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Wang T, Wang Y, Zhu H, Liu Z, Chen YC, Wang L, Duan S, Yin X, Jiang L. Automatic substantia nigra segmentation with Swin-Unet in susceptibility- and T2-weighted imaging: application to Parkinson disease diagnosis. Quant Imaging Med Surg 2024; 14:6337-6351. [PMID: 39281181 PMCID: PMC11400694 DOI: 10.21037/qims-24-27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2024] [Accepted: 07/15/2024] [Indexed: 09/18/2024]
Abstract
Background Accurately distinguishing between Parkinson disease (PD) and healthy controls (HCs) through reliable imaging method is crucial for appropriate therapeutic intervention. However, PD diagnosis is hindered by the subjective nature of the evaluation. We aimed to develop an automatic deep-learning method that can segment the substantia nigra areas on susceptibility-weighted imaging (SWI) and T2-weighted imaging (T2WI) and further differentiate patients with PD from HCs using a machine learning algorithm. Methods Magnetic resonance imaging (MRI) data from 83 patients with PD and 83 age- and sex-matched HCs were obtained on the same 3.0-T MRI scanner. A deep learning method with Swin-Unet was developed to segment volumes of interest (VOIs) on SWI and then map the VOIs on SWI to the corresponding T2WI; features were then extracted from the VOIs on SWI and T2WI. Three machine learning models were developed and compared to differentiate those with PD from HCs. Results Swin-Unet achieved a better Dice coefficient than did U-Net in SWI segmentation (0.832 vs. 0.712). Machine learning models outperformed visual analysis (P>0.05), and logistic regression (LR) achieved the best performance [area under the curve (AUC) ≥0.819] and the most stable (relative standard deviations in AUC ≤0.05). The test results showed that the AUC of the LR model based on SWI segmentation was 0.894 while that of the LR model based on T2WI segmentation was 0.876. There was no significant difference in VOIs based on manual labeling or automatic segmentation across T2WI, SWI, or a combination of the two (P>0.05). The AUCs of the LR model based on automatic segmentation were close to those of the model based on manual labeling (P>0.05). Conclusions Our approach could provide a powerful and useful method for automatically and rapidly diagnosing PD in the clinic with only T2WI.
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Affiliation(s)
- Tongxing Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Yajing Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Haichen Zhu
- Lab of Image Science and Technology, Key Laboratory of Computer Network and Information Integration (Ministry of Education), School of Computer Science and Engineering, Southeast University, Nanjing, China
| | - Zhen Liu
- Department of Radiology, The Affiliated ChuZhou Hospital of AnHui Medical University, Chuzhou, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Liwei Wang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Shaofeng Duan
- GE HealthCare, Precision Health Institution, Shanghai, China
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
| | - Liang Jiang
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, Nanjing, China
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Chen H, Liu X, Luo X, Fu J, Zhou K, Wang N, Li Y, Geng D. An automated hybrid approach via deep learning and radiomics focused on the midbrain and substantia nigra to detect early-stage Parkinson's disease. Front Aging Neurosci 2024; 16:1397896. [PMID: 38832074 PMCID: PMC11144908 DOI: 10.3389/fnagi.2024.1397896] [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: 03/11/2024] [Accepted: 05/01/2024] [Indexed: 06/05/2024] Open
Abstract
Objectives The altered neuromelanin in substantia nigra pars compacta (SNpc) is a valuable biomarker in the detection of early-stage Parkinson's disease (EPD). Diagnosis via visual inspection or single radiomics based method is challenging. Thus, we proposed a novel hybrid model that integrates radiomics and deep learning methodologies to automatically detect EPD based on neuromelanin-sensitive MRI, namely short-echo-time Magnitude (setMag) reconstructed from quantitative susceptibility mapping (QSM). Methods In our study, we collected QSM images including 73 EPD patients and 65 healthy controls, which were stratified into training-validation and independent test sets with an 8:2 ratio. Twenty-four participants from another center were included as the external validation set. Our framework began with the detection of the brainstem utilizing YOLO-v5. Subsequently, a modified LeNet was applied to obtain deep learning features. Meanwhile, 1781 radiomics features were extracted, and 10 features were retained after filtering. Finally, the classified models based on radiomics features, deep learning features, and the hybrid of both were established through machine learning algorithms, respectively. The performance was mainly evaluated using accuracy, net reclassification improvement (NRI), and integrated discrimination improvement (IDI). The saliency map was used to visualize the model. Results The hybrid feature-based support vector machine (SVM) model showed the best performance, achieving ACC of 96.3 and 95.8% in the independent test set and external validation set, respectively. The model established by hybrid features outperformed the one radiomics feature-based (NRI: 0.245, IDI: 0.112). Furthermore, the saliency map showed that the bilateral "swallow tail" sign region was significant for classification. Conclusion The integration of deep learning and radiomic features presents a potent strategy for the computer-aided diagnosis of EPD. This study not only validates the accuracy of our proposed model but also underscores its interpretability, evidenced by differential significance across various anatomical sites.
