Rate of Brain Atrophy in Benign vs Early Multiple Sclerosis

Gauthier, Susan A.; Berger, Annika; Liptak, Zsuzsanna; Yang, Dan; Egorova, Svetlana; Buckle, Guy J.; Glanz, Bonnie I.; Khoury, Samia J.; Bakshi, Rohit; Weiner, Howard L.; Guttmann, Charles R.G.

doi:10.1001/archneurol.2008.567

Cited by 45 publications

(24 citation statements)

References 27 publications

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“…In line with previous observation, 11,21 our findings confirm that a low cortical lesion burden is a main feature of patients having the benign MS course. Indeed, despite the huge difference in disease duration, the benign MS group was characterized by significantly lower CL number and volume, percentage of patients having CLs and number of gadolinium-enhancing CLs, as compared to the RRMS group.…”

Section: Discussionsupporting

confidence: 93%

Low degree of cortical pathology is associated with benign course of multiple sclerosis

et al. 2012

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Section: Discussionsupporting

confidence: 93%

Low degree of cortical pathology is associated with benign course of multiple sclerosis

et al. 2012

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“…Other investigations have demonstrated that T2 lesion load and presence of baseline gadolinium-enhancing lesions on MRI are the greatest predictors of disability and clinical course (43-44). Conversely, while brain atrophy has been found to be less severe among patients with benign MS (16), similar or greater degrees of RNFL loss were seen in our study and are likely reflective of the higher incidence of ON with its inherent visual pathway-specific axonal degeneration. Although patients with benign MS may experience less difficulty with ambulation by definition based on their EDSS, they have reduced QOL scores not only for vision but for overall aspects of physical functioning.…”

Section: Discussionsupporting

confidence: 75%

Visual Pathway Axonal Loss in Benign Multiple Sclerosis

Galetta

Graves

Talman

et al. 2012

Journal of Neuro-Ophthalmology

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Background Benign MS, traditionally defined as EDSS ≤3 and ≥15 years’ disease duration, is thought to follow a milder course. We determined the extent of visual pathway axonal loss by optical coherence tomography (OCT) retinal nerve fiber layer (RNFL) thickness in a benign MS cohort, and examined the relation to vision and quality of life (QOL). Methods In this longitudinal study of vision in MS at three academic centers, a subset of patients with EDSS, visual function, OCT, and QOL assessments was analyzed. Low- and high-contrast letter acuity were performed to assess visual function. RNFL thickness was determined using OCT-3. QOL scales included the 25-Item National Eye Institute Visual Functioning Questionnaire (NEI-VFQ-25) and SF-36. Results Among 68 patients (135 eyes) studied longitudinally, 13 (26 eyes) had benign MS using criteria of EDSS ≤3 and ≥15 years disease duration. Benign MS eyes had as much RNFL thinning (-3.6 μm, P=0.0008 vs. baseline, paired t-test) as typical MS eyes (-3.3 μm, P<0.0001). Both groups had significant low-contrast acuity loss. Prior history of optic neuritis (ON) was more frequent in benign MS (69% vs. 33% of eyes). History of ON distinguished benign vs. typical MS (P=0.002) and correlated with RNFL thickness at baseline (P=0.002) and disease duration (P=0.03), but not EDSS (P=0.32, logistic regression). NEI-VFQ-25 scores were also worse for benign MS, accounting for age (75±21 vs. 88±11, P=0.005). Conclusions Patients with benign MS have RNFL axonal loss that is as marked as that of typical MS, and have reduced vision and QOL. While overall neurologic impairment is mild, visual dysfunction, not well-captured by the EDSS, accounts for a substantial degree of disability in benign MS.

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“…[23–25] In contrast the T2 lesion volume ranked highly amongst predictors of progressive patients, indicating that accrual of lesions, and possibly relapses plays an important role in short-term and possibly long-term disability accrual as has been shown in other studies. [26–28].…”

Section: Discussionmentioning

confidence: 84%

Exploration of machine learning techniques in predicting multiple sclerosis disease course

et al. 2017

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ObjectiveTo explore the value of machine learning methods for predicting multiple sclerosis disease course.Methods1693 CLIMB study patients were classified as increased EDSS≥1.5 (worsening) or not (non-worsening) at up to five years after baseline visit. Support vector machines (SVM) were used to build the classifier, and compared to logistic regression (LR) using demographic, clinical and MRI data obtained at years one and two to predict EDSS at five years follow-up.ResultsBaseline data alone provided little predictive value. Clinical observation for one year improved overall SVM sensitivity to 62% and specificity to 65% in predicting worsening cases. The addition of one year MRI data improved sensitivity to 71% and specificity to 68%. Use of non-uniform misclassification costs in the SVM model, weighting towards increased sensitivity, improved predictions (up to 86%). Sensitivity, specificity, and overall accuracy improved minimally with additional follow-up data. Predictions improved within specific groups defined by baseline EDSS. LR performed more poorly than SVM in most cases. Race, family history of MS, and brain parenchymal fraction, ranked highly as predictors of the non-worsening group. Brain T2 lesion volume ranked highly as predictive of the worsening group.InterpretationSVM incorporating short-term clinical and brain MRI data, class imbalance corrective measures, and classification costs may be a promising means to predict MS disease course, and for selection of patients suitable for more aggressive treatment regimens.

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Rate of Brain Atrophy in Benign vs Early Multiple Sclerosis

Cited by 45 publications

References 27 publications

Low degree of cortical pathology is associated with benign course of multiple sclerosis

Low degree of cortical pathology is associated with benign course of multiple sclerosis

Visual Pathway Axonal Loss in Benign Multiple Sclerosis

Exploration of machine learning techniques in predicting multiple sclerosis disease course

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