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Haliasos N, Giakoumettis D, Gnanaratnasingham P, Low HL, Misbahuddin A, Zikos P, Sakkalis V, Cleo S, Vakis A, Bisdas S. Personalizing Deep Brain Stimulation Therapy for Parkinson's Disease With Whole-Brain MRI Radiomics and Machine Learning. Cureus 2024; 16:e59915. [PMID: 38854362 PMCID: PMC11161197 DOI: 10.7759/cureus.59915] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/08/2024] [Indexed: 06/11/2024] Open
Abstract
Background Deep brain stimulation (DBS) is a well-recognised treatment for advanced Parkinson's disease (PD) patients. Structural brain alterations of the white matter can correlate with disease progression and act as a biomarker for DBS therapy outcomes. This study aims to develop a machine learning-driven predictive model for DBS patient selection using whole-brain white matter radiomics and common clinical variables. Methodology A total of 120 PD patients underwent DBS of the subthalamic nucleus. Their therapy effect was assessed at the one-year follow-up with the Unified Parkinson's Disease Rating Scale-part III (UPDRSIII) motor component. Radiomics analysis of whole-brain white matter was performed with PyRadiomics. The following machine learning methods were used: logistic regression (LR), support vector machine, naïve Bayes, K-nearest neighbours, and random forest (RF) to allow prediction of clinically meaningful UPRDSIII motor response before and after. Clinical variables were also added to the model to improve accuracy. Results The RF model showed the best performance on the final whole dataset with an area under the curve (AUC) of 0.99, accuracy of 0.95, sensitivity of 0.93, and specificity of 0.97. At the same time, the LR model showed an AUC of 0.93, accuracy of 0.88, sensitivity of 0.84, and specificity of 0.91. Conclusions Machine learning models can be used in clinical decision support tools which can deliver true personalised therapy recommendations for PD patients. Clinicians and engineers should choose between best-performing, less interpretable models vs. most interpretable, lesser-performing models. Larger clinical trials would allow to build trust among clinicians and patients to widely use these AI tools in the future.
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Affiliation(s)
- Nikolaos Haliasos
- Neurosurgery, Queen's Hospital, Romford, GBR
- Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University, London, GBR
- Health and Medical Sciences, The Alan Turing Institute for Data Science and Artificial Intelligence, London, GBR
| | | | | | | | | | | | - Vangelis Sakkalis
- Institute of Computer Science, Foundation for Research and Technology, Heraklion, GRC
| | - Spanaki Cleo
- Neurology, School of Medicine, University of Crete, Heraklion, GRC
| | - Antonios Vakis
- Neurosurgery, School of Medicine, University of Crete, Heraklion, GRC
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Mohammadi S, Ghaderi S. Parkinson's disease and Parkinsonism syndromes: Evaluating iron deposition in the putamen using magnetic susceptibility MRI techniques - A systematic review and literature analysis. Heliyon 2024; 10:e27950. [PMID: 38689949 PMCID: PMC11059419 DOI: 10.1016/j.heliyon.2024.e27950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 02/29/2024] [Accepted: 03/08/2024] [Indexed: 05/02/2024] Open
Abstract
Magnetic resonance imaging (MRI) techniques, such as quantitative susceptibility mapping (QSM) and susceptibility-weighted imaging (SWI), can detect iron deposition in the brain. Iron accumulation in the putamen (PUT) can contribute to the pathogenesis of Parkinson's disease (PD) and atypical Parkinsonian disorders. This systematic review aimed to synthesize evidence on iron deposition in the PUT assessed by MRI susceptibility techniques in PD and Parkinsonism syndromes. The PubMed and Scopus databases were searched for relevant studies. Thirty-four studies from January 2007 to October 2023 that used QSM, SWI, or other MRI susceptibility methods to measure putaminal iron in PD, progressive supranuclear palsy (PSP), multiple system atrophy (MSA), and healthy controls (HCs) were included. Most studies have found increased putaminal iron levels in PD patients versus HCs based on higher quantitative susceptibility. Putaminal iron accumulation correlates with worse motor scores and cognitive decline in patients with PD. Evidence regarding differences in susceptibility between PD and atypical Parkinsonism is emerging, with several studies showing greater putaminal iron deposition in PSP and MSA than in PD patients. Alterations in putaminal iron levels help to distinguish these disorders from PD. Increased putaminal iron levels appear to be associated with increased disease severity and progression. Thus, magnetic susceptibility MRI techniques can detect abnormal iron accumulation in the PUT of patients with Parkinsonism. Moreover, quantifying putaminal susceptibility may serve as an MRI biomarker to monitor motor and cognitive changes in PD and aid in the differential diagnosis of Parkinsonian disorders.
