MR image texture in Parkinson’s disease: a longitudinal study

Sikiö, Minna; Holli-Helenius, Kirsi; Harrison, Lara; Ryymin, Pertti; Ruottinen, Hanna; Saunamäki, Tiia; Eskola, Hannu; Elovaara, Irina; Dastidar, Prasun

doi:10.1177/0284185113519775

Cited by 23 publications

(14 citation statements)

References 34 publications

(43 reference statements)

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“…TA may also prove useful to assess disease progression; this was shown in a longitudinal study with Parkinson disease patients where authors found progressive texture abnormalities at both thalami, caudate, and putamen that correlated with clinical deterioration. 23 As previously mentioned, we found only one other study using TA in ALS (https://www.cs.ualberta. ca/research/theses-publications/technical-reports/2014/TR14-01), which also found significant changes in ALS patients compared to controls.…”

Section: Discussionmentioning

confidence: 90%

MRI Texture Analysis Reveals Deep Gray Nuclei Damage in Amyotrophic Lateral Sclerosis

Albuquerque

Anjos

Andrade

et al. 2015

Journal of Neuroimaging

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Section: Discussionmentioning

confidence: 90%

MRI Texture Analysis Reveals Deep Gray Nuclei Damage in Amyotrophic Lateral Sclerosis

Albuquerque

Anjos

Andrade

et al. 2015

Journal of Neuroimaging

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“…In addition, pathological and DTI studies have demonstrated the presence of Lewy body deposits and microstructural deficits in the neostriatum of PD patients (4,7,8,41). All of these microstructural changes might be characterized by alteration of textural patterns of T2W images (28) and captured by radiomics approach.…”

Section: Discussionmentioning

confidence: 99%

“…In addition, recent studies indicated that radiomics features derived from DAT SPECT images can serve as a biomarker for PD progression tracking (26,27). Notably, a previous texture analysis study demonstrated that texture features based on T2-weighted imaging (T2WI) differed between PD patients and healthy controls in many brain regions (28). However, there is no published study that investigated the diagnosis of PD on conventional MRI by using a radiomics approach.…”

Section: Introductionmentioning

confidence: 99%

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

Liu¹,

Wang

Zheng³

et al. 2020

Front. Neurol.

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Background: Parkinson's disease (PD) is a neurodegenerative disease in which the neostriatum, including the caudate nucleus (CN) and putamen (PU), has an important role in the pathophysiology. However, conventional magnetic resonance imaging (MRI) lacks sufficient specificity to diagnose PD. Therefore, the study's aim was to investigate the feasibility of using a radiomics approach to distinguish PD patients from healthy controls on T2-weighted images of the neostriatum and provide a basis for the clinical diagnosis of PD. Methods: T2-weighted images from 69 PD patients and 69 age-and sex-matched healthy controls were obtained on the same 3.0T MRI scanner. Regions of interest (ROIs) were manually placed at the CN and PU on the slices showing the largest respective sizes of the CN and PU. We extracted 274 texture features from each ROI and then used the least absolute shrinkage and selection operator regression to perform feature selection and radiomics signature building to identify the CN and PU radiomics signatures consisting of optimal features. We used a receiver operating characteristic curve analysis to assess the diagnostic performance of two radiomics signatures in a training group and estimate the generalization performance in the test group. Results: There were no significant differences in the demographic and clinical characteristics between the PD patients and healthy controls. The CN and PU radiomics signatures were built using 12 and 7 optimal features, respectively. The performance of the two radiomics signatures to distinguish PD patients from healthy controls was good. In the training and test groups, the AUCs of the CN radiomics signatures were 0.9410 (95% confidence interval [CI]: 0.8986-0.9833) and 0.7732 (95% CI: 0.6292-0.9173), respectively, and the AUCs of the PU radiomics signature were 0.8767 (95% CI: 0.8066-0.9469) and 0.7143 (95% CI: 0.5540-0.8746), respectively. Vertl_GlevNonU_R appeared simultaneously in both the CN and PU radiomics signatures as an optimal feature. A t-test analysis revealed significantly higher levels of texture values of the CN and PU in the PD patients than healthy controls (P < 0.05). Conclusion: Neostriatum radiomics signatures achieved good diagnostic performance for PD and potentially could serve as a basis for the clinical diagnosis of PD.

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“…However the real utility of these texture features might be related to the evolution of the disease and, therefore, its application in longitudinal studies, as it has been recently proven [19]. It is very convenient to use a single number to characterize a measure (in our case, texture features) in each direction.…”

Section: Discussionmentioning

confidence: 99%

“…To complement the methodology, along with the intensity levels in the path, we have developed a radial method to compute the Gray Level Co-occurrence Matrix (GLCM). Firstly developed by Haralick [18], the GLCM is one of the preferred methods to characterize the texture of 2D images, and have been successfully used in the characterization of brain images [15,19]. In our approach, the original algorithm is modified to compute the GLCM along the extracted curvilinear paths and its neighbourhood, from which a number of descriptors of the underlying texture in each direction can be extracted.…”

Section: Introductionmentioning

confidence: 99%

A Structural Parametrization of the Brain Using Hidden Markov Models-Based Paths in Alzheimer’s Disease

Martínez-Murcia

Górriz

Ramı́rez

et al. 2016

Int. J. Neur. Syst.

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The usage of biomedical imaging in the diagnosis of dementia is increasingly widespread. A number of works explore the possibilities of computational techniques and algorithms in what is called Computed Aided Diagnosis. Our work presents an automatic parametrization of the brain structure by means of a path generation algorithm based on Hidden Markov Models. The path is traced using information of intensity and spatial orientation in each node, adapting to the structural changes of the brain. Each path is itself a useful way to extract features from the MRI image, being the intensity levels at each node the most straightforward. However, a further processing consisting of a modification of the Gray Level Co-occurrence Matrix can be used to characterize the textural changes that occur throughout the path, yielding more meaningful values that could be associated to the structural changes in Alzheimer's Disease, as well as providing a significant feature reduction. This methodology achieves high performance, up to 80.3% of accuracy using a single path in differential diagnosis involving Alzheimer-affected subjects versus controls belonging to the Alzheimer's Disease Neuroimaging Initiative (ADNI).

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MR image texture in Parkinson’s disease: a longitudinal study

Abstract: Texture analysis offers a quantitative method for detecting structural changes in brain MR images. However, the protocol and repeatability of the method must be enhanced before possible clinical use.

Cited by 23 publications

References 34 publications

MRI Texture Analysis Reveals Deep Gray Nuclei Damage in Amyotrophic Lateral Sclerosis

MRI Texture Analysis Reveals Deep Gray Nuclei Damage in Amyotrophic Lateral Sclerosis

Parkinson's Disease Diagnosis Using Neostriatum Radiomic Features Based on T2-Weighted Magnetic Resonance Imaging

A Structural Parametrization of the Brain Using Hidden Markov Models-Based Paths in Alzheimer’s Disease

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