1
|
Shimozono T, Shiiba T, Takano K. Radiomics score derived from T1-w/T2-w ratio image can predict motor symptom progression in Parkinson's disease. Eur Radiol 2024:10.1007/s00330-024-10886-2. [PMID: 38958697 DOI: 10.1007/s00330-024-10886-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2023] [Revised: 04/08/2024] [Accepted: 04/26/2024] [Indexed: 07/04/2024]
Abstract
OBJECTIVES To clarify the association between a radiomics score (Rad-score) derived from T1-weighted signal intensity to T2-weighted signal intensity (T1-w/T2-w) ratio images and the progression of motor symptoms in Parkinson's disease (PD). MATERIALS AND METHODS This retrospective study included patients with PD enrolled in the Parkinson's Progression Markers Initiative. The Movement Disorders Society-Unified Parkinson's Disease Rating Scale Part III score ≥ 33 and/or Hoehn and Yahr stage ≥ 3 indicated motor function decline. The Rad-score was constructed using radiomics features extracted from T1-w/T2-w ratio images. The Kaplan-Meier analysis and Cox regression analyses were used to assess the time differences in motor function decline between the high and low Rad-score groups. RESULTS A total of 171 patients with PD were divided into training (n = 101, mean age at baseline, 61.6 ± 9.3 years) and testing (n = 70, mean age at baseline, 61.6 ± 10 years). The patients in the high Rad-score group had a shorter time to motor function decline than those in the low Rad-score group in the training dataset (log-rank test, p < 0.001) and testing dataset (log-rank test, p < 0.001). The multivariate Cox regression using the Rad-score and clinical factors revealed a significant association between the Rad-score and motor function decline in the training dataset (HR = 2.368, 95%CI:1.423-3.943, p < 0.001) and testing dataset (HR = 2.931, 95%CI:1.472-5.837, p = 0.002). CONCLUSION Rad-scores based on radiomics features derived from T1-w/T2-w ratio images were associated with the progression of motor symptoms in PD. CLINICAL RELEVANCE STATEMENT The radiomics score derived from the T1-weighted/T2-weighted ratio images offers a predictive tool for assessing the progression of motor symptom in patients with PD. KEY POINTS Radiomics score derived from T1-weighted/T2-weighted ratio images is correlated with the motor symptoms of Parkinson's disease. A high radiomics score correlated with faster motor function decline in patients with Parkinson's disease. The proposed radiomics score offers predictive insight into the progression of motor symptoms of Parkinson's disease.
Collapse
Affiliation(s)
- Takuya Shimozono
- Department of Neuroimaging and Brain Science, Major in Health Science, Graduate School of Health Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| | - Takuro Shiiba
- Department of Molecular Imaging, Clinical Collaboration Unit, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan.
| | - Kazuki Takano
- Department of Molecular Imaging, Clinical Collaboration Unit, School of Medical Sciences, Fujita Health University, 1-98, Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan
| |
Collapse
|
2
|
Jian Y, Peng J, Wang W, Hu T, Wang J, Shi H, Li X, Chen J, Xu Y, Shao Y, Song Q, Shu Z. Prediction of cognitive decline in Parkinson's disease based on MRI radiomics and clinical features: A multicenter study. CNS Neurosci Ther 2024; 30:e14789. [PMID: 38923776 PMCID: PMC11196371 DOI: 10.1111/cns.14789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/25/2024] [Accepted: 05/11/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE To develop and validate a multimodal combinatorial model based on whole-brain magnetic resonance imaging (MRI) radiomic features for predicting cognitive decline in patients with Parkinson's disease (PD). METHODS This study included a total of 222 PD patients with normal baseline cognition, of whom 68 had cognitive impairment during a 4-year follow-up period. All patients underwent MRI scans, and radiomic features were extracted from the whole-brain MRI images of the training set, and dimensionality reduction was performed to construct a radiomics model. Subsequently, Screening predictive factors for cognitive decline from clinical features and then combining those with a radiomics model to construct a multimodal combinatorial model for predicting cognitive decline in PD patients. Evaluate the performance of the comprehensive model using the receiver-operating characteristic curve, confusion matrix, F1 score, and survival curve. In addition, the quantitative characteristics of diffusion tensor imaging (DTI) from corpus callosum were selected from 52 PD patients to further validate the clinical efficacy of the model. RESULTS The multimodal combinatorial model has good classification performance, with areas under the curve of 0.842, 0.829, and 0.860 in the training, test, and validation sets, respectively. Significant differences were observed in the number of cognitive decline PD patients and corpus callosum-related DTI parameters between the low-risk and high-risk groups distinguished by the model (p < 0.05). The survival curve analysis showed a statistically significant difference in the progression time of mild cognitive impairment between the low-risk and the high-risk groups. CONCLUSIONS The building of a multimodal combinatorial model based on radiomic features from MRI can predict cognitive decline in PD patients, thus providing adaptive strategies for clinical practice.
