Multimodal structural MRI in the diagnosis of motor neuron diseases

Pilar M. Ferraro, Federica Agosta, Nilo Riva, Massimiliano Copetti, Edoardo Gioele Spinelli, Yuri Falzone, Gianni Sorarù, Giancarlo Comi, Adriano Chiò, Massimo Filippi

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

This prospective study developed an MRI-based method for identification of individual motor neuron disease (MND) patients and test its accuracy at the individual patient level in an independent sample compared with mimic disorders. 123 patients with amyotrophic lateral sclerosis (ALS), 44 patients with predominantly upper motor neuron disease (PUMN), 20 patients with ALS-mimic disorders, and 78 healthy controls were studied. The diagnostic accuracy of precentral cortical thickness and diffusion tensor (DT) MRI metrics of corticospinal and motor callosal tracts were assessed in a training cohort and externally proved in a validation cohort using a random forest analysis. In the training set, precentral cortical thickness showed 0.86 and 0.89 accuracy in differentiating ALS and PUMN patients from controls, while DT MRI distinguished the two groups from controls with 0.78 and 0.92 accuracy. In ALS vs controls, the combination of cortical thickness and DT MRI metrics (combined model) improved the classification pattern (0.91 accuracy). In the validation cohort, the best accuracy was reached by DT MRI (0.87 and 0.95 accuracy in ALS and PUMN vs mimic disorders). The combined model distinguished ALS and PUMN patients from mimic syndromes with 0.87 and 0.94 accuracy. A multimodal MRI approach that incorporates motor cortical and white matter alterations yields statistically significant improvement in accuracy over using each modality separately in the individual MND patient classification. DT MRI represents the most powerful tool to distinguish MND from mimic disorders.

Original languageEnglish
Pages (from-to)240-247
Number of pages8
JournalNeuroImage: Clinical
Volume16
DOIs
Publication statusPublished - Jan 1 2017

Fingerprint

Motor Neuron Disease
Amyotrophic Lateral Sclerosis
Diffusion Magnetic Resonance Imaging
Corpus Callosum
Prospective Studies
Control Groups

Keywords

  • Amyotrophic lateral sclerosis
  • Diagnosis
  • Motor neuron disease
  • MRI
  • Random forest analysis

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Neurology
  • Clinical Neurology
  • Cognitive Neuroscience

Cite this

Multimodal structural MRI in the diagnosis of motor neuron diseases. / Ferraro, Pilar M.; Agosta, Federica; Riva, Nilo; Copetti, Massimiliano; Spinelli, Edoardo Gioele; Falzone, Yuri; Sorarù, Gianni; Comi, Giancarlo; Chiò, Adriano; Filippi, Massimo.

In: NeuroImage: Clinical, Vol. 16, 01.01.2017, p. 240-247.

Research output: Contribution to journalArticle

Ferraro, Pilar M. ; Agosta, Federica ; Riva, Nilo ; Copetti, Massimiliano ; Spinelli, Edoardo Gioele ; Falzone, Yuri ; Sorarù, Gianni ; Comi, Giancarlo ; Chiò, Adriano ; Filippi, Massimo. / Multimodal structural MRI in the diagnosis of motor neuron diseases. In: NeuroImage: Clinical. 2017 ; Vol. 16. pp. 240-247.
@article{700c2e5172424cc28b5e88ac7dfee475,
title = "Multimodal structural MRI in the diagnosis of motor neuron diseases",
abstract = "This prospective study developed an MRI-based method for identification of individual motor neuron disease (MND) patients and test its accuracy at the individual patient level in an independent sample compared with mimic disorders. 123 patients with amyotrophic lateral sclerosis (ALS), 44 patients with predominantly upper motor neuron disease (PUMN), 20 patients with ALS-mimic disorders, and 78 healthy controls were studied. The diagnostic accuracy of precentral cortical thickness and diffusion tensor (DT) MRI metrics of corticospinal and motor callosal tracts were assessed in a training cohort and externally proved in a validation cohort using a random forest analysis. In the training set, precentral cortical thickness showed 0.86 and 0.89 accuracy in differentiating ALS and PUMN patients from controls, while DT MRI distinguished the two groups from controls with 0.78 and 0.92 accuracy. In ALS vs controls, the combination of cortical thickness and DT MRI metrics (combined model) improved the classification pattern (0.91 accuracy). In the validation cohort, the best accuracy was reached by DT MRI (0.87 and 0.95 accuracy in ALS and PUMN vs mimic disorders). The combined model distinguished ALS and PUMN patients from mimic syndromes with 0.87 and 0.94 accuracy. A multimodal MRI approach that incorporates motor cortical and white matter alterations yields statistically significant improvement in accuracy over using each modality separately in the individual MND patient classification. DT MRI represents the most powerful tool to distinguish MND from mimic disorders.",
keywords = "Amyotrophic lateral sclerosis, Diagnosis, Motor neuron disease, MRI, Random forest analysis",
author = "Ferraro, {Pilar M.} and Federica Agosta and Nilo Riva and Massimiliano Copetti and Spinelli, {Edoardo Gioele} and Yuri Falzone and Gianni Sorar{\`u} and Giancarlo Comi and Adriano Chi{\`o} and Massimo Filippi",
year = "2017",
month = "1",
day = "1",
doi = "10.1016/j.nicl.2017.08.002",
language = "English",
volume = "16",
pages = "240--247",
journal = "NeuroImage: Clinical",
issn = "2213-1582",
publisher = "ELSEVIER SCI LTD",

