Regional White Matter Atrophy-Based Classification of Multiple Sclerosis in Cross-Sectional and Longitudinal Data

• Published in: American journal of neuroradiology : AJNR Vol. 30; no. 9; pp. 1731 - 1739
• Main Authors: Sampat, M.P, Berger, A.M, Healy, B.C, Hildenbrand, P, Vass, J, Meier, D.S, Chitnis, T, Weiner, H.L, Bakshi, R, Guttmann, C.R.G
• Format: Journal Article
• Language: English
• Published: Oak Brook, IL Am Soc Neuroradiology 01.10.2009; American Society of Neuroradiology
• Subjects: Anatomy, Cross-Sectional - methods; Atrophy - pathology; Biological and medical sciences; Brain; Brain - pathology; Female; Humans; Investigative techniques, diagnostic techniques (general aspects); Longitudinal Studies; Magnetic Resonance Imaging - methods; Male; Medical sciences; Middle Aged; Multiple Sclerosis - pathology; Multiple sclerosis and variants. Guillain barré syndrome and other inflammatory polyneuropathies. Leukoencephalitis; Nerve Fibers, Myelinated - pathology; Nervous system; Nervous system involvement in other diseases. Miscellaneous; Neurology; Radiodiagnosis. Nmr imagery. Nmr spectrometry; Atrophy; Nervous system diseases; Multiple sclerosis; Radiodiagnosis; Central nervous system disease; Classification; White matter; Inflammatory disease
• ISSN: 0195-6108; 1936-959X
• DOI: 10.3174/ajnr.A1659

The different clinical subtypes of multiple sclerosis (MS) may reflect underlying differences in affected neuroanatomic regions. Our aim was to analyze the effectiveness of jointly using the inferior subolivary medulla oblongata volume (MOV) and the cross-sectional area of the corpus callosum in distinguishing patients with relapsing-remitting multiple sclerosis (RRMS), secondary-progressive multiple sclerosis (SPMS), and primary-progressive multiple sclerosis (PPMS). We analyzed a cross-sectional dataset of 64 patients (30 RRMS, 14 SPMS, 20 PPMS) and a separate longitudinal dataset of 25 patients (114 MR imaging examinations). Twelve patients in the longitudinal dataset had converted from RRMS to SPMS. For all images, the MOV and corpus callosum were delineated manually and the corpus callosum was parcellated into 5 segments. Patients from the cross-sectional dataset were classified as RRMS, SPMS, or PPMS by using a decision tree algorithm with the following input features: brain parenchymal fraction, age, disease duration, MOV, total corpus callosum area and areas of 5 segments of the corpus callosum. To test the robustness of the classification technique, we applied the results derived from the cross-sectional analysis to the longitudinal dataset. MOV and central corpus callosum segment area were the 2 features retained by the decision tree. Patients with MOV >0.94 cm(3) were classified as having RRMS. Patients with progressive MS were further subclassified as having SPMS if the central corpus callosum segment area was <55.12 mm(2), and as having PPMS otherwise. In the cross-sectional dataset, 51/64 (80%) patients were correctly classified. For the longitudinal dataset, 88/114 (77%) patient time points were correctly classified as RRMS or SPMS. Classification techniques revealed differences in affected neuroanatomic regions in subtypes of multiple sclerosis. The combination of central corpus callosum segment area and MOV provides good discrimination among patients with RRMS, SPMS, and PPMS.