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Affiliation(s)
- Hongyi Chen
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Xueling Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Xiao Luo
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Junyan Fu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Kun Zhou
- Academy for Engineering and Technology, Fudan University, Shanghai, China
| | - Na Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yuxin Li
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
| | - Daoying Geng
- Academy for Engineering and Technology, Fudan University, Shanghai, China
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
- Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai, China
- China Center for Shanghai Intelligent Imaging for Critical Brain Diseases Engineering and Technology Research, Shanghai, China
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Zhang Q, Wang H, Shi Y, Li W. White matter biomarker for predicting de novo Parkinson's disease using tract-based spatial statistics: a machine learning-based model. Quant Imaging Med Surg 2024; 14:3086-3106. [PMID: 38617147 PMCID: PMC11007501 DOI: 10.21037/qims-23-1478] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 03/07/2024] [Indexed: 04/16/2024]
Abstract
Background Parkinson's disease (PD) is an irreversible, chronic degenerative disease of the central nervous system, potentially associated with cerebral white matter (WM) lesions. Investigating the microstructural alterations within the WM in the early stages of PD can help to identify the disease early and enable intervention to reduce the associated serious threats to health. Methods This study selected 227 cases from the Parkinson's Progression Markers Initiative (PPMI) database, including 152 de novo PD patients and 75 normal controls (NC). Whole-brain voxel analysis of the WM was performed using the tract-based spatial statistics (TBSS) method. The WM regions with statistically significant differences (P<0.05) between the PD and NC groups were identified and used as masks. The mask was applied to each case's fractional anisotropy (FA) image to extract voxel values as feature vectors. Geometric dimensionality reduction was then applied to eliminate redundant values in the feature vectors. Subsequently, the cases were randomly divided into a training group (158 cases, including 103 PD patients and 55 NC) and a test group (69 cases, including 49 PD patients and 20 NC). The least absolute shrinkage and selection operator (LASSO) regression algorithm was employed to extract the minimal set of relevant features, then the random forest (RF) algorithm was utilized for classification using 5-fold cross validation. The resulting model was further integrated with clinical factors to create a comprehensive prediction model. Results In comparison to the NC group, the FA values in PD patients exhibited a statistically significant decrease (P<0.05), indicating the presence of widespread WM lesions across multiple brain regions. Moreover, the PD prediction model, constructed based on these WM lesion regions, yielded prediction accuracy (ACC) and area under the receiver operating characteristic (ROC) curve (AUC) values of 0.778 and 0.865 in the validation set, and 0.783 and 0.831 in the test set, respectively. Furthermore, the performance of the integrated model showed some improvement, with ACC and AUC values in the test set reaching 0.804 and 0.844, respectively. Conclusions The quantitative calculation of WM lesion area on FA images using the TBSS method can serve as a neuroimaging biomarker for diagnosing and predicting early PD at the individual level. When integrated with clinical variables, the predictive performance improves.
