Radiology
HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
QUICK SEARCH:   [advanced]


     

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Miki, Y.
Right arrow Articles by Kolson, D. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Miki, Y.
Right arrow Articles by Kolson, D. L.
(Radiology. 1999;213:395-399.)
© RSNA, 1999

Neuroradiology

Relapsing-Remitting Multiple Sclerosis: Longitudinal Analysis of MR Images-Lack of Correlation between Changes in T2 Lesion Volume and Clinical Findings1

Yukio Miki, MD, PhD, Robert I. Grossman, MD, Jayaram K. Udupa, PhD, Luogang Wei, MS, Marcia Polansky, ScD, Lois J. Mannon, BS, RT and Dennis L. Kolson, MD, PhD

1 From the Departments of Radiology (Y.M., R.I.G., J.K.U., L.W., L.J.M.) and Neurology (D.L.K.), Hospital of the University of Pennsylvania, Ground Fl, Founders, 3400 Spruce St, Philadelphia, PA 19104-4283; the Division of Biometrics, Hahnemann University, Philadelphia, Pa (M.P.); and the Department of Nuclear Medicine and Diagnostic Imaging, Kyoto University Hospital, Kyoto, Japan (Y.M.). Received June 1, 1998; revision requested July 22; final revision received January 8, 1999; accepted April 22. Supported in part by grants R01 NS29029 and M01-RR00040 from the National Institutes of Health, and grant RG2109B from the National Multiple Sclerosis Society. Address reprint requests to R.I.G. (e-mail: grossman@oasis .rad.upenn.edu).


   Abstract
TOP
 Abstract
Introduction
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
PURPOSE: To determine the relationship between T2 lesion volume and either disability measurements or change in T2 lesion volume over time in multiple sclerosis (MS).

MATERIALS AND METHODS: Eighteen patients (age range, 26–53 years) with clinically proved relapsing-remitting MS were examined every 6 months for over 2 years. Three-millimeter-thick contiguous images of the whole brain were obtained. T2 lesion volume was calculated with a highly reproducible volumetric computer method.

RESULTS: A substantial annual increase in T2 lesion volume, with a median annual increase of approximately 8%, was demonstrated. However, there was no significant correlation between absolute T2 lesion volume and either the absolute expanded disability status scale (EDSS) grade (P = .32) or the absolute ambulation index (AI) (P = .20). In addition, no significant correlation between change in T2 lesion volume and change in EDSS grade (P = .42) or AI (P = .37) was found. There was no significant correlation between T2 lesion volume and duration of disease (P = .08).

CONCLUSION: There is no significant correlation between T2 lesion volume and standardized disability measurements despite a substantial increase in T2 lesion volume over time. Patients have an increase in total T2 lesion volume in the brain regardless of their clinical status or disability measurements. T2 lesion volumes as outcomes in therapeutic clinical trials on MS should be viewed as secondary outcomes rather than as surrogate markers of clinical responses.

Index terms: Brain, diseases, 10.871 • Brain, MR, 18.121416 • Magnetic resonance (MR), volume measurement, 10.121416 • Sclerosis, multiple, 10.871


   Introduction
TOP
 Abstract
 Introduction
MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 
Multiple sclerosis (MS) is a disease commonly studied by using magnetic resonance (MR) imaging (1,2) in an attempt to analyze relationships between the intracranial lesions and the clinical status. However, such relationships have been difficult to establish, in part because of difficulty in defining biologically "active" lesions and in applying reproducible quantitative measures of this activity. In investigations (3–5), the behavior of individual MS lesions in the brain of patients has been analyzed by quantitating both the change in the size of the lesions and the absolute number of lesions over time. Although such quantitations have the potential to be useful, problems occur because large and punctate lesions are handled in a binary fashion. Recently developed techniques, through the implementation of computer technology at a variety of levels (1,2,6), may enable the accurate assessment of the volume of high-signal-intensity abnormalities on images obtained by using a long repetition time. The concept of "MS disease burden"—that is, the volume of presumed pathologic myelin-based lesions as determined on the basis of computer analysis of MR images—is a potentially powerful tool for understanding the natural history of MS and subsequently assessing the efficacy of specific therapeutic interventions.

