ACR Appropriateness Criteria® Movement Disorders and Neurodegenerative Diseases

Imaging, Expert Panel on Neurological

doi:10.1016/j.jacr.2020.01.042

Cited by 5 publications

(7 citation statements)

References 81 publications

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“…The presence of MBS in ALS patients has been associated with a poorer prognosis and faster disease progression [6,31]. The reason for this correlation is not fully understood, but it is believed that the involvement of the motor cortex in ALS pathophysiology may play a role.…”

Section: The Fundamental Pathophysiology Of Als and Mbsmentioning

confidence: 99%

“…MBS can be a predictor of the spread of the disease to other regions of the brain and spinal cord. This spread, also known as "neurodegeneration," is a hallmark of ALS and is thought to be responsible for the progressive loss of motor function [31,32]. MBS has been linked to the involvement of the UMNs, suggesting that it may be an indicator of widespread neurodegeneration [7].…”

Section: The Fundamental Pathophysiology Of Als and Mbsmentioning

confidence: 99%

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Motor Band Sign in Motor Neuron Diseases Using Magnetic Resonance Imaging: A Systematic Review

Mohammadi,

Ghaderi

2023

Acta Neurologica Scandinavica

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Motor neuron diseases (MNDs) like amyotrophic lateral sclerosis (ALS) are progressive neurodegenerative disorders affecting upper and lower motor neurons (UMN and LMN). Magnetic resonance imaging (MRI) often reveals a “motor band sign” (MBS) of hypointensity along the precentral gyri in ALS, considered a radiologic hallmark. This review comprehensively summarizes the literature on MBS in ALS and related MNDs using multiple MRI techniques. A systematic search was conducted in the PubMed and Scopus databases to identify relevant studies on MBS in MNDs published until August 2023. Twelve studies were included. Most patients had UMN involvement at the onset. MBS was correlated with UMN impairment severity. Susceptibility-weighted imaging (SWI) detected MBS in the majority of MND patients. The use of SWI could be particularly useful in detecting MBS, and it should be considered as part of the routine clinical MRI protocols. Recent studies suggest that hypointensity and atrophy of the primary motor cortex (M1) and nearby regions can be used as MRI markers of UMN impairment in MNDs. Other MRI techniques like T2-weighted (T2-w), T2 ∗ -w, and fluid-attenuated inversion recovery (FLAIR) also showed characteristic changes. Furthermore, quantitative susceptibility mapping (QSM) is an advanced MRI technique that allows sensitive quantification of iron deposition and has shown promise for accurately detecting MBS in MNDs. The findings suggest that MR neuroimaging techniques can provide valuable insights into the pathophysiology of MND and can be used to detect biomarkers such as MBS. The review demonstrates that advanced MRI techniques can detect cortical and white matter changes reflecting upper motor neuron degeneration in MNDs like ALS. To find out how sensitive and suggestive the MBS is in MNDs and neurodegenerative movement disorders and how well it works as a prognostic indicator, we will need to do more research that combines comprehensive prospective and longitudinal research.

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Section: The Fundamental Pathophysiology Of Als and Mbsmentioning

confidence: 99%

Section: The Fundamental Pathophysiology Of Als and Mbsmentioning

confidence: 99%

Motor Band Sign in Motor Neuron Diseases Using Magnetic Resonance Imaging: A Systematic Review

Mohammadi,

Ghaderi

2023

Acta Neurologica Scandinavica

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“…Brain magnetic resonance imaging (MRI) is recognized as the imaging modality that produces the best images of brain tissues, body fluids, and fat [ 1 ]. It remains the most appropriate modality for diagnosing patients with symptoms of multiple brain diseases including inflammatory diseases, dementia, neurodegenerative disease, cerebrovascular disease, and brain tumors [ 2 , 3 , 4 , 5 ]; hence, it plays an important role in multiple clinical scenarios ranging from acute diagnostics to routine follow-ups.…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

et al. 2022

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We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study’s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.

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“…Therefore, neuroimaging techniques are increasingly used in the workup of PD and can substantially support the clinical diagnosis [ 2 ]. Various neuroimaging modalities, such as magnetic resonance imaging (MRI) of the brain, single-photon emission computed tomography (SPECT), and positron emission tomography (PET), improve the accuracy of the diagnosis in patients with parkinsonism [ 5 , 6 ]. Several radiopharmaceuticals, which target the presynaptic dopamine transporter (DAT) mediating the reuptake of dopamine from the synaptic cleft, derived from tropane and cocaine analogs, including [ 123 I]N-ω-fluoropropyl-2β-carbomethoxy-3β-(4-iodophenyl)nortropane ([ 123 I]fluoropropylcarbomethoxyiodophenylnortropane (FP-CIT), [ 123 I]carbomethoxyiodo-phenyl-tropane ([ 123 I]β-CIT), and [ 18 F]FP-CIT have been used to assess the DAT location and density in the striatum, and their binding to DATs in the striatum is an important clue in the differential diagnosis of parkinsonism [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [18F]FP-CIT Positron Emission Tomography

et al. 2021

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The aim of this study was to compare the performance of a deep-learning convolutional neural network (Faster R-CNN) model to detect imaging findings suggestive of idiopathic Parkinson’s disease (PD) based on [18F]FP-CIT PET maximum intensity projection (MIP) images versus that of nuclear medicine (NM) physicians. The anteroposterior MIP images of the [18F]FP-CIT PET scan of 527 patients were classified as having PD (139 images) or non-PD (388 images) patterns according to the final diagnosis. Non-PD patterns were classified as overall-normal (ONL, 365 images) and vascular parkinsonism with definite defects or prominently decreased dopamine transporter binding (dVP, 23 images) patterns. Faster R-CNN was trained on 120 PD, 320 ONL, and 16 dVP pattern images and tested on the 19 PD, 45 ONL, and seven dVP patterns images. The performance of the Faster R-CNN and three NM physicians was assessed using receiver operating characteristics curve analysis. The difference in performance was assessed using Cochran’s Q test, and the inter-rater reliability was calculated. Faster R-CNN showed high accuracy in differentiating PD from non-PD patterns and also from dVP patterns, with results comparable to those of NM physicians. There were no significant differences in the area under the curve and performance. The inter-rater reliability among Faster R-CNN and NM physicians showed substantial to almost perfect agreement. The deep-learning model accurately differentiated PD from non-PD patterns on MIP images of [18F]FP-CIT PET, and its performance was comparable to that of NM physicians.

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ACR Appropriateness Criteria® Movement Disorders and Neurodegenerative Diseases

Cited by 5 publications

References 81 publications

Motor Band Sign in Motor Neuron Diseases Using Magnetic Resonance Imaging: A Systematic Review

Motor Band Sign in Motor Neuron Diseases Using Magnetic Resonance Imaging: A Systematic Review

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

Faster Region-Based Convolutional Neural Network in the Classification of Different Parkinsonism Patterns of the Striatum on Maximum Intensity Projection Images of [18F]FP-CIT Positron Emission Tomography

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