Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines

Morisi, Rita; Manners, David Neil; Gnecco, Giorgio; Lanconelli, Nico; Testa, Claudia; Evangelisti, Stefania; Talozzi, Lia; Gramegna, Laura Ludovica; Bianchini, Claudio; Calandra-Buonaura, Giovanna; Sambati, Luisa; Giannini, Giulia; Cortelli, Pietro; Tonon, Caterina; Lodi, Raffaele

doi:10.1016/j.parkreldis.2017.11.343

Cited by 18 publications

(33 citation statements)

References 23 publications

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“…However, there is still room for improvement because current atlases lack probabilistic information on infratentorial brain structures that are relevant to MSA neuropathology. In particular, cerebellar volume, the MCP width, increased apparent diffusion coefficient within the MCP as well as the cerebellar hemispheres, and pons atrophy were shown to improve diagnostic accuracy in the differential diagnosis of MSA …”

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confidence: 99%

“…In particular, cerebellar volume, the MCP width, increased apparent diffusion coefficient within the MCP as well as the cerebellar hemispheres, and pons atrophy were shown to improve diagnostic accuracy in the differential diagnosis of MSA. 9,[20][21][22] In the present study, we sought to further refine the currently available FreeSurfer-based subcortical segmentation atlas by adding probabilistic information on the location of the MCP. The morphometric profile was determined in patients with clinically defined MSA-P or MSA-C to evaluate the relative contribution of MCP measurements to diagnostic accuracy and to estimate the added diagnostic yield of MCP measurements for the differential diagnosis of MSA and PD.…”

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confidence: 99%

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Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

et al. 2019

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Background Manual width measurements of the middle cerebellar peduncle on MRI were shown to improve the accuracy of an imaging‐guided diagnosis of multiple system atrophy (MSA). Recently, automated volume segmentation algorithms were able to reliably differentiate patients with Parkinson's disease (PD) and the parkinsonian variant of MSA. The objective of the current study was to integrate probabilistic information of the middle cerebellar peduncle into an existing MRI atlas for automated subcortical segmentation and to evaluate the diagnostic properties of the novel atlas for the differential diagnosis of MSA (parkinsonian and cerebellar variant) versus PD. Methods Three Tesla MRI scans of 48 healthy individuals were used to establish an automated whole‐brain segmentation procedure that includes the volumes of the putamen, cerebellar gray and white matter, and the middle cerebellar peduncles. Classification accuracy of segmented volumes were tested in early‐stage MSA patients (18 MSA‐parkinsonism, 13 MSA‐cerebellar) and 19 PD patients using a C4.5 classifier. Results Putaminal and infratentorial atrophy were present in 77.8% and 61.1% of MSA‐parkinsonian patients, respectively. Four of 18 MSA‐parkinsonian patients (22.2%) had infratentorial atrophy without evidence of putaminal atrophy. Infratentorial atrophy was present in all MSA‐cerebellar patients, with concomitant putaminal atrophy in 46.2% of these cases. The diagnostic algorithm using putaminal and infratentorial volumetric information correctly classified all PD patients and 96.8% of MSA patients. Conclusions The middle cerebellar peduncle was successfully integrated into a subcortical segmentation atlas, and its excellent diagnostic accuracy outperformed existing volumetric MRI processing strategies in differentiating MSA patients with variable atrophy patterns from PD patients. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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confidence: 99%

Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

et al. 2019

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“…[13][14][15][16][17] Recently, several studies have demonstrated that machine learning algorithms trained with MRI data could differentiate between parkinsonian syndromes with high accuracy. [18][19][20][21][22][23][24][25] Nevertheless, most of these studies were based on one single type of MRI acquisition protocol, either T1-weighted volumetry [18][19][20] or diffusion-weighted data, 25 with three studies using a multimodal approach combining volumetry and diffusion 21 or including R2* relaxometry 22 or spectroscopy. 23 Two studies have relied on large cohorts, including 1002 25 or 464 subjects, 19 whereas others studies have investigated smaller samples.…”

Section: Introductionmentioning

confidence: 99%

“…[18][19][20][21][22][23][24][25] Nevertheless, most of these studies were based on one single type of MRI acquisition protocol, either T1-weighted volumetry [18][19][20] or diffusion-weighted data, 25 with three studies using a multimodal approach combining volumetry and diffusion 21 or including R2* relaxometry 22 or spectroscopy. 23 Two studies have relied on large cohorts, including 1002 25 or 464 subjects, 19 whereas others studies have investigated smaller samples. 18,[20][21][22][23] Only two studies have included at the same time PD, PSP, MSA-P and MSA-C subjects, 19,23 while other studies have not differentiated between MSA-P and MSA-C, 18 have only included MSA-P patients 25 or MSA-C patients 24 or have not included MSA 20,21 or PSP patients.…”