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Affiliation(s)
- Sana Mohammadi
- Department of Medical Sciences, School of Medicine, Iran University of Medical Sciences, Tehran, Iran
| | - Sadegh Ghaderi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
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Tang X, Guo R, Zhang C, Zhuang X, Qian X. A Causality-Driven Graph Convolutional Network for Postural Abnormality Diagnosis in Parkinsonians. IEEE TRANSACTIONS ON MEDICAL IMAGING 2023; 42:3752-3763. [PMID: 37581959 DOI: 10.1109/tmi.2023.3305378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/17/2023]
Abstract
Abnormal posture is a common movement disorder in the progress of Parkinson's disease (PD), and this abnormality can increase the risk of falls or even disabilities. The conventional assessment approach depends on the judgment of well-trained experts via canonical scales. However, this approach requires extensive clinical expertise and is highly subjective. Considering the potential of quantitative susceptibility mapping (QSM) in PD diagnosis, this study explored the QSM-based method for the automated classification between PD patients with and without postural abnormalities. Nevertheless, a major challenge is that unstable non-causal features typically lead to less reliable performance. Therefore, we propose a causality-driven graph-convolutional-network framework based on multi-instance learning, where performance stability is enhanced through the invariant prediction principle and causal interventions. Specifically, we adopt an intervention strategy that combines a non-causal intervenor with causal prediction. A stability constraint is proposed to ensure robust integrated prediction under different interventions. Moreover, an intra-class homogeneity constraint is enforced for each individually-learned causality scoring module to promote the extraction of group-level general features, and hence achieve a balance between subject-specific and group-level features. The proposed method demonstrated promising performance through extensive experiments on a real clinical dataset. Also, the features extracted by our method coincide with those reported in previous medical studies on PD posture abnormalities. In general, our work provides a clinically-valuable approach for automated, objective, and reliable diagnosis of postural abnormalities in Parkinsonians. Our source code is publicly available at https://github.com/SJTUBME-QianLab/CausalGCN-PDPA.
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Andrews L, Keller SS, Osman-Farah J, Macerollo A. A structural magnetic resonance imaging review of clinical motor outcomes from deep brain stimulation in movement disorders. Brain Commun 2023; 5:fcad171. [PMID: 37304793 PMCID: PMC10257440 DOI: 10.1093/braincomms/fcad171] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2022] [Revised: 04/05/2023] [Accepted: 05/30/2023] [Indexed: 06/13/2023] Open
Abstract
Patients with movement disorders treated by deep brain stimulation do not always achieve successful therapeutic alleviation of motor symptoms, even in cases where surgery is without complications. Magnetic resonance imaging (MRI) offers methods to investigate structural brain-related factors that may be predictive of clinical motor outcomes. This review aimed to identify features which have been associated with variability in clinical post-operative motor outcomes in patients with Parkinson's disease, dystonia, and essential tremor from structural MRI modalities. We performed a literature search for articles published between 1 January 2000 and 1 April 2022 and identified 5197 articles. Following screening through our inclusion criteria, we identified 60 total studies (39 = Parkinson's disease, 11 = dystonia syndromes and 10 = essential tremor). The review captured a range of structural MRI methods and analysis techniques used to identify factors related to clinical post-operative motor outcomes from deep brain stimulation. Morphometric markers, including volume and cortical thickness were commonly identified in studies focused on patients with Parkinson's disease and dystonia syndromes. Reduced metrics in basal ganglia, sensorimotor and frontal regions showed frequent associations with reduced motor outcomes. Increased structural connectivity to subcortical nuclei, sensorimotor and frontal regions was also associated with greater motor outcomes. In patients with tremor, increased structural connectivity to the cerebellum and cortical motor regions showed high prevalence across studies for greater clinical motor outcomes. In addition, we highlight conceptual issues for studies assessing clinical response with structural MRI and discuss future approaches towards optimizing individualized therapeutic benefits. Although quantitative MRI markers are in their infancy for clinical purposes in movement disorder treatments, structural features obtained from MRI offer the powerful potential to identify candidates who are more likely to benefit from deep brain stimulation and provide insight into the complexity of disorder pathophysiology.
