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Tiu, V. Predictive Magnetic Resonance Imaging Biomarkers in Multiple Sclerosis. Encyclopedia. Available online: https://encyclopedia.pub/entry/20712 (accessed on 18 May 2024).
Tiu V. Predictive Magnetic Resonance Imaging Biomarkers in Multiple Sclerosis. Encyclopedia. Available at: https://encyclopedia.pub/entry/20712. Accessed May 18, 2024.
Tiu, Vlad. "Predictive Magnetic Resonance Imaging Biomarkers in Multiple Sclerosis" Encyclopedia, https://encyclopedia.pub/entry/20712 (accessed May 18, 2024).
Tiu, V. (2022, March 18). Predictive Magnetic Resonance Imaging Biomarkers in Multiple Sclerosis. In Encyclopedia. https://encyclopedia.pub/entry/20712
Tiu, Vlad. "Predictive Magnetic Resonance Imaging Biomarkers in Multiple Sclerosis." Encyclopedia. Web. 18 March, 2022.
Predictive Magnetic Resonance Imaging Biomarkers in Multiple Sclerosis
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This entry is adapted from the peer-reviewed paper 10.3390/medicina58030377

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease of the central nervous system that poses a challenge to clinicians due to its remarkable inter- and intra-individual heterogeneity. MS still lacks specific humoral biomarkers for diagnosis, prognosis, or progression, but data derived from magnetic resonance imaging (MRI) measurements might represent our best predictive biomarkers to date.

MRI biomarkers multiple sclerosis prediction

1. Introduction

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease of the central nervous system that poses a challenge to clinicians due to its remarkable inter- and intra-individual heterogeneity [1].
MS still lacks specific humoral biomarkers for diagnosis, prognosis, or progression, but data derived from magnetic resonance imaging (MRI) measurements might represent our best predictive biomarkers to date.
Indeed, while the role of MRI in the diagnostics of MS is unquestionable, the researchers are still exploring what other data might be derived from this investigation. Almost all aspects of correctly managing MS patients rely on corelating clinical evolution with MRI scans, from initial disease modifying drug (DMT) choice to assessing DMT efficacy, identifying subclinical activity of disease or progression, and many more.

2. The Need for Prognosis Biomarkers in MS

For years now, there has been an ongoing debate whether escalation or induction therapy is better for MS patients. Escalation therapy is defined as starting with a low to moderate efficacy DMT and escalating, if needed due to poor control of the disease, to a second-line therapy. Induction therapy differs from the former by starting out patients with high-efficacy DMTs and switching only at a later time to a maintenance, first-line agent.
Recent data seem to suggest induction therapy should be favored [2][3][4][5]. Notably, The European Committee for Treatment and Research in Multiple Sclerosis (ECTRIMS) recently published a review on aggressive MS treatment [6] and a guideline on treatment choice in MS encouraging more aggressive DMT choices at first (unpublished at the moment of writing this entry, presented as “Update of the ECTRIMS/EAN Guidelines on the Treatment of Multiple Sclerosis. Updated recommendations” by the Steering Committee for the update and upgrade of the ECTRIMS/EAN guideline on the pharmacological treatment of people with Multiple Sclerosis, 15 October 2021, during the 37th ECTRIMS Congress).
How quickly this will be implemented is a different matter. Patients’ choice, intolerable side effects, pregnancy therapy restrictions, and the added burden to healthcare budgets are just some of the obstacles that stand in the way [7].
For now, most MS centers will choose a hybrid strategy—patients who present highly active or aggressive forms of MS will be started on second-line treatment options, while those who have inactive, low-risk forms will be started on more modestly effective therapies such as interferons or glatiramer acetate.
Unfortunately, some cases will be challenging to classify regarding disease activity and predicting progression following diagnosis remains a difficult task.

