [
2] Moreover, the inclusion of other disease-specific anatomical regions, such as basal ganglia [
44,
45], spinal cord [
46,
47], cerebellar [
48] or electrophysiological measures [
49] may improve prognostic categorisation further. As only patients scanned at least 18 months ago were included, the sample size of the study is relatively limited and 20% of the patients were randomly allocated to the validation sample to demonstrate the generalizability of the methods. The present pilot study outlines a proposed prognostic algorithm which should ideally be replicated in larger cohorts or data pooled from multiple centres. Other future directions include assessment of two-year survival, or other clinical milestones, such as introduction of non-invasive ventilation, walking aids, feeding tubes etc. In this study, the cognitive and behavioural profile of the patients were not considered, despite evidence that executive dysfunction is associated with shorter survival [
7] and compliance with assistive devices. [
36] We also acknowledge that, with the current MR technology, the additional prognostic value of MRI indices is limited, and may not be substantial enough to be incorporated in clinical trial designs.
Acknowledgements
We are grateful for the generosity and kindness of all of our patients and their caregivers who have kindly participated in this study.