Collapse
Affiliation(s)
- Yongjie Jian
- Jinzhou Medical University Postgraduate Training Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College)HangzhouZhejiangChina
- Department of Radiology, Affiliated Hospital of Sichuan Nursing Vocational CollegeThe Third People's Hospital of Sichuan ProvinceChengduSichuanChina
| | - Jiaxuan Peng
- Jinzhou Medical University Postgraduate Training Base (Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College)HangzhouZhejiangChina
| | - Wei Wang
- Department of RadiologyThe First Affiliated Hospital of Chongqing Medical and Pharmaceutical CollegeChongqingChina
| | - Tao Hu
- Department of Neurology, Affiliated Hospital of Sichuan Nursing Vocational CollegeThe Third People's Hospital of Sichuan ProvinceChengduSichuanChina
| | - Jing Wang
- Department of Medical TechnologySichuan Nursing Vocational CollegeChengduSichuanChina
| | - Hui Shi
- Department of Radiology, Affiliated Hospital of Sichuan Nursing Vocational CollegeThe Third People's Hospital of Sichuan ProvinceChengduSichuanChina
| | - Xiaoyong Li
- Department of Radiology, Affiliated Hospital of Sichuan Nursing Vocational CollegeThe Third People's Hospital of Sichuan ProvinceChengduSichuanChina
| | - Jingfang Chen
- Department of Radiology, Affiliated Hospital of Sichuan Nursing Vocational CollegeThe Third People's Hospital of Sichuan ProvinceChengduSichuanChina
| | - Yuyun Xu
- Center for Rehabilitation Medicine, Department of RadiologyZhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical CollegeHangzhouZhejiangChina
| | - Yuan Shao
- Center for Rehabilitation Medicine, Department of RadiologyZhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical CollegeHangzhouZhejiangChina
| | - Qiaowei Song
- Center for Rehabilitation Medicine, Department of RadiologyZhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical CollegeHangzhouZhejiangChina
| | - Zhenyu Shu
- Center for Rehabilitation Medicine, Department of RadiologyZhejiang Provincial People's Hospital (Affiliated People's Hospital), Hangzhou Medical CollegeHangzhouZhejiangChina
| |
Collapse
|
3
|
Shah A, Prasad S, Indoria A, Pal PK, Saini J, Ingalhalikar M. Free water imaging in Parkinson's disease and atypical parkinsonian disorders. J Neurol 2024; 271:2521-2528. [PMID: 38265472 DOI: 10.1007/s00415-024-12184-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2023] [Revised: 12/28/2023] [Accepted: 12/30/2023] [Indexed: 01/25/2024]
Abstract
BACKGROUND Free water (FW)-corrected diffusion measures are more precise compared to standard diffusion measures. This study comprehensively evaluates FW and corrected diffusion metrics for whole brain white and deep gray matter (WM, GM) structures in patients with Parkinson's disease (PD), progressive supranuclear palsy (PSP) and multiple system atrophy (MSA) and attempts to ascertain the probable patterns of WM abnormalities. METHOD Diffusion MRI was acquired for subjects with PD (n = 133), MSA (n = 25), PSP (n = 30) and matched healthy controls (HC) (n = 99, n = 24, n = 12). Diffusion metrics of FA, MD, AD, RD were generated and FW, corrected FA maps were calculated using a bi-tensor model. TBSS was carried out at 5000 permutations with significance at p < 0.05. For GM, diffusivity maps were extracted from the basal ganglia, and analyzed at an FDR with p < 0.05. RESULTS Compared to HC, PD showed focal changes in FW. MSA showed changes in the cerebellum and brainstem, and PSP showed increase in FW involving supratentorial WM and midbrain. All three showed increased substantia nigra FW. MSA, PSP demonstrated increased FW in bilateral putamen. PD showed increased FW in left GP externa, and bilateral thalamus. Compared to HC, MSA had increased FW in bilateral GP interna, and left thalamic. PSP had an additional increase in FW of the right GP externa, right GP interna, and bilateral thalamus. CONCLUSION The present study demonstrated definitive differences in the patterns of FW alterations between PD and atypical parkinsonian disorders suggesting the possibility of whole brain FW maps being used as markers for diagnosis of these disorders.