3. How Do We Define Prognosis and What Is a Bad One?

When talking about MS, a clinician will judge the prognosis of each case based on disease activity and risk factors for a poor evolution (more on that later), while also considering the current burden of the disease, clinical subtype, and many other factors.
Assessing this risk, however, is quite difficult, as each factor involved is under some degree of uncertainty or controversy. For example, there is no universal definition for “aggressive” MS.
In 2018, an ECTRIMS Focused Workshop on Aggressive MS tried and failed to define this term due to lack of available data correlating severe disease with imaging and molecular biomarkers [8].
Whether it is called “aggressive”, “highly active”, or “malignant” [9][10][11], most definitions (spanning decades) usually agree that it should be a rapid deterioration to a certain EDSS (usually to a score of 6.0 over 5 to 10 years), with some authors considering other conditions such as the number or features of relapses (aggressiveness, sequelae, EDSS impact, certain functional systems involved, etc.) or failure of DMTs. Others considered that a time of 3 years from RRMS onset to SPMS phase would also qualify as aggressive. Most authors will also include MRI features in defining aggressive MS (with gadolinium-enhancing lesions and new T2 lesions being key markers most of the time) [12][13][14][15][16][17][18].
The other end of this spectrum is the holy grail of managing MS—the “NEDA-4” status. Standing for “No Evidence of Disease Activity”, NEDA-4 is a concept that evolved over time by adding more items to the previous definitions (there was a NEDA, a NEDA-3, and now this). It is currently defined as no evidence of relapses, new or enlarged T2 lesions, and 6-month confirmed disability progression (defined as an increase in EDSS score of 1.5 points from a baseline score of 0, of 1.0 point from a baseline score of 1.0 or more, or of 0.5 points from a baseline score of greater than 5.0). The mean annualized rate of brain volume loss should also be less than 0.4% [19][20][21][22][23].
The very concept of defining and measuring disability and disability progression in MS is still flawed to some degree, and even the most used scales today—the EDSS (Expanded Disability Status Scale) and the MSFC (Multiple Sclerosis Functional Composite)—have important limitations.
The EDSS falls short on some important aspects, such as its non-linear progression, bimodal population distribution (distribution grouping around the scores of 3 and 6), irregular progression between intervals, measuring different aspects of disability at different points along the scale, inter- and intra-rater reliability issues, and poor to moderate correlation with MRI measures [24][25][26][27][28]. The EDSS has also received criticism for being imprecise at the lower end of the scale, insensitive at the middle and upper ends, and too heavily dependent on ambulation; not to mention that the upper extremity and cognitive functions are insufficiently assessed, that the cerebellar functional system has a very limited contribution to the score, and the list goes on [29][30].
The MSFC, which was specifically developed to overcome these problems, also has issues with a noticeable learning effect, poor patient acceptance (especially for the PASSAT testing), not being recognized by regulatory agencies as a primary disability outcome measure, and also lacking visual testing and still falling short in correlating with other MS measures such as the MRI [27][31].
It is because clinicians, patients, and studies alike define prognosis in MS today by the time elapsed to reach a certain degree of disability or to reaching a continuous progression of disability for a sustained period of time that it is of paramount importance to correctly define and track disability in MS. 

4. Are Prognosis Biomarkers in MS Even Possible?

Prognosis biomarkers in MS were a rather controversial term, since MS tends to be quite an unpredictable disease [32]. In the long period of time that has elapsed since MS was first described, many tried to find risk factors for a poor prognosis. The fact that almost all these attempts have now been long forgotten is testimony to the difficult task ahead.
Kurtzke made an attempt at this with the “five-year rule”, stating that patients who had minimal accumulated disability following the first five years of disease evolution faced more favorable outcomes—needless to say, this has since been disproven [33].
Are prognosis biomarkers in MS even possible? The answer is probably “yes”, and we have had one of those biomarkers available for decades now.

5. The Use of MRI Metrics as Prognosis Biomarkers

The advent of MRI scans in the 1980s brought a revolution to the world of MS [34], with MRI criteria quickly being developed and standardizing the diagnostic process [35]. Research into how MRI data can be used in MS is still driving forward our understanding of the disease today.
Many MRI parameters have been correlated with MS, arguably the most popular of which are white matter lesions (contrast enhancing lesions, new lesions on longitudinal scans, and total white matter lesion volume and number) and cerebral and spinal volumetrics, with gray matter disease being a hot topic in recent years.

