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Li T, Chen X, Jing Y, Wang H, Zhang T, Zhang L, Ding H, Xie M, He L. Diagnostic Value of Multiparameter MRI-Based Radiomics in Pediatric Myelin Oligodendrocyte Glycoprotein Antibody-Associated Disorders. AJNR Am J Neuroradiol 2023; 44:1425-1431. [PMID: 37973182 PMCID: PMC10714848 DOI: 10.3174/ajnr.a8045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 09/28/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND PURPOSE Myelin oligodendrocyte glycoprotein antibody-associated disorders (MOGAD) have a higher prevalence among children. For children undergoing the initial manifestation of MOGAD, prompt diagnosis has paramount importance. This study assessed the performance of multiparameter MRI-based radiomics in distinguishing patients with and without MOGAD with idiopathic inflammatory demyelinating diseases. MATERIALS AND METHODS We enrolled a cohort of 121 patients diagnosed with idiopathic inflammatory demyelinating diseases, including 68 children with MOGAD and 53 children without MOGAD. Radiomics models (T1WI, T2WI, FLAIR, and compound model) using features extracted from demyelinating lesions within the brain parenchyma were developed in the training set. The performance of these models underwent validation within the internal testing set. Additionally, we gathered clinical factors and MRI features of brain parenchymal lesions at their initial presentation. Subsequently, these variables were used in the construction of a clinical prediction model through multivariate logistic regression analysis. RESULTS The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the training set were 0.781 (95% CI, 0.689-0.864), 0.959 (95% CI, 0.924-0.987), 0.939 (95% CI, 0.898-0.979), and 0.989 (95% CI, 0.976-0.999), respectively. The areas under the curve for the radiomics models (T1WI, T2WI, FLAIR, and the compound model) in the testing set were 0.500 (95% CI, 0.304-0.652), 0.833 (95% CI, 0.697-0.944), 0.804 (95% CI, 0.664-0.918), and 0.905 (95% CI, 0.803-0.979), respectively. The areas under the curve of the clinical prediction model in the training set and testing set were 0.700 and 0.289, respectively. CONCLUSIONS Multiparameter MRI-based radiomics helps distinguish MOGAD from non-MOGAD in patients with idiopathic inflammatory demyelinating diseases.
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Affiliation(s)
- Ting Li
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Xin Chen
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Yang Jing
- Huiying Medical Technology Co (Y.J.), Dongsheng Science and Technology Park, Beijing, China
| | - Haoru Wang
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ting Zhang
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Li Zhang
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Hao Ding
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Mingye Xie
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
| | - Ling He
- From the Department of Radiology (T.L., X.C., H.W., T.Z., L.Z., H.D., M.X., L.H.), Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatrics, Chongqing, China
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Naji Y, Mahdaoui M, Klevor R, Kissani N. Artificial Intelligence and Multiple Sclerosis: Up-to-Date Review. Cureus 2023; 15:e45412. [PMID: 37854769 PMCID: PMC10581506 DOI: 10.7759/cureus.45412] [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] [Accepted: 09/17/2023] [Indexed: 10/20/2023] Open
Abstract
Multiple sclerosis (MS) remains a challenging neurological disorder for the clinician in terms of diagnosis and management. The growing integration of AI-based algorithms in healthcare offers a golden opportunity for clinicians and patients with MS. AI models are based on statistical analyses of large quantities of data from patients including "demographics, genetics, clinical and radiological presentation." These approaches are promising in the quest for greater diagnostic accuracy, tailored management plans, and better prognostication of disease. The use of AI in multiple sclerosis represents a paradigm shift in disease management. With ongoing advancements in AI technologies and the increasing availability of large-scale datasets, the potential for further innovation is immense. As AI continues to evolve, its integration into clinical practice will play a vital role in improving diagnostics, optimizing treatment strategies, and enhancing patient outcomes for MS. This review is about conducting a literature review to identify relevant studies on AI applications in MS. Only peer-reviewed studies published in the last four years have been selected. Data related to AI techniques, advancements, and implications are extracted. Through data analysis, key themes and tendencies are identified. The review presents a cohesive synthesis of the current state of AI and MS, highlighting potential implications and new advancements.
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Affiliation(s)
- Yahya Naji
- Neurology Department, REGNE Research Laboratory, Faculty of Medicine and Pharmacy, Ibn Zohr University, Agadir, MAR
- Neurology Department, Agadir University Hospital, Agadir, MAR
| | - Mohamed Mahdaoui
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Raymond Klevor
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
| | - Najib Kissani
- Neurology Department, University Hospital Mohammed VI, Marrakech, MAR
- Neuroscience Research Laboratory, Faculty of Medicine and Pharmacy, Cadi Ayyad University, Marrakech, MAR
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Cui J, Miao X, Yanghao X, Qin X. Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases. Front Neurol 2023; 14:1171167. [PMID: 37360350 PMCID: PMC10288367 DOI: 10.3389/fneur.2023.1171167] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 05/16/2023] [Indexed: 06/28/2023] Open
Abstract
Background The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. Methods Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. Results On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. "Glioma" is a current attractive research hotspot. Keywords such as "machine learning," "brain metastasis," and "gene mutations" have recently appeared at the research frontier. Conclusion Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors.
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Weidauer S, Raab P, Hattingen E. Diagnostic approach in multiple sclerosis with MRI: an update. Clin Imaging 2021; 78:276-285. [PMID: 34174655 DOI: 10.1016/j.clinimag.2021.05.025] [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: 02/25/2021] [Revised: 05/06/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Although neurological examination and medical history are the first and most important steps towards the diagnosis of multiple sclerosis (MS), MRI has taken a prominent role in the diagnostic workflow especially since the implementation of McDonald criteria. However, before applying those on MR imaging features, other diseases must be excluded and MS should be favoured as the most likely diagnosis. For the prognosis the earliest possible and correct diagnosis of MS is crucial, since increasingly effective disease modifying therapies are available for the different forms of clinical manifestation and progression. This review deals with the significance of MRI in the diagnostic workup of MS with special regard to daily clinical practice. The recommended MRI protocols for baseline and follow-up examinations are summarized and typical MS lesion patterns ("green flags") in four defined CNS compartments are introduced. Pivotal is the recognition of neurological aspects as well as imaging findings atypical for MS ("red flags"). In addition, routinely assessment of Aquaporin-4-IgG antibodies specific for neuromyelitis optica spectrum disorders (NMOSD) as well as the knowledge of associated lesion patterns on MRI is recommended. Mistaken identity of such lesions with MS and consecutive implementation of disease modifying therapies for MS can worsen the course of NMOSD.
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Affiliation(s)
- Stefan Weidauer
- Department of Neurology, Sankt Katharinen Hospital, Teaching Hospital of the Goethe University, Seckbacher Landstraße 65, 60389 Frankfurt am Main, Germany.
| | - Peter Raab
- Institute of Diagnostic and Interventional Neuroradiology, Hannover Medical School, Carl Neuberg Straße 1, 30625 Hannover, Germany
| | - Elke Hattingen
- Institute of Neuroradiology, Goethe University, Schleusenweg 2-16, 60528 Frankfurt am Main, Germany
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