Collapse
Affiliation(s)
- Apurva Shah
- Symbiosis Center for Medical Image Analysis and Symbiosis Institute of Technology, Symbiosis International University, Lavale, Mulshi, Pune, 412115, Maharashtra, India
| | - Shweta Prasad
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Hosur Road, Bengaluru, 560029, Karnataka, India
| | - Abhilasha Indoria
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Hosur Road, Bengaluru, 560029, Karnataka, India
| | - Pramod Kumar Pal
- Department of Neurology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Hosur Road, Bengaluru, 560029, Karnataka, India
| | - Jitender Saini
- Department of Neuroimaging and Interventional Radiology, National Institute of Mental Health and Neuro Sciences (NIMHANS), Hosur Road, Bengaluru, 560029, Karnataka, India
| | - Madhura Ingalhalikar
- Symbiosis Center for Medical Image Analysis and Symbiosis Institute of Technology, Symbiosis International University, Lavale, Mulshi, Pune, 412115, Maharashtra, India.
| |
Collapse
|
4
|
Li C, Hui D, Wu F, Xia Y, Shi F, Yang M, Zhang J, Peng C, Feng J, Li C. Automatic diagnosis of Parkinson's disease using artificial intelligence base on routine T1-weighted MRI. Front Med (Lausanne) 2024; 10:1303501. [PMID: 38249966 PMCID: PMC10797132 DOI: 10.3389/fmed.2023.1303501] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 12/08/2023] [Indexed: 01/23/2024] Open
Abstract
Background Parkinson's disease (PD) is the second most common neurodegenerative disease. An objective diagnosis method is urgently needed in clinical practice. In this study, deep learning and radiomics techniques were studied to automatically diagnose PD from healthy controls (HCs). Methods 155 PD patients and 154 HCs were randomly divided into a training set (246 patients) and a testing set (63 patients). The brain subregions identification and segmentation were automatically performed with a VB-net, and radiomics features of billateral thalamus, caudatum, putamen and pallidum were extracted. Five independent machine learning classifiers [Support Vector Machine (SVM), Stochastic gradient descent (SGD), random forest (RF), quadratic discriminant analysis (QDA) and decision tree (DT)] were trained on the training set, and validated on the testing. Delong test was used to compare the performance of different models. Results Our VB-net could automatically identify and segment the brain into 109 regions. 2,264 radiomics features were automatically extracted from the billateral thalamus, caudatum, putamen or pallidum of each patient. After four step of features dimensionality reduction, Delong tests showed that the SVM model based on combined features had the best performance, with AUCs of 0.988 (95% CI: 0.979 ~ 0.998, specificity = 91.1%, sensitivity =100%, accuracy = 89.4% and precision = 88.2%) and 0.976 (95% CI: 0.942 ~ 1.000, specificity = 100%, sensitivity = 87.1%, accuracy = 93.5% and precision = 88.6%) in the training set and testing set, respectively. Decision curve analysis showed that the clinical benefit of the line graph model was high. Conclusion The SVM model based on combined features could be used to diagnose PD with high accuracy. Our fully automatic model could rapidly process the MRI data and distinguish PD and HCs in one minute. It greatly improved the diagnostic efficiency and has a great potential value in clinical practice to help the early diagnosis of PD.