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Affiliation(s)
- Qi Zhang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Haoran Wang
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Yonghong Shi
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
| | - Wensheng Li
- Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China
- Department of Human Anatomy and Histoembryology, School of Basic Medical Science, Fudan University, Shanghai, China
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Bu S, Pang H, Li X, Zhao M, Wang J, Liu Y, Yu H. Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson's disease and multiple system atrophy. BMC Med Imaging 2023; 23:204. [PMID: 38066432 PMCID: PMC10709839 DOI: 10.1186/s12880-023-01169-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 12/01/2023] [Indexed: 12/18/2023] Open
Abstract
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 (PD) and multiple system atrophy (MSA). METHODS A total of 201 participants, including 57 patients with PD, 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 PD, as well as among MSA, PD, and HC. These classifications were based on features extracted from T1WI, SWI, and a combination of T1WI and SWI. 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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Affiliation(s)
- Shuting Bu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Huize Pang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Xiaolu Li
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Mengwan Zhao
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Juzhou Wang
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Yu Liu
- Department of Radiology, the First Hospital of China Medical University, Shenyang, 110001, China
| | - Hongmei Yu
- Department of Neurology, the First Hospital of China Medical University, 155 Nanjing North Street, Shenyang, Liaoning, 110001, PR China.
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Gore S, Dhole A, Kumbhar S, Jagtap J. Radiomics for Parkinson's disease classification using advanced texture-based biomarkers. MethodsX 2023; 11:102359. [PMID: 37791007 PMCID: PMC10543659 DOI: 10.1016/j.mex.2023.102359] [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: 08/21/2023] [Accepted: 08/30/2023] [Indexed: 10/05/2023] Open
Abstract
Parkinson's disease (PD) is one of the neurodegenerative diseases and its manual diagnosis leads to time-consuming process. MRI-based computer-aided diagnosis helps medical experts to diagnose PD more precisely and fast. Texture-based radiomic analysis is carried out on 3D MRI scans of T1 weighted and resting-state modalities. 43 subjects from Neurocon and 40 subjects from Tao-Wu dataset were examined, which consisted of 36 scans of healthy controls and 47 scans of Parkinson's patients. Total 360 2D MRI images are selected among around 17000 slices of T1-weighted and resting scans of selected 72 subjects. Local binary pattern (LBP) method was applied with custom variants to acquire advanced textural biomarkers from MRI images. LBP histogram helped to learn discriminative local patterns to detect and classify Parkinson's disease. Using recursive feature elimination, data dimensions of around 150-300 LBP histogram features were reduced to 13-21 most significant features based on score, and important features were analysed using SVM and random forest algorithms. Variant-I of LBP has performed well with highest test accuracy of 83.33%, precision of 84.62%, recall of 91.67%, and f1-score of 88%. Classification accuracies were obtained from 61.11% to 83.33% and AUC-ROC values range from 0.43 to 0.86 using four variants of LBP.•Parkinson's classification is carried out using an advanced biomedical texture feature. Texture extraction using four variants of uniform, rotation invariant LBP method is performed for radiomic analysis of Parkinson's disorder.•Proposed method with support vector machine classifier is experimented and an accuracy of 83.33% is achieved with 10-fold cross validation for detection of Parkinson's patients from MRI-based radiomic analysis.•The proposed predictive model has proved the potential of textures of extended version of LBP, which have demonstrated subtle variations in local appearance for Parkinson's detection.
