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Jiang X, Shen C, Caba B, Arnold DL, Elliott C, Zhu B, Fisher E, Belachew S, Gafson AR. Assessing the utility of magnetic resonance imaging-based "SuStaIn" disease subtyping for precision medicine in relapsing-remitting and secondary progressive multiple sclerosis. Mult Scler Relat Disord 2023; 77:104869. [PMID: 37459715 DOI: 10.1016/j.msard.2023.104869] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 06/16/2023] [Accepted: 07/01/2023] [Indexed: 09/10/2023]
Abstract
BACKGROUND Patient stratification and individualized treatment decisions based on multiple sclerosis (MS) clinical phenotypes are arbitrary. Subtype and Staging Inference (SuStaIn), a published machine learning algorithm, was developed to identify data-driven disease subtypes with distinct temporal progression patterns using brain magnetic resonance imaging; its clinical utility has not been assessed. The objective of this study was to explore the prognostic capability of SuStaIn subtyping and whether it is a useful personalized predictor of treatment effects of natalizumab and dimethyl fumarate. METHODS Subtypes were available from the trained SuStaIn model for 3 phase 3 clinical trials in relapsing-remitting and secondary progressive MS. Regression models were used to determine whether baseline SuStaIn subtypes could predict on-study clinical and radiological disease activity and progression. Differences in treatment responses relative to placebo between subtypes were determined using interaction terms between treatment and subtype. RESULTS Natalizumab and dimethyl fumarate reduced inflammatory disease activity in all SuStaIn subtypes (all p < 0.001). SuStaIn MS subtyping alone did not discriminate responder heterogeneity based on new lesion formation and disease progression (p > 0.05 across subtypes). CONCLUSION SuStaIn subtypes correlated with disease severity and functional impairment at baseline but were not predictive of disability progression and could not discriminate treatment response heterogeneity.
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Affiliation(s)
| | - Changyu Shen
- Biogen, 225 Binney Street, Cambridge, MA 02142, USA
| | - Bastien Caba
- Biogen, 225 Binney Street, Cambridge, MA 02142, USA
| | - Douglas L Arnold
- NeuroRx Research, Montreal, Quebec, Canada; McGill University, Montreal, Quebec, Canada
| | | | - Bing Zhu
- Biogen, 225 Binney Street, Cambridge, MA 02142, USA
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Boziki M, Bakirtzis C, Sintila SA, Kesidou E, Gounari E, Ioakimidou A, Tsavdaridou V, Skoura L, Fylaktou A, Nikolaidou V, Stangou M, Nikolaidis I, Giantzi V, Karafoulidou E, Theotokis P, Grigoriadis N. Ocrelizumab in Patients with Active Primary Progressive Multiple Sclerosis: Clinical Outcomes and Immune Markers of Treatment Response. Cells 2022; 11:cells11121959. [PMID: 35741088 PMCID: PMC9222195 DOI: 10.3390/cells11121959] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Revised: 06/14/2022] [Accepted: 06/15/2022] [Indexed: 02/04/2023] Open
Abstract
Ocrelizumab is a B-cell-depleting monoclonal antibody approved for the treatment of relapsing-remitting multiple sclerosis (RRMS) and active primary progressive MS (aPPMS). This prospective, uncontrolled, open-label, observational study aimed to assess the efficacy of ocrelizumab in patients with aPPMS and to dissect the clinical, radiological and laboratory attributes of treatment response. In total, 22 patients with aPPMS followed for 24 months were included. The primary efficacy outcome was the proportion of patients with optimal response at 24 months, defined as patients free of relapses, free of confirmed disability accumulation (CDA) and free of T1 Gd-enhancing lesions and new/enlarging T2 lesions on the brain and cervical MRI. In total, 14 (63.6%) patients and 13 patients (59.1%) were classified as responders at 12 and 24 months, respectively. Time exhibited a significant effect on mean absolute and normalized gray matter cerebellar volume (F = 4.342, p = 0.23 and F = 4.279, p = 0.024, respectively). Responders at 24 months exhibited reduced peripheral blood ((%) of CD19+ cells) plasmablasts compared to non-responders at the 6-month point estimate (7.69 ± 4.4 vs. 22.66 ± 7.19, respectively, p = 0.043). Response to ocrelizumab was linked to lower total and gray matter cerebellar volume loss over time. Reduced plasmablast depletion was linked for the first time to sub-optimal response to ocrelizumab in aPPMS.