Collapse
Affiliation(s)
- Chang Li
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Dongming Hui
- Department of Radiology, Chongqing Western Hospital, Chongqing, China
| | - Faqi Wu
- Department of Medical Service, Yanzhuang Central Hospital of Gangcheng District, Chongqing, China
| | - Yuwei Xia
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China
| | - Feng Shi
- Department of Research and Development, Shanghai United Imaging Intelligence, Co., Ltd. Shanghai, China
| | - Mingguang Yang
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Jinrui Zhang
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chao Peng
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Junbang Feng
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| | - Chuanming Li
- Bioengineering College of Chongqing University, Chongqing University Central Hospital (Chongqing Emergency Medical Center), Chongqing, China
| |
Collapse
|
5
|
Sun J, Cong C, Li X, Zhou W, Xia R, Liu H, Wang Y, Xu Z, Chen X. Identification of Parkinson's disease and multiple system atrophy using multimodal PET/MRI radiomics. Eur Radiol 2024; 34:662-672. [PMID: 37535155 DOI: 10.1007/s00330-023-10003-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Revised: 05/08/2023] [Accepted: 06/06/2023] [Indexed: 08/04/2023]
Abstract
OBJECTIVES To construct a machine learning model for differentiating Parkinson's disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics. METHODS One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models. RESULTS The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model. CONCLUSIONS Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics. CLINICAL RELEVANCE STATEMENT This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance. KEY POINTS •Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA. •The radiomics-based nomogram was developed to differentiate between PD and MSA. •Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.
Collapse
Affiliation(s)
- Jinju Sun
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Chao Cong
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
- School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China
| | - Xinpeng Li
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China
| | - Weicheng Zhou
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Renxiang Xia
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | | | - Yi Wang
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China
| | - Zhiqiang Xu
- Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China.
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China.
- Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China.
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
Sun F, Lyu J, Jian S, Qin Y, Tang X. Accurate measurement of magnetic resonance parkinsonism index by a fully automatic and deep learning quantification pipeline. Eur Radiol 2023; 33:8844-8853. [PMID: 37480547 DOI: 10.1007/s00330-023-09979-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 05/23/2023] [Accepted: 05/25/2023] [Indexed: 07/24/2023]
Abstract
OBJECTIVES This study aims at a fully automatic pipeline for measuring the magnetic resonance parkinsonism index (MRPI) using deep learning methods. METHODS MRPI is defined as the product of the pons area to the midbrain area ratio and the middle cerebellar peduncle (MCP) width to the superior cerebellar peduncle (SCP) width ratio. In our proposed pipeline, we first used nnUNet to segment the brainstem and then employed HRNet to identify two key boundary points so as to sub-divide the whole brainstem into midbrain and pons. HRNet was also employed to predict the MCP endpoints for measuring the MCP width. Finally, we segmented the SCP on an oblique coronal plane and calculated its width. A total of 400 T1-weighted magnetic resonance images (MRIs) were used to train the nnUNet and HRNet models. Five-fold cross-validation was conducted to evaluate our proposed pipeline's performance on the training dataset. We also evaluated the performance of our proposed pipeline on three external datasets. Two of them had two raters manually measuring the MRPI values, providing insights into automatic accuracy versus inter-rater variability. RESULTS We obtained average absolute percentage errors (APEs) of 17.21%, 18.17%, 20.83%, and 22.83% on the training dataset and the three external validation datasets, while the inter-rater average APE measured on the first two external validation datasets was 11.31%. Our proposed pipeline significantly improved the MRPI quantification accuracy over a representative state-of-the-art traditional approach (p < 0.001). CONCLUSION The proposed automatic pipeline can accurately predict MRPI that is comparable with manual measurement. CLINICAL RELEVANCE STATEMENT This study presents an automated magnetic resonance parkinsonism index measurement tool that can analyze large amounts of magnetic resonance images, enhance the efficiency of Parkinsonism-Plus syndrome diagnosis, reduce the workload of clinicians, and minimize the impact of human factors on diagnosis. KEY POINTS • We propose an automatic pipeline for measuring the magnetic resonance parkinsonism index from magnetic resonance images. • The effectiveness of the proposed pipeline is successfully established on multiple datasets and comparisons with inter-rater measurements. • The proposed pipeline significantly outperforms a state-of-the-art quantification approach, being much closer to ground truth.