6. Evaluating White Matter (WM) Pathology in MS

WM lesions have been used as biomarkers for the prognosis and progression of the disease for a long time, with WM lesions often being one of the most important factors in guiding DMT choice. For short-to-medium term, baseline MRI scans have been considered by most clinicians to give the most accurate predictions of all biomarkers and have been used in guiding DMT choice [36][37][38][39][40][41].
Classic MRI measures that evaluate WMLs in MS, using conventional techniques (T1, T2, fluid-attenuated inversion recovery (FLAIR), etc.), usually refer to the number and volume of gadolinium-enhancing (GdE) lesions, as well as hyperintense lesions on T2-weighted scans and hypointense “black holes” on T1-weighted scans [42].
MRI scanners have been getting better and better at detecting WMLs [43][44]. Limitations still exist, however, as some authors have shown that T2/FLAIR WMHs overestimate neuropathologically confirmed demyelination in the periventricular areas but underestimate it in the deep WM [45], and overall sensibility and specificity hovers around 80% to 90%. As with all MRI measurements, higher field strengths and resolutions (3D versus 2D) will produce better results [46].
A significant number of lesions visible on MRI go undetected clinically. Studies have shown that, even when assessing conventional sequences at 1.5T MRI scans, subclinical pathological processes might be 5 to 10 times more active than clinically expected [47].

7. Spinal Atrophy

The spinal cord (particularly, the cervical segment) is more atrophied in MS patients versus that in healthy controls, with a greater atrophy rate than the total brain one, and greater in PPMS rather than RRMS [48].
Reliable longitudinal measurements are possible using the standardized cross-sectional area of the upper cervical cord [49]. Automated MRI measurements including total volume and individual white and gray matter volumes are also possible today [50].
Evidence is sparse regarding clinical outcomes and particularly regarding prognosis implications for individual-level longitudinal follow up, but recent data show that even a small increase in the spinal atrophy rate is associated with a significantly increased risk of disability progression [51].

8. Evaluating Gray Matter (GM) Pathology in MS

It has been known for decades that postmortem cerebral histological examinations of MS patients reveal cortical demyelination (and pathology) that is often more extensive than white matter demyelination [52][53][54].
Research has shown that GM abnormalities seem to occur from the first clinical demyelinating event (clinically isolated syndrome—CIS), and their presence predicts the conversion to MS, as well as the progressive accrual of disability. GM pathology might be the earliest manifestation of MS, and it has been proofed that GM atrophy is more severe than WM atrophy early in the course of the disease [55].
GM pathology also elegantly explains the observed dissociation between markers of inflammatory demyelination (relapses, WML gadolinium enhancement, and WML burden) and disease progression [54]. Physical disability, fatigue, and cognitive impairment in MS all seem to be tied to GM pathology as well [56][57][58].
This accumulating knowledge generated a shift toward considering MS as a pathology involving both WM and GM (the 2017 McDonalds criteria included, for the first time, cortical lesions as proof of dissemination in space) [59].
GM pathology can currently be evaluated in two ways on MRI scans—GM lesions and GM volumetrics. Most studies investigate deep gray matter (DGM) and cortical GM separately.