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Affiliation(s)
- Luke Andrews
- Correspondence to: Luke Andrews The BRAIN Lab, University of Liverpool Cancer Research Centre 200 London Rd, Liverpool L3 9TA, United Kingdom E-mail:
| | - Simon S Keller
- The Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool L3 9TA, UK
| | - Jibril Osman-Farah
- Department of Neurology and Neurosurgery, The Walton Centre NHS Foundation Trust, Liverpool L97LJ, UK
| | - Antonella Macerollo
- Correspondence may also be sent to: Antonella Macerollo. The Walton Centre NHS Trust, Lower Lane Liverpool L9 7LJ, United Kingdom E-mail:
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Zhao W, Yang C, Tong R, Chen L, Chen M, Gillen KM, Li G, Ma C, Wang Y, Wu X, Li J. Relationship Between Iron Distribution in Deep Gray Matter Nuclei Measured by Quantitative Susceptibility Mapping and Motor Outcome After Deep Brain Stimulation in Patients With Parkinson's Disease. J Magn Reson Imaging 2023. [PMID: 36594513 DOI: 10.1002/jmri.28574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 12/07/2022] [Accepted: 12/07/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Deep brain stimulation (DBS) of the subthalamic nucleus (STN) improves motor deficits in advanced Parkinson's disease (PD) patients, but the degree of motor improvement varies across individuals. PD pathology involves the changes of iron spatial distribution in the deep gray matter nuclei. PURPOSE To explore the relationship between the iron spatial distribution and motor improvement among PD patients who underwent STN-DBS surgery in three regions: substantia nigra (SN), STN, and dentate nucleus (DN). STUDY TYPE Prospective. SUBJECTS Forty PD patients (49.7 ± 8.8 years, 22 males/18 females) who underwent bilateral STN-DBS. FIELD STRENGTH/SEQUENCE A 3 T preoperative three-dimensional spoiled bipolar-readout multi-echo gradient recalled echo and two-dimensional fast spin echo sequences. ASSESSMENT Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS III) scores were assessed 2-3 days before and 6 months after STN-DBS. The first- and second-order texture features in regions of interest were measured on susceptibility maps. STATISTICAL TESTS Intraclass correlation coefficient was used to determine the consistency of the region of interest volumes delineated by the two raters. Pearson or Spearman's correlation coefficients were used to assess the relationship between motor improvement after DBS and texture features. A P-value <0.05 was considered statistically significant. RESULTS MDS-UPDRS III scores were reduced by 59.9% after STN-DBS in 40 PD patients. Motor improvement correlated with second-order texture parameters in the SN including angular second moment (r = -0.449), correlation (rho = 0.326), sum of squares (r = 0.402), sum of entropy (rho = 0.421), and entropy (r = 0.410). Additionally, DBS outcome negatively correlated with mean susceptibility values in the DN (r = -0.400). DATA CONCLUSION PD patients with a more homogeneous iron distribution throughout the SN or a higher iron concentration in the DN responded worse to STN-DBS. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Weiwei Zhao
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Chunhui Yang
- Department of Neurosurgery, Changhai Hospital, Shanghai, China
| | - Rui Tong
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Luguang Chen
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Mengying Chen
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Kelly M Gillen
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Gaiying Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital, Shanghai, China
| | - Yi Wang
- Department of Radiology, Weill Medical College of Cornell University, New York, New York, USA
| | - Xi Wu
- Department of Neurosurgery, Changhai Hospital, Shanghai, China
| | - Jianqi Li
- Shanghai Key Laboratory of Magnetic Resonance, School of Physics and Electronic Science, East China Normal University, Shanghai, China
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Saudargiene A, Radziunas A, Dainauskas JJ, Kucinskas V, Vaitkiene P, Pranckeviciene A, Laucius O, Tamasauskas A, Deltuva V. Radiomic features of amygdala nuclei and hippocampus subfields help to predict subthalamic deep brain stimulation motor outcomes for Parkinson‘s disease patients. Front Neurosci 2022; 16:1028996. [PMID: 36312034 PMCID: PMC9606748 DOI: 10.3389/fnins.2022.1028996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background and purposeThe aim of the study is to predict the subthalamic nucleus (STN) deep brain stimulation (DBS) outcomes for Parkinson’s disease (PD) patients using the radiomic features extracted from pre-operative magnetic resonance images (MRI).MethodsThe study included 34 PD patients who underwent DBS implantation in the STN. Five patients (15%) showed poor DBS motor outcome. All together 9 amygdalar nuclei and 12 hippocampus subfields were segmented using Freesurfer 7.0 pipeline from pre-operative MRI images. Furthermore, PyRadiomics platform was used to extract 120 radiomic features for each nuclei and subfield resulting in 5,040 features. Minimum Redundancy Maximum Relevance (mRMR) feature selection method was employed to reduce the number of features to 20, and 8 machine learning methods (regularized binary logistic regression (LR), decision tree classifier (DT), linear discriminant analysis (LDA), naive Bayes classifier (NB), kernel support vector machine (SVM), deep feed-forward neural network (DNN), one-class support vector machine (OC-SVM), feed-forward neural network-based autoencoder for anomaly detection (DNN-A)) were applied to build the models for poor vs. good and very good STN-DBS motor outcome prediction.ResultsThe highest mean prediction accuracy was obtained using regularized LR (96.65 ± 7.24%, AUC 0.98 ± 0.06) and DNN (87.25 ± 14.80%, AUC 0.87 ± 0.18).ConclusionThe results show the potential power of the radiomic features extracted from hippocampus and amygdala MRI in the prediction of STN-DBS motor outcomes for PD patients.
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Affiliation(s)
- Ausra Saudargiene
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- *Correspondence: Ausra Saudargiene,
| | - Andrius Radziunas
- Department of Neurosurgery, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Justinas J. Dainauskas
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Vytautas Kucinskas
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Paulina Vaitkiene
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Aiste Pranckeviciene
- Department of Health Psychology, Faculty of Public Health, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Neurology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Ovidijus Laucius
- Department of Neurology, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Arimantas Tamasauskas
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Neurosurgery, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Vytenis Deltuva
- Neuroscience Institute, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
- Department of Neurosurgery, Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
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