How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review
Introduction: Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framew...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158223001821 |
| _version_ | 1827854892938559488 |
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| author | Federico Spagnolo Adrien Depeursinge Sabine Schädelin Aysenur Akbulut Henning Müller Muhamed Barakovic Lester Melie-Garcia Meritxell Bach Cuadra Cristina Granziera |
| author_facet | Federico Spagnolo Adrien Depeursinge Sabine Schädelin Aysenur Akbulut Henning Müller Muhamed Barakovic Lester Melie-Garcia Meritxell Bach Cuadra Cristina Granziera |
| author_sort | Federico Spagnolo |
| collection | DOAJ |
| description | Introduction: Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI).Aims: Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow.Methods: Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI’s six-steps, which include a tool’s technical assessment, clinical validation, and integration.Results: We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth.Conclusions: To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients’ management of such tools remain almost unexplored. |
| first_indexed | 2024-03-12T11:37:08Z |
| format | Article |
| id | doaj.art-1f1c6e70d8c84dcba420fa64e3c6f4b3 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-03-12T11:37:08Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-1f1c6e70d8c84dcba420fa64e3c6f4b32023-09-01T05:01:09ZengElsevierNeuroImage: Clinical2213-15822023-01-0139103491How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic reviewFederico Spagnolo0Adrien Depeursinge1Sabine Schädelin2Aysenur Akbulut3Henning Müller4Muhamed Barakovic5Lester Melie-Garcia6Meritxell Bach Cuadra7Cristina Granziera8Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; MedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, SwitzerlandMedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; Nuclear Medicine and Molecular Imaging Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, SwitzerlandTranslational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Clinical Trial Unit, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, SwitzerlandTranslational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Ankara University School of Medicine, Ankara, TurkeyMedGIFT, Institute of Informatics, School of Management, HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland, Sierre, Switzerland; The Sense Research and Innovation Center, Lausanne and Sion, SwitzerlandTranslational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, SwitzerlandTranslational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, SwitzerlandCIBM Center for Biomedical Imaging, Lausanne, Switzerland; Radiology Department, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, SwitzerlandTranslational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Corresponding author at: Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland.Introduction: Over the past few years, the deep learning community has developed and validated a plethora of tools for lesion detection and segmentation in Multiple Sclerosis (MS). However, there is an important gap between validating models technically and clinically. To this end, a six-step framework necessary for the development, validation, and integration of quantitative tools in the clinic was recently proposed under the name of the Quantitative Neuroradiology Initiative (QNI).Aims: Investigate to what extent automatic tools in MS fulfill the QNI framework necessary to integrate automated detection and segmentation into the clinical neuroradiology workflow.Methods: Adopting the systematic Cochrane literature review methodology, we screened and summarised published scientific articles that perform automatic MS lesions detection and segmentation. We categorised the retrieved studies based on their degree of fulfillment of QNI’s six-steps, which include a tool’s technical assessment, clinical validation, and integration.Results: We found 156 studies; 146/156 (94%) fullfilled the first QNI step, 155/156 (99%) the second, 8/156 (5%) the third, 3/156 (2%) the fourth, 5/156 (3%) the fifth and only one the sixth.Conclusions: To date, little has been done to evaluate the clinical performance and the integration in the clinical workflow of available methods for MS lesion detection/segmentation. In addition, the socio-economic effects and the impact on patients’ management of such tools remain almost unexplored.http://www.sciencedirect.com/science/article/pii/S2213158223001821MRIMultiple sclerosisSystematic reviewLesion segmentationLesion detection |
| spellingShingle | Federico Spagnolo Adrien Depeursinge Sabine Schädelin Aysenur Akbulut Henning Müller Muhamed Barakovic Lester Melie-Garcia Meritxell Bach Cuadra Cristina Granziera How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review NeuroImage: Clinical MRI Multiple sclerosis Systematic review Lesion segmentation Lesion detection |
| title | How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review |
| title_full | How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review |
| title_fullStr | How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review |
| title_full_unstemmed | How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review |
| title_short | How far MS lesion detection and segmentation are integrated into the clinical workflow? A systematic review |
| title_sort | how far ms lesion detection and segmentation are integrated into the clinical workflow a systematic review |
| topic | MRI Multiple sclerosis Systematic review Lesion segmentation Lesion detection |
| url | http://www.sciencedirect.com/science/article/pii/S2213158223001821 |
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