Section: Introductionmentioning

confidence: 99%

“…23 Two studies have relied on large cohorts, including 1002 25 or 464 subjects, 19 whereas others studies have investigated smaller samples. 18,[20][21][22][23] Only two studies have included at the same time PD, PSP, MSA-P and MSA-C subjects, 19,23 while other studies have not differentiated between MSA-P and MSA-C, 18 have only included MSA-P patients 25 or MSA-C patients 24 or have not included MSA 20,21 or PSP patients. 22 Moreover, these studies have mainly been designed in a research environment and tested without an independent replication cohort.…”

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confidence: 99%

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Automated classification of neurodegenerative parkinsonian syndromes using multimodal magnetic resonance imaging in a clinical setting

Chougar

Faouzi

Pyatigorskaya

et al. 2020

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Background: Several studies have shown that machine learning algorithms using MRI data can accurately discriminate parkinsonian syndromes. Validation under clinical conditions is missing. Objectives: To evaluate the accuracy for the categorization of parkinsonian syndromes of a machine learning algorithm trained with a research cohort and tested on an independent clinical replication cohort. Methods: 361 subjects, including 94 healthy controls, 139 patients with PD, 60 with PSP with Richardson's syndrome, 41 with MSA of the parkinsonian variant (MSA-P) and 27 with MSA of the cerebellar variant (MSA-P), were recruited. They were divided into a training cohort (n=179) scanned in a research environment, and a replication cohort (n=182), scanned in clinical conditions on different MRI systems. Volumes and DTI metrics in 13 brain regions were used as input for a supervised machine learning algorithm. Results: High accuracy was achieved using volumetry in the classification of PD versus PSP, PD versus MSA-P, PD versus MSA-C, PD versus atypical parkinsonian syndromes and PSP versus MSA-C in both cohorts, although slightly lower in the replication cohort (balanced accuracy: 0.800 to 0.915 in the training cohort; 0.741 to 0.928 in the replication cohort). Performance was lower in the classification of PSP versus MSA-P and MSA-P versus MSA-C. When adding DTI metrics, the performance tended to increase in the training cohort, but not in the replication cohort. Conclusions: A machine learning approach based on volumetric and DTI data can accurately classify subjects with early-stage parkinsonism, scanned on different MRI systems, in the setting of their clinical workup.

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Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

et al. 2020

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Background Machine learning algorithms using magnetic resonance imaging (MRI) data can accurately discriminate parkinsonian syndromes. Validation in patients recruited in routine clinical practice is missing. Objective The aim of this study was to assess the accuracy of a machine learning algorithm trained on a research cohort and tested on an independent clinical replication cohort for the categorization of parkinsonian syndromes. Methods Three hundred twenty‐two subjects, including 94 healthy control subjects, 119 patients with Parkinson's disease (PD), 51 patients with progressive supranuclear palsy (PSP) with Richardson's syndrome, 35 with multiple system atrophy (MSA) of the parkinsonian variant (MSA‐P), and 23 with MSA of the cerebellar variant (MSA‐C), were recruited. They were divided into a training cohort (n = 179) scanned in a research environment and a replication cohort (n = 143) examined in clinical practice on different MRI systems. Volumes and diffusion tensor imaging (DTI) metrics in 13 brain regions were used as input for a supervised machine learning algorithm. To harmonize data across scanners and reduce scanner‐dependent effects, we tested two types of normalizations using patient data or healthy control data. Results In the replication cohort, high accuracies were achieved using volumetry in the classification of PD–PSP, PD–MSA‐C, PSP–MSA‐C, and PD‐atypical parkinsonism (balanced accuracies: 0.840–0.983, area under the receiver operating characteristic curves: 0.907–0.995). Performances were lower for the classification of PD–MSA‐P, MSA‐C–MSA‐P (balanced accuracies: 0.765–0.784, area under the receiver operating characteristic curve: 0.839–0.871) and PD–PSP–MSA (balanced accuracies: 0.773). Performance using DTI was improved when normalizing by controls, but remained lower than that using volumetry alone or combined with DTI. Conclusions A machine learning approach based on volumetry enabled accurate classification of subjects with early‐stage parkinsonism, examined on different MRI systems, as part of their clinical assessment. © 2020 International Parkinson and Movement Disorder Society

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Multi-class parkinsonian disorders classification with quantitative MR markers and graph-based features using support vector machines

Cited by 18 publications

References 23 publications

Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

Morphometric MRI profiles of multiple system atrophy variants and implications for differential diagnosis

Automated classification of neurodegenerative parkinsonian syndromes using multimodal magnetic resonance imaging in a clinical setting

Automated Categorization of Parkinsonian Syndromes Using Magnetic Resonance Imaging in a Clinical Setting

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