9. A Brief Glance at Prognosis Scores

What if it were not one single element that should be used as a prediction tool, but rather multiple factors that are known to be associated with a poor outcome? This concept, of creating prognosis scores in MS, has been around for a long, long time.
Many authors tried to use data derived from large cohorts of patients (some of whom had a natural history of the disease) and create a prediction model for long-term prognosis, based mainly on clinical and MRI data available in the early stages of diagnosis (usually from baseline to one-year follow up).
A systemized review of prognosis scores that had been published up to August 2019 (with over 30 scores included in the analysis) concluded that “Although a number of prediction models for RRMS have been reported, most are at high risk of bias and lack external validation and impact analysis, restricting their application to routine clinical practice” [60].
Overall, the most robust predictors of poor prognosis across these scores seem to be early sphincter involvement, higher baseline disability, and certain MRI measurements (brain atrophy rate and T2 lesions number and volume). Unfortunately, these rely on established damage and, therefore, are not ideal prognostic markers of the future [60].
Published in May 2021, the Secondary Progressive Risk Score (SP-RiSc) by Calabrese et al. [61] was not included in the review mentioned earlier. This score is different from its predecessors as it heavily relies on cortical pathology, which greatly enhances its predictive accuracy. The predictors included are age, baseline EDSS, cortical lesions number at baseline and 2-year follow up, WM lesions number, cerebellar cortical volume at baseline and 2-year follow up, global cortical thickness at baseline and 2-year follow up).
What is perhaps most important is that SP-RiSC performs with great accuracy, sensibility, and sensitivity at the individual level, with scores of ≥17.7 indicating a 92% probability of converting to SPMS within 10 years from the disease diagnosis. In contrast, patients with SP-RiSc < 17.7 had an 87% probability of remaining in the relapsing–remitting phase.

