Substantia Nigra Radiomics Feature Extraction of Parkinson’s Disease Based on Magnitude Images of Susceptibility-Weighted Imaging

Ren, Qingguo; Wang, Yihua; Leng, Shanshan; Nan, Xiaomin; Zhang, Bin; Shuai, Xinyan; Zhang, Jianyuan; Xia, Xiaona; Li, Ye; Meng, Xia; Zhao, Chuanzhi

doi:10.3389/fnins.2021.646617

Cited by 12 publications

(14 citation statements)

References 23 publications

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“…The sensitivity and specificity of radiomics analysis for the diagnosis and progression prediction has been demonstrated in multiple diseases 18 – 20 . This approach is widely used in oncological applications, however it is less frequently used in neurological disorders, with few studies performed in PD 21 – 24 .…”

Section: Introductionmentioning

confidence: 99%

Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease

Bashat

Thaler

Shacham

et al. 2022

npj Parkinsons Dis.

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MRI was suggested as a promising method for the diagnosis and assessment of Parkinson’s Disease (PD). We aimed to assess the sensitivity of neuromelanin-MRI and T2* with radiomics analysis for detecting PD, identifying individuals at risk, and evaluating genotype-related differences. Patients with PD and non-manifesting (NM) participants [NM-carriers (NMC) and NM-non-carriers (NMNC)], underwent MRI and DAT-SPECT. Imaging-based metrics included 48 neuromelanin and T2* radiomics features and DAT-SPECT specific-binding-ratios (SBR), were extracted from several brain regions. Imaging values were assessed for their correlations with age, differences between groups, and correlations with the MDS-likelihood-ratio (LR) score. Several machine learning classifiers were evaluated for group classification. A total of 127 participants were included: 46 patients with PD (62.3 ± 10.0 years) [15:LRRK2-PD, 16:GBA-PD, and 15:idiopathic-PD (iPD)], 47 NMC (51.5 ± 8.3 years) [24:LRRK2-NMC and 23:GBA-NMC], and 34 NMNC (53.5 ± 10.6 years). No significant correlations were detected between imaging parameters and age. Thirteen MRI-based parameters and radiomics features demonstrated significant differences between PD and NMNC groups. Support-Vector-Machine (SVM) classifier achieved the highest performance (AUC = 0.77). Significant correlations were detected between LR scores and two radiomic features. The classifier successfully identified two out of three NMC who converted to PD. Genotype-related differences were detected based on radiomic features. SBR values showed high sensitivity in all analyses. In conclusion, neuromelanin and T2* MRI demonstrated differences between groups and can be used for the assessment of individuals at-risk in cases when DAT-SPECT can’t be performed. Combining neuromelanin and T2*-MRI provides insights into the pathophysiology underlying PD, and suggests that iron accumulation precedes neuromelanin depletion during the prodromal phase.

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Section: Introductionmentioning

confidence: 99%

Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease

Bashat

Thaler

Shacham

et al. 2022

npj Parkinsons Dis.

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“…Intra-and inter-observer correlation coe cients (ICCs) were employed to quantify observer agreement in the extraction of radiomics features from the VOIs. [24] Then ROIs on SWI were linearly registered to individual T1WI and the quality of the registrations was manually assessed. Co-registration procedure was performed using advanced normalization tools (ANTs).…”

Section: Roi Segmentation and Image Processingmentioning

confidence: 99%

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy

Bu,

Pang,

et al. 2023

Preprint

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Objectives This study aims to investigate the potential of radiomics with multiple parameters from conventional T1 weighted imaging (T1WI) and susceptibility weighted imaging (SWI) in distinguishing between idiopathic Parkinson's disease (IPD) and multiple system atrophy (MSA).Methods A total of 201 participants, including 57 patients with IPD, 74 with MSA, and 70 healthy control (HCs) individuals, underwent T1WI and SWI scans. From the 12 subcortical nuclei (e.g. red nucleus, substantia nigra, subthalamic nucleus, putamen, globus pallidus, and caudate nucleus), 2640 radiomic features were extracted from both T1WI and SWI scans. Three classification models - logistic regression (LR), support vector machine (SVM), and light gradient boosting machine (LGBM) - were used to distinguish between MSA and IPD, and MSA, IPD, and HC, respectively, based on T1WI features, SWI features, and a combination of T1WI and SWI features. Five-fold cross-validation was used to evaluate the performance of the models with metrics such as sensitivity, specificity, accuracy, and area under the receiver operating curve (AUC). During each fold, the ANOVA and least absolute shrinkage and selection operator (LASSO) methods were used to identify the most relevant subset of features for the model training process.Results The LGBM model trained by the features combination of T1WI and SWI exhibited the most outstanding differential performance in both the three-class classification task of MSA vs. PD vs. HC and the binary classification task of MSA vs. PD, with an accuracy of 0.814 and 0.854, and an AUC of 0.904 and 0.881, respectively. The texture-based differences (GLCM) of the SN and the shape-based differences of the GP were highly effective in discriminating between the three classes and two classes, respectively.Conclusions Radiomic features combining T1WI and SWI can achieve a satisfactory differential diagnosis for PD, MSA, and HC groups, as well as for PD and MSA groups, thus providing a useful tool for clinical decision-making based on routine MRI sequences.

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“…Parkinson’s disease (PD) is the second most common progressive neurodegenerative disease, affecting 1% of the population over 60 years ( Lin et al, 2020 ; Ren et al, 2021 ), and it is becoming more and more prevalent and associated with increased mortality ( Shu et al, 2021 ). The clinical symptoms of PD are heterogeneous, presenting a variety of motor symptoms (e.g., static tremor, bradykinesia, or rigidity) and non-motor symptoms (e.g., sensory and autonomic dysfunction, cognitive deficits, or disorders of mood) ( Kim et al, 2017 ; Amoroso et al, 2018 ; Lin et al, 2020 ; Sheng et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Shi

Zhang

Wang

et al. 2022

Front. Aging Neurosci.

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Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.

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Substantia Nigra Radiomics Feature Extraction of Parkinson’s Disease Based on Magnitude Images of Susceptibility-Weighted Imaging

Cited by 12 publications

References 23 publications

Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease

Neuromelanin and T2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson’s disease

Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy

Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

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