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Affiliation(s)
- Sonal Gore
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Aniket Dhole
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Shrishail Kumbhar
- Pimpri Chinchwad College of Engineering, Nigdi, Pune, Maharashtra, India
| | - Jayant Jagtap
- Symbiosis Institute of Technology (SIT), Symbiosis International (Deemed University), (SIU), Lavale, Pune, Maharashtra, India
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Bian J, Wang X, Hao W, Zhang G, Wang Y. The differential diagnosis value of radiomics-based machine learning in Parkinson's disease: a systematic review and meta-analysis. Front Aging Neurosci 2023; 15:1199826. [PMID: 37484694 PMCID: PMC10357514 DOI: 10.3389/fnagi.2023.1199826] [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: 04/04/2023] [Accepted: 06/21/2023] [Indexed: 07/25/2023] Open
Abstract
Background In recent years, radiomics has been increasingly utilized for the differential diagnosis of Parkinson's disease (PD). However, the application of radiomics in PD diagnosis still lacks sufficient evidence-based support. To address this gap, we carried out a systematic review and meta-analysis to evaluate the diagnostic value of radiomics-based machine learning (ML) for PD. Methods We systematically searched Embase, Cochrane, PubMed, and Web of Science databases as of November 14, 2022. The radiomics quality assessment scale (RQS) was used to evaluate the quality of the included studies. The outcome measures were the c-index, which reflects the overall accuracy of the model, as well as sensitivity and specificity. During this meta-analysis, we discussed the differential diagnostic value of radiomics-based ML for Parkinson's disease and various atypical parkinsonism syndromes (APS). Results Twenty-eight articles with a total of 6,057 participants were included. The mean RQS score for all included articles was 10.64, with a relative score of 29.56%. The pooled c-index, sensitivity, and specificity of radiomics for predicting PD were 0.862 (95% CI: 0.833-0.891), 0.91 (95% CI: 0.86-0.94), and 0.93 (95% CI: 0.87-0.96) in the training set, and 0.871 (95% CI: 0.853-0.890), 0.86 (95% CI: 0.81-0.89), and 0.87 (95% CI: 0.83-0.91) in the validation set, respectively. Additionally, the pooled c-index, sensitivity, and specificity of radiomics for differentiating PD from APS were 0.866 (95% CI: 0.843-0.889), 0.86 (95% CI: 0.84-0.88), and 0.80 (95% CI: 0.75-0.84) in the training set, and 0.879 (95% CI: 0.854-0.903), 0.87 (95% CI: 0.85-0.89), and 0.82 (95% CI: 0.77-0.86) in the validation set, respectively. Conclusion Radiomics-based ML can serve as a potential tool for PD diagnosis. Moreover, it has an excellent performance in distinguishing Parkinson's disease from APS. The support vector machine (SVM) model exhibits excellent robustness when the number of samples is relatively abundant. However, due to the diverse implementation process of radiomics, it is expected that more large-scale, multi-class image data can be included to develop radiomics intelligent tools with broader applicability, promoting the application and development of radiomics in the diagnosis and prediction of Parkinson's disease and related fields. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=383197, identifier ID: CRD42022383197.
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Affiliation(s)
- Jiaxiang Bian
- School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Xiaoyang Wang
- School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Wei Hao
- School of Clinical Medicine, Weifang Medical University, Weifang, China
| | - Guangjian Zhang
- Department of Neurosurgery, Weifang People’s Hospital, Weifang, China
| | - Yuting Wang
- Department of Neurosurgery, Weifang People’s Hospital, Weifang, China
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Ben Bashat D, Thaler A, Lerman Shacham H, Even-Sapir E, Hutchison M, Evans KC, Orr-Urterger A, Cedarbaum JM, Droby A, Giladi N, Mirelman A, Artzi M. Neuromelanin and T 2*-MRI for the assessment of genetically at-risk, prodromal, and symptomatic Parkinson's disease. NPJ Parkinsons Dis 2022; 8:139. [PMID: 36271084 PMCID: PMC9586960 DOI: 10.1038/s41531-022-00405-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2022] [Accepted: 09/30/2022] [Indexed: 11/23/2022] Open
Abstract
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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Affiliation(s)
- Dafna Ben Bashat
- grid.413449.f0000 0001 0518 6922Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Avner Thaler
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Hedva Lerman Shacham
- grid.413449.f0000 0001 0518 6922Department of Nuclear Medicine, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Einat Even-Sapir
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Department of Nuclear Medicine, Tel-Aviv Sourasky Medical Center, Tel Aviv, Israel
| | | | | | - Avi Orr-Urterger
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Genomic Research Laboratory for Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Jesse M. Cedarbaum