References

  1. Ruggieri, S.; Pontecorvo, S.; Tortorella, C.; Gasperini, C. Induction treatment strategy in multiple sclerosis: A review of past experiences and future perspectives. Mult. Scler. Demyelinating Disord. 2018, 3, 5.
  2. Rieckmann, P. Concepts of induction and escalation therapy in multiple sclerosis. J. Neurol. Sci. 2009, 277, S42–S45.
  3. Rieckmann, P.; Traboulsee, A.; Devonshire, V.; Oger, J. Escalating immunotherapy of multiple sclerosis. Ther. Adv. Neurol. Disord. 2008, 1, 181–192.
  4. Spelman, T.; Magyari, M.; Piehl, F.; Svenningsson, A.; Rasmussen, P.V.; Kant, M.; Sellebjerg, F.; Joensen, H.; Hillert, J.; Lycke, J. Treatment Escalation vs. Immediate Initiation of Highly Effective Treatment for Patients with Relapsing-Remitting Multiple Sclerosis: Data from 2 Different National Strategies. JAMA Neurol. 2021, 78, 1197–1204.
  5. Simpson, A.; Mowry, E.M.; Newsome, S.D. Early Aggressive Treatment Approaches for Multiple Sclerosis. Curr. Treat. Options Neurol. 2021, 23, 19.
  6. Arrambide, G.; Iacobaeus, E.; Amato, M.P.; Derfuss, T.; Vukusic, S.; Hemmer, B.; Brundin, L.; Tintore, M.; Berger, J.; Boyko, A.; et al. Aggressive multiple sclerosis (2): Treatment. Mult. Scler. J. 2020, 26, 1352458520924595.
  7. Hartung, D.M. Health economics of disease-modifying therapy for multiple sclerosis in the United States. Ther. Adv. Neurol. Disord. 2021, 14, 1756286420987031.
  8. Iacobaeus, E.; Arrambide, G.; Amato, M.P.; Derfuss, T.; Vukusic, S.; Hemmer, B.; Tintoré, M.; Brundin, L. Aggressive multiple sclerosis (1): Towards a definition of the phenotype. Mult. Scler. J. 2020, 26, 1352458520925369.
  9. Lublin, F.D.; Reingold, S.C. Defining the clinical course of multiple sclerosis: Results of an international survey. National Multiple Sclerosis Society (USA) Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. Neurology 1996, 46, 907–911.
  10. Díaz, C.; Zarco, L.A.; Rivera, D.M. Highly active multiple sclerosis: An update. Mult. Scler. Relat. Disord. 2019, 30, 215–224.
  11. Rush, C.A.; MacLean, H.J.; Freedman, M.S. Aggressive multiple sclerosis: Proposed definition and treatment algorithm. Nat. Rev. Neurol. 2015, 11, 379–389.
  12. Malpas, C.B.; Manouchehrinia, A.; Sharmin, S.; Roos, I.; Horakova, D.; Havrdova, E.K.; Trojano, M.; Izquierdo, G.; Eichau, S.; Bergamaschi, R.; et al. Aggressive form of multiple sclerosis can be predicted early after disease onset. Mult. Scler. J. 2019, 25, 605–607.
  13. Tintore, M.; Arrambide, G.; Otero-Romero, S.; Carbonell-Mirabent, P.; Río, J.; Tur, C.; Comabella, M.; Nos, C.; Arévalo, M.J.; Anglada, E.; et al. The long-term outcomes of CIS patients in the Barcelona inception cohort: Looking back to recognize aggressive MS. Mult. Scler. J. 2020, 26, 1658–1669.
  14. Saccardi, R.; Freedman, M.; Sormani, M.P.; Atkins, H.; Farge, D.; Griffith, L.; Kraft, G.; Mancardi, G.L.; Nash, R.; Pasquini, M.; et al. A prospective, randomized, controlled trial of autologous haematopoietic stem cell transplantation for aggressive multiple sclerosis: A position paper. Mult. Scler. J. 2012, 18, 825–834.
  15. Edan, G.; Comi, G.; Le Page, E.; Leray, E.; Rocca, M.A.; Filippi, M. Mitoxantrone prior to interferon beta-1b in aggressive relapsing multiple sclerosis: A 3-year randomised trial. J. Neurol. Neurosurg. Psychiatry 2011, 82, 1344–1350.
  16. Freedman, M.S.; Rush, C.A. Severe, Highly Active, or Aggressive Multiple Sclerosis. Contin. Lifelong Learn. Neurol. 2016, 22, 761–784.
  17. Bowen, J.D. Highly Aggressive Multiple Sclerosis. Contin. Lifelong Learn. Neurol. 2019, 25, 689–714.
