A Combined Radiomics and Machine Learning Approach to Overcome the Clinicoradiologic Paradox in Multiple Sclerosis

References

1. 1.↵
   1. Rovira A,
   2. Wattjes MP,
   3. Tintore M, et al

   ; MAGNIMS Study Group. Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis: clinical implementation in the diagnostic process. Nat Rev Neurol 2015;11:471–82 doi:10.1038/nrneurol.2015.106 pmid:26149978

   CrossRefPubMed
2. 2.↵
   1. Wattjes MP,
   2. Rovira A,
   3. Miller D, et al

   . Evidence-based guidelines: MAGNIMS consensus guidelines on the use of MRI in multiple sclerosis: establishing disease prognosis and monitoring patients. Nat Rev Neurol 2015;11:597–606 doi:10.1038/nrneurol.2015.157 pmid:26369511

   CrossRefPubMed
3. 3.↵
   1. van Munster CE,
   2. Uitdehaag BM

   . Outcome measures in clinical trials for multiple sclerosis. CNS drugs 2017;31:217–36 doi:10.1007/s40263-017-0412-5 pmid:28185158

   CrossRefPubMed
4. 4.↵
   1. Giovannoni G,
   2. Turner B,
   3. Gnanapavan S, et al

   . Is it time to target no evident disease activity (NEDA) in multiple sclerosis? Mult Scler Relat Disord 2015;4:329–33 doi:10.1016/j.msard.2015.04.006 pmid:26195051

   CrossRefPubMed
5. 5.↵
   1. Chard D,
   2. Trip SA

   . Resolving the clinico-radiological paradox in multiple sclerosis. F1000Res 2017;6:1828 doi:10.12688/f1000research.11932.1 pmid:29093810

   CrossRefPubMed
6. 6.↵
   1. Barkhof F

   . The clinico-radiological paradox in multiple sclerosis revisited. Curr Opin Neurol 2002;15:239–45 doi:10.1097/00019052-200206000-00003 pmid:12045719

   CrossRefPubMedWeb of Science
7. 7.↵
   1. Uitdehaag BM

   . Disability outcome measures in Phase III clinical trials in multiple sclerosis. CNS Drugs 2018;32:543–58 doi:10.1007/s40263-018-0530-8 pmid:29926371

   CrossRefPubMed
8. 8.↵
   1. Storelli L,
   2. Rocca MA,
   3. Pagani E, et al

   ; MAGNIMS Study Group. Measurement of whole-brain and gray matter atrophy in multiple sclerosis: assessment with MR imaging. Radiology 2018;288:554–64 doi:10.1148/radiol.2018172468 pmid:29714673

   CrossRefPubMed
9. 9.↵
   1. Kearney H,
   2. Miller DH,
   3. Ciccarelli O

   . Spinal cord MRI in multiple sclerosis–diagnostic, prognostic and clinical value. Nat Rev Neurol 2015;11:327–38 doi:10.1038/nrneurol.2015.80 pmid:26009002

   CrossRefPubMed
10. 10.↵
    1. Calabrese M,
    2. Filippi M,
    3. Gallo P

    . Cortical lesions in multiple sclerosis. Nat Rev Neurol 2010;6:438–44 doi:10.1038/nrneurol.2010.93 pmid:20625376

    CrossRefPubMedWeb of Science
11. 11.↵
    1. Moll NM,
    2. Rietsch AM,
    3. Thomas S, et al

    . Multiple sclerosis normal-appearing white matter: pathology-imaging correlations. Ann Neurol 2011;70:764–73 doi:10.1002/ana.22521 pmid:22162059

    CrossRefPubMed
12. 12.↵
    1. Pontillo G,
    2. Cocozza S,
    3. Lanzillo R, et al

    . Determinants of deep gray matter atrophy in multiple sclerosis: a multimodal MRI study. AJNR Am J Neuroradiol 2019;40:99–106 doi:10.3174/ajnr.A5915 pmid:30573464

    Abstract/FREE Full Text
13. 13.↵
    1. Tewarie P,
    2. Steenwijk MD,
    3. Tijms BM, et al

    . Disruption of structural and functional networks in long-standing multiple sclerosis. Hum Brain Mapp 2014;35:5946–61 doi:10.1002/hbm.22596 pmid:25053254

    CrossRefPubMed
14. 14.↵
    1. Traboulsee A,
    2. Simon JH,
    3. Stone L, et al

    . Revised Recommendations of the Consortium of MS Centers Task Force for a Standardized MRI Protocol and Clinical Guidelines for the Diagnosis and Follow-Up of Multiple Sclerosis. AJNR Am J Neuroradiol 2016;37:394–401 doi:10.3174/ajnr.A4539 pmid:26564433

    Abstract/FREE Full Text
15. 15.↵
    1. Kuceyeski A,
    2. Maruta J,
    3. Relkin N, et al

    . The Network Modification (NeMo) tool: elucidating the effect of white matter integrity changes on cortical and subcortical structural connectivity. Brain Connect 2013;3:451–63 doi:10.1089/brain.2013.0147 pmid:23855491

    CrossRefPubMed
16. 16.↵
    1. Gillies RJ,
    2. Kinahan PE,
    3. Hricak H

    . Radiomics: images are more than pictures, they are data. Radiology 2016;278:563–77 doi:10.1148/radiol.2015151169 pmid:26579733

    CrossRefPubMed
17. 17.↵
    1. Abraham A,
    2. Pedregosa F,
    3. Eickenberg M, et al

    . Machine learning for neuroimaging with scikit-learn. Front Neuroinform 2014;8:14 doi:10.3389/fninf.2014.00014 pmid:24600388

    CrossRefPubMed
18. 18.↵
    1. Polman CH,
    2. Reingold SC,
    3. Banwell B, et al

    . Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria. Ann Neurol 2011;69:292–302 doi:10.1002/ana.22366 pmid:21387374

    CrossRefPubMedWeb of Science
19. 19.↵
    1. Lublin FD

    . New multiple sclerosis phenotypic classification. Eur Neurol 2014;72 (Suppl 1):1–5 doi:10.1159/000367614 pmid:25278115

    CrossRefPubMed
20. 20.↵
    1. Tzourio-Mazoyer N,
    2. Landeau B,
    3. Papathanassiou D, et al

    . Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002;15:273–89 doi:10.1006/nimg.2001.0978 pmid:11771995

    CrossRefPubMedWeb of Science
21. 21.↵
    1. van Griethuysen JJ,
    2. Fedorov A,
    3. Parmar C, et al

    . Computational radiomics system to decode the radiographic phenotype. Cancer Res 2017;77:e104–07 doi:10.1158/0008-5472.CAN-17-0339 pmid:29092951

    Abstract/FREE Full Text
22. 22.↵
    1. Frank E,
    2. Hall M,
    3. Trigg L, et al

    . Data mining in bioinformatics using Weka. Bioinformatics 2004;20:2479–81 doi:10.1093/bioinformatics/bth261 pmid:15073010

    CrossRefPubMedWeb of Science
23. 23.↵
    1. Pedregosa F,
    2. Varoquaux G,
    3. Gramfort A, et al

    . Scikit-learn: machine learning in Python. J Mach Learn Res 2011;12:2825–30

    CrossRefPubMed
24. 24.↵
    1. Barkhof F

    . MRI in multiple sclerosis: correlation with Expanded Disability Status Scale (EDSS). Mult Scler 1999;5:283–86 doi:10.1177/135245859900500415 pmid:10467389

    CrossRefPubMedWeb of Science
25. 25.↵
    1. Hackmack K,
    2. Weygandt M,
    3. Wuerfel J, et al

    . Can we overcome the 'clinico-radiological paradox' in multiple sclerosis? J Neurol 2012;259:2151–60 doi:10.1007/s00415-012-6475-9 pmid:22446893

    CrossRefPubMed
26. 26.↵
    1. Roca P,
    2. Attye A,
    3. Colas L, et al

    ; OFSEP Investigators; Steering Committee; Investigators; Imaging Group. Artificial intelligence to predict clinical disability in patients with multiple sclerosis using FLAIR MRI. Diagn Interv Imaging 2020;101:795–802 doi:10.1016/j.diii.2020.05.009 pmid:32651155

    CrossRefPubMed
27. 27.↵
    1. Eshaghi A,
    2. Prados F,
    3. Brownlee WJ, et al

    ; MAGNIMS Study Group, Deep gray matter volume loss drives disability worsening in multiple sclerosis. Ann Neurol 2018;83:210–22 doi:10.1002/ana.25145 pmid:29331092

    CrossRefPubMed
28. 28.↵
    1. Azevedo CJ,
    2. Cen SY,
    3. Khadka S, et al

    . Thalamic atrophy in multiple sclerosis: a magnetic resonance imaging marker of neurodegeneration throughout disease. Ann Neurol 2018;83:223–24. doi:10.1002/ana.25150 pmid:29328531

    CrossRefPubMed
29. 29.↵
    1. Feinstein A,
    2. Roy P,
    3. Lobaugh N, et al

    . Structural brain abnormalities in multiple sclerosis patients with major depression. Neurology 2004;62:586–90 doi:10.1212/01.wnl.0000110316.12086.0c pmid:14981175

    CrossRefPubMed
30. 30.↵
    1. Cocozza S,
    2. Petracca M,
    3. Mormina E, et al

    . Cerebellar lobule atrophy and disability in progressive MS. J Neurol Neurosurg Psychiatry 2017;88:1065–72 doi:10.1136/jnnp-2017-316448 pmid:28844067

    Abstract/FREE Full Text
31. 31.↵
    1. Liu Y,
    2. Dong D,
    3. Zhang L, et al

    . Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder. Eur Radiol 2019;29:4670–77 doi:10.1007/s00330-019-06026-w pmid:30770971

    CrossRefPubMed
32. 32.↵
    1. Michoux N,
    2. Guillet A,
    3. Rommel D, et al

    . Texture analysis of T2-weighted MR images to assess acute inflammation in brain MS lesions. PLoS One 2015;10:e0145497 doi:10.1371/journal.pone.0145497 pmid:26693908

    CrossRefPubMed
33. 33.↵
    1. Zivadinov R,
    2. Tavazzi E,
    3. Bergsland N, et al

    . Brain iron at quantitative MRI is associated with disability in multiple sclerosis. Radiology 2018;289:487–96 doi:10.1148/radiol.2018180136 pmid:30015589

    CrossRefPubMed
34. 34.↵
    1. Chen JT,
    2. Collins DL,
    3. Atkins HL, et al

    ; Canadian MS/BMT Study Group. Magnetization transfer ratio evolution with demyelination and remyelination in multiple sclerosis lesions. Ann Neurol 2008;63:254–62 doi:10.1002/ana.21302 pmid:18257039

    CrossRefPubMedWeb of Science
35. 35.↵
    1. Meyer-Moock S,
    2. Feng YS,
    3. Maeurer M, et al

    . 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 doi:10.1186/1471-2377-14-58 pmid:24666846

    CrossRefPubMed
36. 36.↵
    1. Bologna M,
    2. Corino V,
    3. Mainardi L

    . Technical note: virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI-radiomics of the brain. Medical Phys 2019;46:5116–23 doi:10.1002/mp.13834 pmid:31539450

    CrossRefPubMed