Collapse
Affiliation(s)
- Fuhai Sun
- Department of Electronic and Electrical Engineering, College of Engineering, Southern University of Science and Technology, Xili, Nanshan, Shenzhen, 518055, People's Republic of China
| | - Junyan Lyu
- Department of Electronic and Electrical Engineering, College of Engineering, Southern University of Science and Technology, Xili, Nanshan, Shenzhen, 518055, People's Republic of China
| | - Si Jian
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, Wuhan, 430030, People's Republic of China
| | - Yuanyuan Qin
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Jiefang Avenue, Wuhan, 430030, People's Republic of China.
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, College of Engineering, Southern University of Science and Technology, Xili, Nanshan, Shenzhen, 518055, People's Republic of China.
- Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, People's Republic of China.
| |
Collapse
|
8
|
Yang M, Huang X, Huang L, Cai G. Diagnosis of Parkinson’s disease based on 3D ResNet: The frontal lobe is crucial. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023]
|
9
|
Luo Y, Chen H, Gui M. Radiomics and Hybrid Models Based on Machine Learning to Predict Levodopa-Induced Dyskinesia of Parkinson's Disease in the First 6 Years of Levodopa Treatment. Diagnostics (Basel) 2023; 13:2511. [PMID: 37568874 PMCID: PMC10417024 DOI: 10.3390/diagnostics13152511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/23/2023] [Accepted: 07/26/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Current research on the prediction of movement complications associated with levodopa therapy in Parkinson's disease (PD) is limited. levodopa-induced dyskinesia (LID) is a movement complication that seriously affects the life quality of PD patients. One-third of PD patients develop LID within 1 to 6 years of levodopa treatment. This study aimed to construct models based on radiomics and machine learning to predict early LID in PD. METHODS We extracted radiomics features from the T1-weighted MRI obtained in the baseline of 49 PD control and 54 PD with LID in the first 6 years of levodopa therapy. Six brain regions related to the onset of PD were segmented as regions of interest (ROIs). The least absolute shrinkage and selection operator (LASSO) was used for feature selection. Using the machine learning methods of support vector machine (SVM), random forest (RF), and AdaBoost, we constructed radiomics models and hybrid models. The hybrid models combined the radiomics features and the Unified Parkinson's Disease Rating Scale part III (UPDRS III) total score. The five-fold cross-validation was performed and repeated 20 times to validate the stability of the classifiers. We used sensitivity, specificity, accuracy, receiver operating characteristic (ROC) curves, and area under the ROC curve (AUC) for model validation. RESULTS We selected 33 out of 6138 radiomics features. In the testing set of the radiomics model, the AUC values of the SVM, RF, and AdaBoost classifiers were 0.905, 0.808, and 0.778, respectively, and the accuracies were 0.839, 0.742, and 0.710. The hybrid models had better prediction performance. In the testing set, the AUC values of SVM, RF, and AdaBoost classifiers were 0.958, 0.861, and 0.832, respectively, and the accuracies were 0.903, 0.806, and 0.774. CONCLUSIONS Our results indicate that T1-weighted MRI is valuable in predicting early LID in PD. This work demonstrates that the combination of radiomics features and clinical features has good potential and value for identifying early LID in PD.
Collapse
Affiliation(s)
- Yang Luo
- Department of Neurology, Xiangya Hospital, Central South University, Changsha 410083, China;
| | - Huiqin Chen
- Department of Radiology, Xiangya Hospital, Central South University, Changsha 410083, China;
| | - Mingzhen Gui
- School of Automation, Central South University, Changsha 410083, China
| |
Collapse
|
10
|
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.
