Evidence map›Paper›PMID 39985309›Full record

ArticleAnnals of neurology2025

Regional Cerebral Atrophy Contributes to Personalized Survival Prediction in Amyotrophic Lateral Sclerosis: A Multicentre, Machine Learning, Deformation-Based Morphometry Study.

Isabelle Lajoie, Canadian ALS Neuroimaging Consortium (CALSNIC), Sanjay Kalra, Mahsa Dadar

Abstract readMulticenter Study
In one paragraph

Article in Annals of neurology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

12 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Isabelle LajoieDouglas Mental Health University Health Centre, Montreal, Quebec, Canada.ORCID 0009-0004-6745-1532
Canadian ALS Neuroimaging Consortium (CALSNIC)
Sanjay Kalra *Neuroscience and Mental Health Institute, University of Alberta, Edmonton, Alberta, Canada.
Mahsa Dadar *Douglas Mental Health University Health Centre, Montreal, Quebec, Canada.ORCID 0000-0003-4008-2672

Funding

ALS Society of CanadaFondation Brain Canada
6 · The paper itself

Abstract

objectiveAccurate personalized survival prediction in amyotrophic lateral sclerosis is essential for effective patient care planning. This study investigates whether grey and white matter changes measured by magnetic resonance imaging can improve individual survival predictions.

methodsWe analyzed data from 178 patients with amyotrophic lateral sclerosis and 166 healthy controls in the Canadian Amyotrophic Lateral Sclerosis Neuroimaging Consortium study. A voxel-wise linear mixed-effects model assessed disease-related and survival-related atrophy detected through deformation-based morphometry, controlling for age, sex, and scanner variations. Additional linear mixed-effects models explored associations between regional imaging and clinical measurements, and their associations with time to the composite outcome of death, tracheostomy, or permanent assisted ventilation. We evaluated whether incorporating imaging features alongside clinical data could improve the performance of an individual survival distribution model.

resultsDeformation-based morphometry uncovered distinct voxel-wise atrophy patterns linked to disease progression and survival, with many of these regional atrophies significantly associated with clinical manifestations of the disease. By integrating regional imaging features with clinical data, we observed a substantial enhancement in the performance of survival models across key metrics. Our analysis identified specific brain regions, such as the corpus callosum, rostral middle frontal gyrus, and thalamus, where atrophy predicted an increased risk of mortality.

interpretationThis study suggests that brain atrophy patterns measured by deformation-based morphometry provide valuable insights beyond clinical assessments for prognosis. It offers a more comprehensive approach to prognosis and highlights brain regions involved in disease progression and survival, potentially leading to a better understanding of amyotrophic lateral sclerosis. ANN NEUROL 2025;97:1144-1157.

Indexed as

Amyotrophic Lateral SclerosisBrainMachine LearningAgedAtrophyDisease ProgressionFemaleGray MatterHumansMagnetic Resonance ImagingMaleMiddle AgedWhite Matter

Identifiers

PMID39985309
PMCPMC12082021

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.