  18. Ellenberger, D.; Flachenecker, P.; Fneish, F.; Frahm, N.; Hellwig, K.; Paul, F.; Stahmann, A.; Warnke, C.; Rommer, P.S.; Zettl, U.K. Aggressive multiple sclerosis: A matter of measurement and timing. Brain 2020, 143, e97.
  19. Kappos, L.; de Stefano, N.; Freedman, M.S.; Cree, B.; Radue, E.-W.; Sprenger, T.; Sormani, M.P.; Smith, T.; Häring, D.A.; Meier, D.P.; et al. Inclusion of brain volume loss in a revised measure of ‘no evidence of disease activity’ (NEDA-4) in relapsing–remitting multiple sclerosis. Mult. Scler. J. 2016, 22, 1297–1305.
  20. De Stefano, N.; Stromillo, M.L.; Giorgio, A.; Bartolozzi, M.L.; Battaglini, M.; Baldini, M.; Portaccio, E.; Amato, M.P.; Sormani, M.P. Establishing pathological cut-offs of brain atrophy rates in multiple sclerosis. J. Neurol. Neurosurg. Psychiatry 2016, 87, 93–99.
  21. Di Sabatino, E.; Gaetani, L.; Sperandei, S.; Fiacca, A.; Guercini, G.; Parnetti, L.; Di Filippo, M. The no evidence of disease activity (NEDA) concept in MS: Impact of spinal cord MRI. J. Neurol. 2021, 1–7.
  22. Prosperini, L.; Annovazzi, P.; Boffa, L.; Buscarinu, M.C.; Gallo, A.; Matta, M.; Moiola, L.; Musu, L.; Perini, P. No evidence of disease activity (NEDA-3) and disability improvement after alemtuzumab treatment for multiple sclerosis: A 36-month real-world study. J. Neurol. 2018, 265, 2851–2860.
  23. Pandit, L. No Evidence of Disease Activity (NEDA) in Multiple Sclerosis—Shifting the Goal Posts. Ann. Indian Acad. Neurol. 2019, 22, 261–263.
  24. Hobart, J.C.; Kalkers, N.F.; Barkhof, F.; Uitdehaag, B.M.J.; Polman, C.H.; Thompson, A. Outcome measures for multiple sclerosis clinical trials: Relative measurement precision of the Expanded Disability Status Scale and Multiple Sclerosis Functional C omposite. Mult. Scler. J. 2004, 10, 41–46.
  25. Hobart, J.; Freeman, J.; Thompson, A. Kurtzke scales revisited: The application of psychometric methods to clinical intuition. Brain 2000, 123 Pt 5, 1027–1040.
  26. Noseworthy, J.H.; Vandervoort, M.K.; Wong, C.J.; Ebers, G. Interrater variability with the Expanded Disability Status Scale (EDSS) and Functional Systems (FS) in a multiple sclerosis clinical trial. Neurology 1990, 40, 971–975.
  27. Meyer-Moock, S.; Feng, Y.-S.; Maeurer, M.; Dippel, F.-W.; Kohlmann, T. Systematic literature review and validity evaluation of the Expanded Disability Status Scale (EDSS) and the Multiple Sclerosis Functional Composite (MSFC) in patients with multiple sclerosis. BMC Neurol. 2014, 14, 58.
  28. Aboulenein-Djamshidian, F.; Krššák, M.; Serbecic, N.; Rauschka, H.; Beutelspacher, S.; Kukurová, I.J.; Valkovič, L.; Khan, A.; Prayer, D.; Kristoferitsch, W. CROP—The Clinico-Radiologico-Ophthalmological Paradox in Multiple Sclerosis: Are Patterns of Retinal and MRI Changes Heterogeneous and Thus Not Predictable? PLoS ONE 2015, 10, e0142272.
  29. Cinar, B.P.; Yorgun, Y.G. What We Learned from The History of Multiple Sclerosis Measurement: Expanded Disease Status Scale. Arch. Neuropsychiatry 2018, 55 (Suppl. 1), S69–S75.
  30. Bin Sawad, A.; Seoane-Vazquez, E.; Rodriguez-Monguio, R.; Turkistani, F. Evaluation of the Expanded Disability Status Scale and the Multiple Sclerosis Functional Composite as clinical endpoints in multiple sclerosis clinical trials: Quantitative meta-analyses. Curr. Med. Res. Opin. 2016, 32, 1969–1974.
  31. Rudick, R.A.; Miller, D.; Bethoux, F.; Rao, S.M.; Lee, J.-C.; Stough, D.; Reece, C.; Schindler, D.; Mamone, B.; Alberts, J. The Multiple Sclerosis Performance Test (MSPT): An iPad-Based Disability Assessment Tool. J. Vis. Exp. 2014, e51318.