Collapse
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
| |
Collapse
|
11
|
Hu WZ, Cao LX, Yin JH, Zhao XS, Piao YS, Gu WH, Ma JH, Wan ZR, Huang Y. Non-motor symptoms in multiple system atrophy: A comparative study with Parkinson's disease and progressive supranuclear palsy. Front Neurol 2023; 13:1081219. [PMID: 36756345 PMCID: PMC9901543 DOI: 10.3389/fneur.2022.1081219] [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: 10/27/2022] [Accepted: 12/29/2022] [Indexed: 01/24/2023] Open
Abstract
Background Non-motor symptoms (NMS) are compulsory clinical features for the clinical diagnosis of multiple system atrophy (MSA), some of which precede motor symptoms onset. To date, few studies have systematically investigated NMS in MSA and the timing of presenting NMS as the disease progresses. Clinically, MSA is difficult to be differentiated from Parkinson's disease (PD) and progressive supranuclear palsy (PSP), and the differences in NMS between MSA and PD/PSP remain unclear. The aim of this study was to compare the burden of NMS between MSA and PD/PSP and to delineate the timing of NMS presentation relative to the onset of motor symptoms in MSA. Methods A total of 61, 87, and 30 patients with MSA, PD, and PSP, respectively, were enrolled in this study. NMS was systematically assessed in all patients using the NMS scale (NMSS), and the onset of NMS relative to the onset of motor symptoms in MSA was investigated. Results MSA group had higher total NMSS scores (82.15 ± 46.10) than the PD (36.14 ± 30.78) and PSP (50.30 ± 55.05) groups (p < 0.001 overall). The number distribution pattern of the NMS was significantly different among the three parkinsonian disorders (p < 0.001 overall). In total, 85.2% of patients with MSA had more than 10 NMS, which was significantly higher than PD (28.7%) and PSP (33.3%). The frequency and scores of many NMSS subdomains and symptoms were higher in MSA than in PD and PSP (all p < 0.05). Multivariate logistic regression analysis revealed that patients with fainting, lack of motivation, swallowing, and loss of sexual interest could be attributed to MSA rather than PD or PSP, while patients with loss of concentration and forgetfulness were characteristic features of PD or PSP rather than MSA. REM-sleep behavior disorder (RBD), constipation, problems having sex, and loss of sexual interest preceded the motor symptoms onset of MSA by 2.81 ± 4.51, 1.54 ± 6.32, 1.35 ± 4.70, and 0.45 ± 3.61 years, respectively. Conclusion The NMS spectrum in MSA differs from that of PD and PSP. Patients with MSA have a higher NMS burden than patients with PD or PSP. RBD, constipation, problems having sex, and loss of sexual interest may become early diagnostic clinical markers of MSA.
Collapse
Affiliation(s)
- Wen-Zheng Hu
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ling-Xiao Cao
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Jin-Hui Yin
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xue-Song Zhao
- Traditional Chinese Medical Department, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Ying-Shan Piao
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Wei-Hong Gu
- Neurology Department, China-Japan Friendship Hospital, Beijing, China
| | - Jing-Hong Ma
- Neurology Department, XuanWu Hospital, Capital Medical University, Beijing, China
| | - Zhi-Rong Wan
- Department of Neurology, Aerospace Central Hospital, Beijing, China
| | - Yue Huang
- China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Capital Medical University, Beijing, China,Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China,Department of Pharmacology, Faculty of Medicine and Health, School of Medical Sciences, University of New South Wales, Sydney, NSW, Australia,*Correspondence: Yue Huang ✉
| |
Collapse
|
12
|
Tafuri B, Lombardi A, Nigro S, Urso D, Monaco A, Pantaleo E, Diacono D, De Blasi R, Bellotti R, Tangaro S, Logroscino G. The impact of harmonization on radiomic features in Parkinson’s disease and healthy controls: A multicenter study. Front Neurosci 2022; 16:1012287. [PMID: 36300169 PMCID: PMC9589497 DOI: 10.3389/fnins.2022.1012287] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 09/20/2022] [Indexed: 11/29/2022] Open
Abstract
Radiomics is a challenging development area in imaging field that is greatly capturing interest of radiologists and neuroscientists. However, radiomics features show a strong non-biological variability determined by different facilities and imaging protocols, limiting the reproducibility and generalizability of analysis frameworks. Our study aimed to investigate the usefulness of harmonization to reduce site-effects on radiomics features over specific brain regions. We selected T1-weighted magnetic resonance imaging (MRI) by using the MRI dataset Parkinson’s Progression Markers Initiative (PPMI) from different sites with healthy controls (HC) and Parkinson’s disease (PD) patients. First, the investigation of radiomics measure discrepancies were assessed on healthy brain regions-of-interest (ROIs) via a classification pipeline based on LASSO feature selection and support vector machine (SVM) model. Then, a ComBat-based harmonization approach was applied to correct site-effects. Finally, a validation step on PD subjects evaluated diagnostic accuracy before and after harmonization of radiomics data. Results on healthy subjects demonstrated a dependence from site-effects that could be corrected with ComBat harmonization. LASSO regressor after harmonization was unable to select any feature to distinguish controls by site. Moreover, harmonized radiomics features achieved an area under the receiving operating characteristic curve (AUC) of 0.77 (compared to AUC of 0.71 for raw radiomics measures) in distinguish Parkinson’s patients from HC. We found a not-negligible site-effect studying radiomics of HC pre- and post-harmonization of features. Our validation study on PD patients demonstrated a significant influence of non-biological noise source in diagnostic performances. Finally, harmonization of multicenter radiomic data represent a necessary step to make analysis pipelines reliable and replicable for multisite neuroimaging studies.
