Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy
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 tot...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-12-01
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| Series: | BMC Medical Imaging |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12880-023-01169-1 |
| _version_ | 1797397699836772352 |
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| author | Shuting Bu Huize Pang Xiaolu Li Mengwan Zhao Juzhou Wang Yu Liu Hongmei Yu |
| author_facet | Shuting Bu Huize Pang Xiaolu Li Mengwan Zhao Juzhou Wang Yu Liu Hongmei Yu |
| author_sort | Shuting Bu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T01:13:58Z |
| format | Article |
| id | doaj.art-ba81cee1d8cc43ba8021f18ad0ef364a |
| institution | Directory Open Access Journal |
| issn | 1471-2342 |
| language | English |
| last_indexed | 2024-03-09T01:13:58Z |
| publishDate | 2023-12-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Imaging |
| spelling | doaj.art-ba81cee1d8cc43ba8021f18ad0ef364a2023-12-10T12:36:01ZengBMCBMC Medical Imaging1471-23422023-12-0123111010.1186/s12880-023-01169-1Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophyShuting Bu0Huize Pang1Xiaolu Li2Mengwan Zhao3Juzhou Wang4Yu Liu5Hongmei Yu6Department of Radiology, the First Hospital of China Medical UniversityDepartment of Radiology, the First Hospital of China Medical UniversityDepartment of Radiology, the First Hospital of China Medical UniversityDepartment of Radiology, the First Hospital of China Medical UniversityDepartment of Radiology, the First Hospital of China Medical UniversityDepartment of Radiology, the First Hospital of China Medical UniversityDepartment of Neurology, the First Hospital of China Medical UniversityAbstract 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.https://doi.org/10.1186/s12880-023-01169-1Idiopathic Parkinson’s diseaseMultiple system atrophyRadiomicsMRILight GBM |
| spellingShingle | Shuting Bu Huize Pang Xiaolu Li Mengwan Zhao Juzhou Wang Yu Liu Hongmei Yu Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy BMC Medical Imaging Idiopathic Parkinson’s disease Multiple system atrophy Radiomics MRI Light GBM |
| title | Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy |
| title_full | Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy |
| title_fullStr | Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy |
| title_full_unstemmed | Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy |
| title_short | Multi-parametric radiomics of conventional T1 weighted and susceptibility-weighted imaging for differential diagnosis of idiopathic Parkinson’s disease and multiple system atrophy |
| title_sort | multi parametric radiomics of conventional t1 weighted and susceptibility weighted imaging for differential diagnosis of idiopathic parkinson s disease and multiple system atrophy |
| topic | Idiopathic Parkinson’s disease Multiple system atrophy Radiomics MRI Light GBM |
| url | https://doi.org/10.1186/s12880-023-01169-1 |
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