  32. Lublin, F.D.; Reingold, S.C.; Cohen, J.A.; Cutter, G.R.; Sørensen, P.S.; Thompson, A.J.; Wolinsky, J.S.; Balcer, L.J.; Banwell, B.; Barkhof, F.; et al. Defining the clinical course of multiple sclerosis: The 2013 revisions. Neurology 2014, 83, 278–286.
  33. Kurtzke, J.F. Historical and Clinical Perspectives of the Expanded Disability Status Scale. Neuroepidemiology 2008, 31, 1–9.
  34. Edelman, R.R. The History of MR Imaging as Seen through the Pages of Radiology. Radiology 2014, 273, S181–S200.
  35. Filippi, M.; Rocca, M.A.; Ciccarelli, O.; de Stefano, N.; Evangelou, N.; Kappos, L.; Rovira, A.; Sastre-Garriga, J.; Tintorè, M.; Frederiksen, J.L.; et al. MRI criteria for the diagnosis of multiple sclerosis: MAGNIMS consensus guidelines. Lancet Neurol. 2016, 15, 292–303.
  36. Swanton, J.; Fernando, K.; Miller, D. Early prognosis of multiple sclerosis. Handb. Clin. Neurol. 2014, 122, 371–391.
  37. Barnett, Y.; Garber, J.Y.; Barnett, M.H. MRI biomarkers of disease progression in multiple sclerosis: Old dog, new tricks? Quant. Imaging Med. Surg. 2020, 10, 527–532.
  38. Filippi, M.; Preziosa, P.; Banwell, B.L.; Barkhof, F.; Ciccarelli, O.; de Stefano, N.; Geurts, J.J.G.; Paul, F.; Reich, D.S.; Toosy, A.T.; et al. Assessment of lesions on magnetic resonance imaging in multiple sclerosis: Practical guidelines. Brain 2019, 142, 1858–1875.
  39. Filippi, M.; Rocca, M.A. New magnetic resonance imaging biomarkers for the diagnosis of multiple sclerosis. Expert Opin. Med. Diagn. 2012, 6, 109–120.
  40. Reich, D.S.; Lucchinetti, C.F.; Calabresi, P.A. Multiple Sclerosis. N. Engl. J. Med. 2018, 378, 169–180.
  41. Gajofatto, A.; Calabrese, M.; Benedetti, M.D.; Monaco, S. Clinical, MRI, and CSF Markers of Disability Progression in Multiple Sclerosis. Dis. Markers 2013, 35, 687–699.
  42. McKinley, R.; Wepfer, R.; Grunder, L.; Aschwanden, F.; Fischer, T.; Friedli, C.; Muri, R.; Rummel, C.; Verma, R.; Weisstanner, C.; et al. Automatic detection of lesion load change in Multiple Sclerosis using convolutional neural networks with segmentation confidence. NeuroImage Clin. 2020, 25, 102104.
  43. Sarbu, N.; Shih, R.Y.; Jones, R.V.; Horkayne-Szakaly, I.; Oleaga, L.; Smirniotopoulos, J.G. White Matter Diseases with Radiologic-Pathologic Correlation. RadioGraphics 2016, 36, 1426–1447.
  44. Fernando, M.S.; O’Brien, J.; Perry, R.H.; English, P.; Forster, G.; McMeekin, W.; Slade, J.Y.; Golkhar, A.; Matthews, F.; Barber, R.; et al. Comparison of the pathology of cerebral white matter with post-mortem magnetic resonance imaging (MRI) in the elderly brain. Neuropathol. Appl. Neurobiol. 2004, 30, 385–395.
  45. Haller, S.; Kovari, E.V.; Herrmann, F.R.; Cuvinciuc, V.; Tomm, A.-M.; Zulian, G.B.; Lovblad, K.-O.; Giannakopoulos, P.; Bouras, C. Do brain T2/FLAIR white matter hyperintensities correspond to myelin loss in normal aging? A radiologic-neuropathologic correlation study. Acta Neuropathol. Commun. 2013, 1, 14.
  46. Tawfik, A.I.; Kamr, W.H. Diagnostic value of 3D-FLAIR magnetic resonance sequence in detection of white matter brain lesions in multiple sclerosis. Egypt. J. Radiol. Nucl. Med. 2020, 51, 127.
  47. Van Waesberghe, J.H.; van Walderveen, M.A.; Castelijns, J.A.; Scheltens, P.; à Nijeholt, G.L.; Polman, C.H.; Barkhof, F. Patterns of lesion development in multiple sclerosis: Longitudinal observations with T1-weighted spin-echo and magnetization transfer MR. AJNR Am. J. Neuroradiol. 1998, 19, 675–683.