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Affiliation(s)
- Marina Boziki
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Christos Bakirtzis
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Styliani-Aggeliki Sintila
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Evangelia Kesidou
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Evdoxia Gounari
- Microbiology Laboratory, Department of Immunology, AHEPA University Hospital, 54636 Thessaloniki, Greece; (E.G.); (A.I.); (V.T.); (L.S.)
| | - Aliki Ioakimidou
- Microbiology Laboratory, Department of Immunology, AHEPA University Hospital, 54636 Thessaloniki, Greece; (E.G.); (A.I.); (V.T.); (L.S.)
| | - Vasiliki Tsavdaridou
- Microbiology Laboratory, Department of Immunology, AHEPA University Hospital, 54636 Thessaloniki, Greece; (E.G.); (A.I.); (V.T.); (L.S.)
| | - Lemonia Skoura
- Microbiology Laboratory, Department of Immunology, AHEPA University Hospital, 54636 Thessaloniki, Greece; (E.G.); (A.I.); (V.T.); (L.S.)
| | - Asimina Fylaktou
- National Peripheral Histocompatibility Center, Immunology Department, Hippokration General Hospital, 54642 Thessaloniki, Greece; (A.F.); (V.N.)
| | - Vasiliki Nikolaidou
- National Peripheral Histocompatibility Center, Immunology Department, Hippokration General Hospital, 54642 Thessaloniki, Greece; (A.F.); (V.N.)
| | - Maria Stangou
- Department of Nephrology, Medical School, Aristotle University of Thessaloniki, Hippokration Hospital, 54642 Thessaloniki, Greece;
| | - Ioannis Nikolaidis
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Virginia Giantzi
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Eleni Karafoulidou
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Paschalis Theotokis
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
| | - Nikolaos Grigoriadis
- Multiple Sclerosis Center of the 2nd Neurological University Department, School of Medicine, Aristotle University of Thessaloniki, AHEPA General University Hospital, 54636 Thessaloniki, Greece; (M.B.); (C.B.); (S.-A.S.); (E.K.); (I.N.); (V.G.); (E.K.); (P.T.)
- Special Unit for Biomedical Research and Education, School of Medicine, Aristotle University of Thessaloniki, 54636 Thessaloniki, Greece
- Correspondence:
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Fiani B, Pasko KBD, Sarhadi K, Covarrubias C. Current uses, emerging applications, and clinical integration of artificial intelligence in neuroradiology. Rev Neurosci 2021; 33:383-395. [PMID: 34506699 DOI: 10.1515/revneuro-2021-0101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Accepted: 08/18/2021] [Indexed: 11/15/2022]
Abstract
Artificial intelligence (AI) is a branch of computer science with a variety of subfields and techniques, exploited to serve as a deductive tool that performs tasks originally requiring human cognition. AI tools and its subdomains are being incorporated into healthcare delivery for the improvement of medical data interpretation encompassing clinical management, diagnostics, and prognostic outcomes. In the field of neuroradiology, AI manifested through deep machine learning and connected neural networks (CNNs) has demonstrated incredible accuracy in identifying pathology and aiding in diagnosis and prognostication in several areas of neurology and neurosurgery. In this literature review, we survey the available clinical data highlighting the utilization of AI in the field of neuroradiology across multiple neurological and neurosurgical subspecialties. In addition, we discuss the emerging role of AI in neuroradiology, its strengths and limitations, as well as future needs in strengthening its role in clinical practice. Our review evaluated data across several subspecialties of neurology and neurosurgery including vascular neurology, spinal pathology, traumatic brain injury (TBI), neuro-oncology, multiple sclerosis, Alzheimer's disease, and epilepsy. AI has established a strong presence within the realm of neuroradiology as a successful and largely supportive technology aiding in the interpretation, diagnosis, and even prognostication of various pathologies. More research is warranted to establish its full scientific validity and determine its maximum potential to aid in optimizing and providing the most accurate imaging interpretation.
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Affiliation(s)
- Brian Fiani
- Department of Neurosurgery, Desert Regional Medical Center, 1150 N Indian Canyon Dr, Palm Springs, CA, 92262, USA
| | - Kory B Dylan Pasko
- School of Medicine, Georgetown University, 3900 Reservoir Rd NW, Washington, DC, 20007, USA
| | - Kasra Sarhadi
- Department of Neurology, University of Washington, Main Hospital, 325 9th Ave, Seattle, WA, 98104, USA
| | - Claudia Covarrubias
- School of Medicine, Universidad Anáhuac Querétaro, Cto. Universidades I, Fracción 2, 76246 Qro., Querétaro, Mexico
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