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 language | English |
|---|---|
| Pages (from-to) | 240-247 |
| Number of pages | 8 |
| Journal | NeuroImage: Clinical |
| Volume | 16 |
| DOIs | |
| Publication status | Published - Jan 1 2017 |
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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 journal › Article
}
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 -