Collapse
Affiliation(s)
- Benedetta Tafuri
- Dipartimento di Ricerca Clinica in Neurologia, Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Pia Fondazione Cardinale G. Panico, Università degli Studi di Bari Aldo Moro, Lecce, Italy
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
- *Correspondence: Benedetta Tafuri,
| | - Angela Lombardi
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Salvatore Nigro
- Dipartimento di Ricerca Clinica in Neurologia, Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Pia Fondazione Cardinale G. Panico, Università degli Studi di Bari Aldo Moro, Lecce, Italy
- Istituto di Nanotecnologia, Consiglio Nazionale delle Ricerche (CNR-NANOTEC), Lecce, Italy
| | - Daniele Urso
- Dipartimento di Ricerca Clinica in Neurologia, Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Pia Fondazione Cardinale G. Panico, Università degli Studi di Bari Aldo Moro, Lecce, Italy
- Department of Neurosciences, King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
| | - Alfonso Monaco
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
| | - Ester Pantaleo
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Domenico Diacono
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
| | - Roberto De Blasi
- Dipartimento di Ricerca Clinica in Neurologia, Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Pia Fondazione Cardinale G. Panico, Università degli Studi di Bari Aldo Moro, Lecce, Italy
- Dipartimento di Radiologia, Pia Fondazione Cardinale G. Panico, Lecce, Italy
| | - Roberto Bellotti
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Dipartimento Interateneo di Fisica M. Merlin, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Sabina Tangaro
- Istituto Nazionale di Fisica Nucleare (INFN), Sezione di Bari, Bari, Italy
- Dipartimento di Scienze del Suolo, Della Pianta e degli Alimenti, Università degli Studi di Bari Aldo Moro, Bari, Italy
| | - Giancarlo Logroscino
- Dipartimento di Ricerca Clinica in Neurologia, Centro per le Malattie Neurodegenerative e l’Invecchiamento Cerebrale, Pia Fondazione Cardinale G. Panico, Università degli Studi di Bari Aldo Moro, Lecce, Italy
- Dipartimento di Scienze Mediche di Base, Neuroscienze e Organi di Senso, Università degli Studi di Bari Aldo Moro, Bari, Italy
- Giancarlo Logroscino,
| |
Collapse
|
13
|
Li J, Liu X, Wang X, Liu H, Lin Z, Xiong N. Diffusion Tensor Imaging Radiomics for Diagnosis of Parkinson’s Disease. Brain Sci 2022; 12:brainsci12070851. [PMID: 35884658 PMCID: PMC9313106 DOI: 10.3390/brainsci12070851] [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: 03/19/2022] [Revised: 06/13/2022] [Accepted: 06/14/2022] [Indexed: 11/25/2022] Open
Abstract
Background: Diagnosis of Parkinson’s Disease (PD) based on clinical symptoms and scale scores is mostly objective, and the accuracy of neuroimaging for PD diagnosis remains controversial. This study aims to introduce a radiomic tool to improve the sensitivity and specificity of diagnosis based on Diffusion Tensor Imaging (DTI) metrics. Methods: In this machine learning-based retrospective study, we collected basic clinical information and DTI images from 54 healthy controls (HCs) and 56 PD patients. Among them, 60 subjects (30 PD patients and 30 HCs) were assigned to the training group, whereas the test cohort was 26 PD patients and 24 HCs. After the feature extraction and selection using newly developed image processing software Ray-plus, LASSO regression was used to finalize radiomic features. Results: A total of 4600 radiomic features were extracted, of which 12 were finally selected. The values of the AUC (area under the subject operating curve) in the training group, the validation group, and overall were 0.911, 0.931, and 0.919, respectively. Conclusion: This study introduced a novel radiometric and computer algorithm based on DTI images, which can help increase the sensitivity and specificity of PD screening.