  48. Casserly, C.; Seyman, E.; Alcaide-Leon, P.; Guenette, M.; Lyons, C.; Sankar, S.; Svendrovski, A.; Baral, S.; Oh, J. Spinal Cord Atrophy in Multiple Sclerosis: A Systematic Review and Meta-Analysis. J. Neuroimaging 2018, 28, 556–586.
  49. Lin, X.; Tench, C.R.; Turner, B.; Blumhardt, L.D.; Constantinescu, C. Spinal cord atrophy and disability in multiple sclerosis over four years: Application of a reproducible automated technique in monitoring disease progression in a cohort of the interferon -1a (Rebif) treatment trial. J. Neurol. Neurosurg. Psychiatry 2003, 74, 1090–1094.
  50. Abdel-Aziz, K.; Ciccarelli, O. Rationale for quantitative MRI of the human spinal cord and clinical applications. In Wheeler-Kingshott CAMBT-QMRI of the SC; Chapter 1.1; Cohen-Adad, J., Ed.; Academic Press: San Diego, CA, USA, 2014; pp. 3–21.
  51. Sastre-Garriga, J.; on behalf of the MAGNIMS Study Group; Pareto, D.; Battaglini, M.; Rocca, M.A.; Ciccarelli, O.; Enzinger, C.; Wuerfel, J.; Sormani, M.P.; Barkhof, F.; et al. MAGNIMS consensus recommendations on the use of brain and spinal cord atrophy measures in clinical practice. Nat. Rev. Neurol. 2020, 16, 171–182.
  52. Bo, L.; Geurts, J.J.G.; Mork, S.J.; van der Valk, P. Grey matter pathology in multiple sclerosis. Acta Neurol. Scand. 2006, 113, 48–50.
  53. Pirko, I.; Lucchinetti, C.F.; Sriram, S.; Bakshi, R. Gray matter involvement in multiple sclerosis. Neurology 2007, 68, 634–642.
  54. Calabrese, M.; Favaretto, A.; Martini, V.; Gallo, P. Grey matter lesions in MS: From histology to clinical implications. Prion 2013, 7, 20–27.
  55. Sastre-Garriga, J.; Ingle, G.T.; Chard, D.T.; Cercignani, M.; Ramió-Torrentà, L.; Miller, D.H.; Thompson, A.J. Grey and white matter volume changes in early primary progressive multiple sclerosis: A longitudinal study. Brain 2005, 128, 1454–1460.
  56. Uher, T.; Horakova, D.; Bergsland, N.; Tyblova, M.; Ramasamy, D.P.; Seidl, Z.; Vaneckova, M.; Krasensky, J.; Havrdova, E.; Zivadinov, R. MRI correlates of disability progression in patients with CIS over 48 months. NeuroImage Clin. 2014, 6, 312–319.
  57. Raz, E.; Cercignani, M.; Sbardella, E.; Totaro, P.; Pozzilli, C.; Bozzali, M.; Pantano, P. Gray- and White-Matter Changes 1 Year after First Clinical Episode of Multiple Sclerosis: MR Imaging. Radiology 2010, 257, 448–454.
  58. Varosanec, M.; Uher, T.; Horáková, D.; Hagemeier, J.; Bergsland, N.; Tyblova, M.; Seidl, Z.; Vaneckova, M.; Krasensky, J.; Dwyer, M.; et al. Longitudinal Mixed-Effect Model Analysis of the Association between Global and Tissue-Specific Brain Atrophy and Lesion Accumulation in Patients with Clinically Isolated Syndrome. Am. J. Neuroradiol. 2015, 36, 1457–1464.
  59. Thompson, A.J.; Banwell, B.L.; Barkhof, F.; Carroll, W.M.; Coetzee, T.; Comi, G.; Correale, J.; Fazekas, F.; Filippi, M.; Freedman, M.S.; et al. Diagnosis of multiple sclerosis: 2017 revisions of the McDonald criteria. Lancet Neurol. 2018, 17, 162–173.
  60. Brown, F.S.; Glasmacher, S.A.; Kearns, P.; MacDougall, N.; Hunt, D.; Connick, P.; Chandran, S. Systematic review of prediction models in relapsing remitting multiple sclerosis. PLoS ONE 2020, 15, e0233575.
  61. Pisani, A.I.; Scalfari, A.; Crescenzo, F.; Romualdi, C.; Calabrese, M. A novel prognostic score to assess the risk of progression in relapsing−remitting multiple sclerosis patients. Eur. J. Neurol. 2021, 28, 2503–2512.
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Subjects: Neuroimaging
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Vlad Tiu
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Update Date: 28 Mar 2022
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