J D Dworkin1, K A Linn2, I Oguz3, G M Fleishman3, R Bakshi4,5,6, G Nair7, P A Calabresi8, R G Henry9, J Oh8,10, N Papinutto9, D Pelletier11, W Rooney12, W Stern9, N L Sicotte13, D S Reich7,8, R T Shinohara2. 1. From the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.) jdwor@pennmedicine.upenn.edu. 2. From the Departments of Biostatistics, Epidemiology, and Informatics (J.D.D., K.A.L., R.T.S.). 3. Radiology (I.O., G.M.F.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania. 4. Laboratory for Neuroimaging Research (R.B.), Partners Multiple Sclerosis Center, Ann Romney Center for Neurologic Diseases. 5. Departments of Neurology (R.B.). 6. Radiology (R.B.), Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 7. Translational Neuroradiology Section (G.N., D.S.R.), National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland. 8. Department of Neurology (P.A.C., J.O., D.S.R.), the Johns Hopkins University School of Medicine, Baltimore, Maryland. 9. Department of Neurology (R.G.H., N.P., W.S.), University of California, San Francisco, San Francisco, California. 10. Keenan Research Centre for Biomedical Science (J.O.), St. Michael's Hospital, University of Toronto, Toronto, Ontario, Canada. 11. Department of Neurology (D.P.), Keck School of Medicine, University of Southern California, Los Angeles, California. 12. Advanced Imaging Research Center (W.R.), Oregon Health & Science University, Portland, Oregon. 13. Department of Neurology (N.L.S.), Cedars-Sinai Medical Center, Los Angeles, California. A complete list of the NAIMS participants is provided in the acknowledgment section.
Abstract
BACKGROUND AND PURPOSE: Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions. MATERIALS AND METHODS: MR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites. RESULTS: The proposed count and the criterion standard count were highly correlated (r = 0.97, P < .001) and not significantly different (t59 = -.83, P = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated (t58 = -2.73, P < .01) with the Expanded Disability Status Scale. Average lesion size had a higher association with the Expanded Disability Status Scale (r = 0.35, P < .01) than lesion load (r = 0.10, P = .44) or lesion count (r = -.12, P = .36) alone. CONCLUSIONS: This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.
BACKGROUND AND PURPOSE: Lesion load is a common biomarker in multiple sclerosis, yet it has historically shown modest association with clinical outcome. Lesion count, which encapsulates the natural history of lesion formation and is thought to provide complementary information, is difficult to assess in patients with confluent (ie, spatially overlapping) lesions. We introduce a statistical technique for cross-sectionally counting pathologically distinct lesions. MATERIALS AND METHODS: MR imaging was used to assess the probability of a lesion at each location. The texture of this map was quantified using a novel technique, and clusters resembling the center of a lesion were counted. Validity compared with a criterion standard count was demonstrated in 60 subjects observed longitudinally, and reliability was determined using 14 scans of a clinically stable subject acquired at 7 sites. RESULTS: The proposed count and the criterion standard count were highly correlated (r = 0.97, P < .001) and not significantly different (t59 = -.83, P = .41), and the variability of the proposed count across repeat scans was equivalent to that of lesion load. After accounting for lesion load and age, lesion count was negatively associated (t58 = -2.73, P < .01) with the Expanded Disability Status Scale. Average lesion size had a higher association with the Expanded Disability Status Scale (r = 0.35, P < .01) than lesion load (r = 0.10, P = .44) or lesion count (r = -.12, P = .36) alone. CONCLUSIONS: This study introduces a novel technique for counting pathologically distinct lesions using cross-sectional data and demonstrates its ability to recover obscured longitudinal information. The proposed count allows more accurate estimation of lesion size, which correlated more closely with disability scores than either lesion load or lesion count alone.
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