Collapse
Affiliation(s)
- Jingwen Li
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (J.L.); (X.W.); (H.L.)
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China;
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan 430022, China
| | - Xinyi Wang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (J.L.); (X.W.); (H.L.)
| | - Hanshu Liu
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (J.L.); (X.W.); (H.L.)
| | - Zhicheng Lin
- Laboratory of Psychiatric Neurogenomics, McLean Hospital, Harvard Medical School, Belmont, MA 02478, USA;
| | - Nian Xiong
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (J.L.); (X.W.); (H.L.)
- Wuhan Red Cross Hospital, Wuhan 430022, China
- Correspondence:
| |
Collapse
|
14
|
Lim SJ, Suh CH, Shim WH, Kim SJ. Diagnostic performance of T2* gradient echo, susceptibility-weighted imaging, and quantitative susceptibility mapping for patients with multiple system atrophy-parkinsonian type: a systematic review and meta-analysis. Eur Radiol 2021; 32:308-318. [PMID: 34272590 DOI: 10.1007/s00330-021-08174-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2021] [Revised: 06/17/2021] [Accepted: 06/25/2021] [Indexed: 01/08/2023]
Abstract
OBJECTIVES To investigate the diagnostic performance of T2*-weighted gradient echo (GRE) imaging, susceptibility-weighted imaging (SWI), or quantitative susceptibility mapping (QSM) in differentiating multiple system atrophy-parkinsonian type (MSA-P) from Parkinson's disease (PD). METHODS A systematic literature search through the MEDLINE and EMBASE databases was performed, starting on September 8, 2020, to identify studies evaluating the diagnostic performance of putaminal hypointensity on T2* GRE or SWI and phase shift on QSM in differentiating MSA-P from PD. The pooled sensitivity and specificity were obtained using hierarchical logistic regression modeling and hierarchical summary receiver operating characteristic (HSROC) modeling. The pooled diagnostic yields of T2* GRE, SWI, or QSM among MSA-P patients were calculated using the DerSimonian-Laird random-effects model. RESULTS Twelve original articles with 985 patients were finally included. SWI was performed in seven studies, T2* GRE was performed in three studies, and QSM was performed in two studies. The pooled sensitivity and specificity were 0.65 (95% CI 0.51-0.78) and 0.90 (95% CI 0.83-0.95), respectively. The area under the HSROC curve was 0.87 (95% CI 0.84-0.90). The Higgins I2 statistic calculations revealed considerable heterogeneity in terms of both sensitivity (I2 = 72.12%) and specificity (I2 = 70.38%). The coupled forest plot revealed the threshold effect. For the nine studies in which area under the curve (AUC) was obtainable, the AUC ranged from 0.68 to 0.947, with a median of 0.819. The pooled diagnostic yield of T2* GRE, SWI, or QSM was 66% (95% CI 51-78%). CONCLUSIONS Putaminal hypointensity on T2* GRE or SWI and phase shift on QSM might be a promising diagnostic tool in differentiating MSA-P from PD. Further large multicenter prospective study is warranted. KEY POINTS • Three different index tests, definitions of positive image findings, thresholds, the way how to draw ROIs, reference standard, and MRI parameters could affect the heterogeneity of the study. • The pooled sensitivity and specificity were 0.65 (95% CI 0.51-0.78) and 0.90 (95% CI 0.83-0.95), respectively. • The pooled diagnostic yield of T2* GRE, SWI, or QSM was 66% (95% CI 51-78%).
Collapse
Affiliation(s)
- Su Jin Lim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Chong Hyun Suh
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
| | - Woo Hyun Shim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Joon Kim
- Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| |
Collapse
|