- Coeruleus Clinical Sciences LLC, Woodbridge, CT USA ,grid.47100.320000000419368710Yale University School of Medicine, New Haven, CT USA
| | - Amgad Droby
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Nir Giladi
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Anat Mirelman
- grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel ,grid.413449.f0000 0001 0518 6922Laboratory of Early Markers Of Neurodegeneration (LEMON), Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Moran Artzi
- grid.413449.f0000 0001 0518 6922Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel ,grid.12136.370000 0004 1937 0546Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
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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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Ren Q, An P, Jin K, Xia X, Huang Z, Xu J, Huang C, Jiang Q, Meng X. A Pilot Study of Radiomic Based on Routine CT Reflecting Difference of Cerebral Hemispheric Perfusion. Front Neurosci 2022; 16:851720. [PMID: 35431785 PMCID: PMC9009332 DOI: 10.3389/fnins.2022.851720] [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: 01/10/2022] [Accepted: 03/03/2022] [Indexed: 11/30/2022] Open
Abstract
Background To explore the effectiveness of radiomics features based on routine CT to reflect the difference of cerebral hemispheric perfusion. Methods We retrospectively recruited 52 patients with severe stenosis or occlusion in the unilateral middle cerebral artery (MCA), and brain CT perfusion showed an MCA area with deficit perfusion. Radiomics features were extracted from the stenosis side and contralateral of the MCA area based on precontrast CT. Two different region of interest drawing methods were applied. Then the patients were randomly grouped into training and testing sets by the ratio of 8:2. In the training set, ANOVA and the Elastic Net Regression with fivefold cross-validation were conducted to filter and choose the optimized features. Moreover, different machine learning models were built. In the testing set, the area under the receiver operating characteristic (AUC) curve, calibration, and clinical utility were applied to evaluate the predictive performance of the models. Results The logistic regression (LR) for the triangle-contour method and artificial neural network (ANN) for the semiautomatic-contour method were chosen as radiomics models for their good prediction efficacy in the training phase (AUC = 0.869, 0.873) and the validation phase (AUC = 0.793, 0.799). The radiomics algorithms of the triangle-contour and semiautomatic-contour method were implemented in the whole training set (AUC = 0.870, 0.867) and were evaluated in the testing set (AUC = 0.760, 0.802). According to the optimal cutoff value, these two methods can classify the vascular stenosis side class and normal side class. Conclusion Radiomic predictive feature based on precontrast CT image could reflect the difference of cerebral hemispheric perfusion to some extent.
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Affiliation(s)
- Qingguo Ren
- Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Panpan An
- Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Ke Jin
- Deepwise AI Lab, Beijing Deepwise and League of PHD Technology Co., Ltd., Beijing, China
| | - Xiaona Xia
- Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Zhaodi Huang
- Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Jingxu Xu
- Deepwise AI Lab, Beijing Deepwise and League of PHD Technology Co., Ltd., Beijing, China
| | - Chencui Huang
- Deepwise AI Lab, Beijing Deepwise and League of PHD Technology Co., Ltd., Beijing, China
| | - Qingjun Jiang
- Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Qingdao, China
| | - Xiangshui Meng
- Radiology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Qingdao, China
- *Correspondence: Xiangshui Meng,
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Shi D, Zhang H, Wang G, Wang S, Yao X, Li Y, Guo Q, Zheng S, Ren K. Machine Learning for Detecting Parkinson's Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis. Front Aging Neurosci 2022; 14:806828. [PMID: 35309885 PMCID: PMC8928361 DOI: 10.3389/fnagi.2022.806828] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 01/19/2022] [Indexed: 12/03/2022] Open
Abstract
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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Affiliation(s)
- Dafa Shi
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Haoran Zhang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Guangsong Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Siyuan Wang
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Xiang Yao
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yanfei Li
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Qiu Guo
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Shuang Zheng
- School of Medicine, Xiamen University, Xiamen, China
| | - Ke Ren
- Department of Radiology, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- Xiamen Key Laboratory for Endocrine-Related Cancer Precision Medicine, Xiang’an Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
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