}

TY - JOUR

T1 - Multimodal structural MRI in the diagnosis of motor neuron diseases

AU - Ferraro, Pilar M.

AU - Agosta, Federica

AU - Riva, Nilo

AU - Copetti, Massimiliano

AU - Spinelli, Edoardo Gioele

AU - Falzone, Yuri

AU - Sorarù, Gianni

AU - Comi, Giancarlo

AU - Chiò, Adriano

AU - Filippi, Massimo

PY - 2017/1/1

Y1 - 2017/1/1

N2 - This prospective study developed an MRI-based method for identification of individual motor neuron disease (MND) patients and test its accuracy at the individual patient level in an independent sample compared with mimic disorders. 123 patients with amyotrophic lateral sclerosis (ALS), 44 patients with predominantly upper motor neuron disease (PUMN), 20 patients with ALS-mimic disorders, and 78 healthy controls were studied. The diagnostic accuracy of precentral cortical thickness and diffusion tensor (DT) MRI metrics of corticospinal and motor callosal tracts were assessed in a training cohort and externally proved in a validation cohort using a random forest analysis. In the training set, precentral cortical thickness showed 0.86 and 0.89 accuracy in differentiating ALS and PUMN patients from controls, while DT MRI distinguished the two groups from controls with 0.78 and 0.92 accuracy. In ALS vs controls, the combination of cortical thickness and DT MRI metrics (combined model) improved the classification pattern (0.91 accuracy). In the validation cohort, the best accuracy was reached by DT MRI (0.87 and 0.95 accuracy in ALS and PUMN vs mimic disorders). The combined model distinguished ALS and PUMN patients from mimic syndromes with 0.87 and 0.94 accuracy. A multimodal MRI approach that incorporates motor cortical and white matter alterations yields statistically significant improvement in accuracy over using each modality separately in the individual MND patient classification. DT MRI represents the most powerful tool to distinguish MND from mimic disorders.

AB - This prospective study developed an MRI-based method for identification of individual motor neuron disease (MND) patients and test its accuracy at the individual patient level in an independent sample compared with mimic disorders. 123 patients with amyotrophic lateral sclerosis (ALS), 44 patients with predominantly upper motor neuron disease (PUMN), 20 patients with ALS-mimic disorders, and 78 healthy controls were studied. The diagnostic accuracy of precentral cortical thickness and diffusion tensor (DT) MRI metrics of corticospinal and motor callosal tracts were assessed in a training cohort and externally proved in a validation cohort using a random forest analysis. In the training set, precentral cortical thickness showed 0.86 and 0.89 accuracy in differentiating ALS and PUMN patients from controls, while DT MRI distinguished the two groups from controls with 0.78 and 0.92 accuracy. In ALS vs controls, the combination of cortical thickness and DT MRI metrics (combined model) improved the classification pattern (0.91 accuracy). In the validation cohort, the best accuracy was reached by DT MRI (0.87 and 0.95 accuracy in ALS and PUMN vs mimic disorders). The combined model distinguished ALS and PUMN patients from mimic syndromes with 0.87 and 0.94 accuracy. A multimodal MRI approach that incorporates motor cortical and white matter alterations yields statistically significant improvement in accuracy over using each modality separately in the individual MND patient classification. DT MRI represents the most powerful tool to distinguish MND from mimic disorders.

KW - Amyotrophic lateral sclerosis

KW - Diagnosis

KW - Motor neuron disease

KW - MRI

KW - Random forest analysis

UR - http://www.scopus.com/inward/record.url?scp=85026754705&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85026754705&partnerID=8YFLogxK

U2 - 10.1016/j.nicl.2017.08.002

DO - 10.1016/j.nicl.2017.08.002

M3 - Article

AN - SCOPUS:85026754705

VL - 16

SP - 240

EP - 247

JO - NeuroImage: Clinical

JF - NeuroImage: Clinical

SN - 2213-1582

ER -