Radiomics for the Detection of Active Sacroiliitis Using MR Imaging
Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in de...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Diagnostics |
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| Online Access: | https://www.mdpi.com/2075-4418/13/15/2587 |
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| author | Matthaios Triantafyllou Michail E. Klontzas Emmanouil Koltsakis Vasiliki Papakosta Konstantinos Spanakis Apostolos H. Karantanas |
| author_facet | Matthaios Triantafyllou Michail E. Klontzas Emmanouil Koltsakis Vasiliki Papakosta Konstantinos Spanakis Apostolos H. Karantanas |
| author_sort | Matthaios Triantafyllou |
| collection | DOAJ |
| description | Detecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field. |
| first_indexed | 2024-03-11T00:29:42Z |
| format | Article |
| id | doaj.art-b4490d62a3554f3a8e23479b5cd2ba55 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T00:29:42Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-b4490d62a3554f3a8e23479b5cd2ba552023-11-18T22:47:30ZengMDPI AGDiagnostics2075-44182023-08-011315258710.3390/diagnostics13152587Radiomics for the Detection of Active Sacroiliitis Using MR ImagingMatthaios Triantafyllou0Michail E. Klontzas1Emmanouil Koltsakis2Vasiliki Papakosta3Konstantinos Spanakis4Apostolos H. Karantanas5Department of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, GreeceDepartment of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, GreeceDepartment of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, GreeceDepartment of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, GreeceDepartment of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, GreeceDepartment of Medical Imaging, University Hospital of Heraklion, 71110 Heraklion, GreeceDetecting active inflammatory sacroiliitis at an early stage is vital for prescribing medications that can modulate disease progression and significantly delay or prevent debilitating forms of axial spondyloarthropathy. Conventional radiography and computed tomography offer limited sensitivity in detecting acute inflammatory findings as these methods primarily identify chronic structural lesions. Conversely, Magnetic Resonance Imaging (MRI) is the preferred technique for detecting bone marrow edema, although it is a complex process requiring extensive expertise. Additionally, ascertaining the origin of lesions can be challenging, even for experienced medical professionals. Machine learning (ML) has showcased its proficiency in various fields by uncovering patterns that are not easily perceived from multi-dimensional datasets derived from medical imaging. The aim of this study is to develop a radiomic signature to aid clinicians in diagnosing active sacroiliitis. A total of 354 sacroiliac joints were segmented from axial fluid-sensitive MRI images, and their radiomic features were extracted. After selecting the most informative features, a number of ML algorithms were utilized to identify the optimal method for detecting active sacroiliitis, leading to the selection of an Extreme Gradient Boosting (XGBoost) model that accomplished an Area Under the Receiver-Operating Characteristic curve (AUC-ROC) of 0.71, thus further showcasing the potential of radiomics in the field.https://www.mdpi.com/2075-4418/13/15/2587active sacroiliitisaxial spondyloarthropathyradiomicsmachine learningbone marrow edema |
| spellingShingle | Matthaios Triantafyllou Michail E. Klontzas Emmanouil Koltsakis Vasiliki Papakosta Konstantinos Spanakis Apostolos H. Karantanas Radiomics for the Detection of Active Sacroiliitis Using MR Imaging Diagnostics active sacroiliitis axial spondyloarthropathy radiomics machine learning bone marrow edema |
| title | Radiomics for the Detection of Active Sacroiliitis Using MR Imaging |
| title_full | Radiomics for the Detection of Active Sacroiliitis Using MR Imaging |
| title_fullStr | Radiomics for the Detection of Active Sacroiliitis Using MR Imaging |
| title_full_unstemmed | Radiomics for the Detection of Active Sacroiliitis Using MR Imaging |
| title_short | Radiomics for the Detection of Active Sacroiliitis Using MR Imaging |
| title_sort | radiomics for the detection of active sacroiliitis using mr imaging |
| topic | active sacroiliitis axial spondyloarthropathy radiomics machine learning bone marrow edema |
| url | https://www.mdpi.com/2075-4418/13/15/2587 |
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