Dataset Information

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

ABSTRACT: 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 International Parkinson and Movement Disorder Society.

SUBMITTER: Krismer F

PROVIDER: S-EPMC6767501 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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

Krismer Florian F Seppi Klaus K Göbel Georg G Steiger Ruth R Zucal Isabel I Boesch Sylvia S Gizewski Elke R ER Wenning Gregor K GK Poewe Werner W Scherfler Christoph C

Movement disorders : official journal of the Movement Disorder Society 20190328 7

<h4>Background</h4>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 automate ...[more]

PMID: 30919495

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Project description:BackgroundManual region-of-interest analysis of putaminal and middle cerebellar peduncle diffusivity distinguishes patients with multiple system atrophy (MSA) and Parkinson's disease (PD) with high diagnostic accuracy. However, a recent meta-analysis found substantial between-study heterogeneity of diagnostic accuracy due to the lack of harmonized imaging protocols and standardized analyses pipelines.ObjectiveEvaluation of diagnostic accuracy of observer-independent analysis of microstructural integrity as measured by diffusion-tensor imaging in patients with MSA and PD.MethodsA total of 29 patients with MSA and 19 patients with PD (matched for age, gender, and disease duration) with 3 years of follow-up were investigated with diffusion-tensor imaging and T1-weighted magnetic resonance imaging. Automated localization of relevant brain regions was obtained, and mean diffusivity and fractional anisotropy values were averaged within the regions of interest. The classification was performed using a C5.0 hierachical decision tree algorithm.ResultsMean diffusivity of the middle cerebellar peduncle and cerebellar gray and white matter compartment as well as the putamen were significantly increased in patients with MSA and showed superior effect sizes compared to the volumetric analysis of these regions. A classifier model identified mean diffusivity of the middle cerebellar peduncle and putamen as the most predictive parameters. Cross-validation of the classification model yields a Cohen's κ and overall diagnostic accuracy of 0.823 and 0.914, respectively.ConclusionAnalysis of microstructural integrity within the middle cerebellar peduncle and putamen yielded a superior effect size compared to the volumetric measures, resulting in excellent diagnostic accuracy to discriminate patients with MSA from PD in the early to moderate disease stages. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Project description:Background Although artificial intelligence (AI) shows promise across many aspects of radiology, the use of AI to create differential diagnoses for rare and common diseases at brain MRI has not been demonstrated. Purpose To evaluate an AI system for generation of differential diagnoses at brain MRI compared with radiologists. Materials and Methods This retrospective study tested performance of an AI system for probabilistic diagnosis in patients with 19 common and rare diagnoses at brain MRI acquired between January 2008 and January 2018. The AI system combines data-driven and domain-expertise methodologies, including deep learning and Bayesian networks. First, lesions were detected by using deep learning. Then, 18 quantitative imaging features were extracted by using atlas-based coregistration and segmentation. Third, these image features were combined with five clinical features by using Bayesian inference to develop probability-ranked differential diagnoses. Quantitative feature extraction algorithms and conditional probabilities were fine-tuned on a training set of 86 patients (mean age, 49 years ± 16 [standard deviation]; 53 women). Accuracy was compared with radiology residents, general radiologists, neuroradiology fellows, and academic neuroradiologists by using accuracy of top one, top two, and top three differential diagnoses in 92 independent test set patients (mean age, 47 years ± 18; 52 women). Results For accuracy of top three differential diagnoses, the AI system (91% correct) performed similarly to academic neuroradiologists (86% correct; P = .20), and better than radiology residents (56%; P < .001), general radiologists (57%; P < .001), and neuroradiology fellows (77%; P = .003). The performance of the AI system was not affected by disease prevalence (93% accuracy for common vs 85% for rare diseases; P = .26). Radiologists were more accurate at diagnosing common versus rare diagnoses (78% vs 47% across all radiologists; P < .001). Conclusion An artificial intelligence system for brain MRI approached overall top one, top two, and top three differential diagnoses accuracy of neuroradiologists and exceeded that of less-specialized radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Zaharchuk in this issue.

Dataset Information

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

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

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