In this article, we focus on the volumetric results of a prospective longitudinal MR imaging study of a cohort of patients with relapsing-remitting MS. Although there have been other studies in which volumetric methods were used, the present report represents a departure from previously published studies in two important ways. First, we developed a highly reproducible volumetric computer application. This automated program enables data to be compared without the problems of substantial interreader or intrareader variability (7). Interreader or intrareader differences in longitudinal studies are problematic when the variations in readings are equal to or greater than the variations in disease volume. Second, all of the data in our study were acquired with contiguous, 3-mm sections with a fast spin-echo pulse sequence. With this technique, one can attempt to minimize volume averaging with relatively thin sections while maximizing the magnetization transfer effect with the use of multiple 180° pulses in a fast spin-echo sequence. The longitudinal volumetric data in our cohort of untreated patients with relapsing-remitting MS were acquired for more than 24 months and then correlated with disease duration, study duration, and contemporaneously acquired disability measurements. The purpose of this study was to assess the relationship between T2 lesion volume and disability measurements and that between T2 lesion volume and change in T2 lesion volume over time in patients with MS.


   MATERIALS AND METHODS
TOP
 Abstract
 Introduction
 MATERIALS AND METHODS
RESULTS
 DISCUSSION
 References
 
Our cohort consisted of 18 patients (14 women, four men; age range, 26–53 years; mean age, 40.8 years) who had definite MS (8) with a relapsing-remitting course and were recruited into this study. This study was part of a larger investigation of the MR imaging–based natural history of MS that was supported by the National Institutes of Health. The study was approved by our institutional review board, and written informed consent was obtained from all of the subjects.

The patients were examined by a neurologist (D.L.K.) who specializes in the care of patients who have MS. Relapsing-remitting disease was defined according to the following criterion: at least two relapses over the preceding 2 years, with relapse defined as a new neurologic deficit or exacerbation of a previous deficit that was confirmed at clinical examination and had developed over a period of 1–5 days in a previously stable patient and lasted at least 48 hours. Each patient underwent MR imaging and clinical evaluation in approximately 6-month intervals throughout the duration of this study (range, 2–4 years; median, 3 years). The duration of disease in these patients at the start of the study ranged from 4.3 months to 10.7 years, with a median duration of 3.2 years (average, 3.9 years). No patient had previously received, or ever received during the course of the study, interferon beta-1b or copaxone. Thus, our cohort had not received immunomodulating therapy either before or during the interval of our study.

To obtain the lesion volumes reported herein, MR imaging was performed with a 1.5-T imaging unit (GE Medical Systems, Milwaukee, Wis) by using the following fast spin-echo MR imaging protocol: repetition time, 2,500 msec; effective echo times, 18 and 90 msec; field of view, 22 cm; matrix size, 256 x 192; number of sections acquired, 50; section thickness (contiguous, interleaved), 3 mm; echo train length, eight; pixel size, 0.86 mm. Each study was then transferred electronically to our medical image processing laboratory where the total lesion volume was calculated.

Lesion volume calculations were performed by using an internal version of 3DVIEWNIX software on a Sun Sparc 20 workstation (Sun Microsystems, Mountain View, Calif) with four processors (256 Mbyte RAM). The calculations were based on a theory of object definition in images called "fuzzy connectedness," which has been previously described (9). The premise of this theory is that object information, such as that regarding lesions on T2-weighted images, is inherently "fuzzy," and local image properties within an object exhibit spatial contiguity (ie, connectedness), which also is a fuzzy phenomenon (9). In the fuzzy connectedness method, both intermediate-weighted and T2-weighted signal intensities that are assigned to each voxel in the acquired image are used as a vector in determining the different elements—that is, white matter, gray matter, cerebrospinal fluid, and potential lesions—each as a three-dimensional fuzzy connected entity. All of the possible connecting paths between all possible pairs of voxels in the volume image are considered in determining the four types of elements.

The theory and algorithms of fuzzy connectedness (9) and the use of the method in MS lesion detection on intermediate- and T2–weighted image pairs (7) are described in detail elsewhere. First, white matter, gray matter, and cerebrospinal fluid are detected. Then, in the holes between the union of the white matter and gray matter, potential lesions are detected. These potential lesions are also fuzzy connected objects, which are then presented one by one to the operator, who either accepts or rejects them as true lesions. This procedure is based on the notion that computers outperform humans in delineating objects and humans outperform computers in recognition tasks (7,10–12). Because the software uses both intermediate- and T2-weighted images for the segmentation, lesions with increased T2 signal intensity that are adjacent to structures that contain cerebrospinal fluid, such as periventricular lesions or isolated arcuate fiber lesions, are not difficult for the software to recognize as lesions (13). The volume of the accepted lesions is then summed. This method has been validated in several studies (7,10) and in more than 300 data sets.

The coefficient of variation for intrareader and interreader variability based on the fuzzy connectedness method is 0.9% for total lesion volume (7). The time for user interaction per case is approximately 15 minutes and includes the time for defining white matter, gray matter, and cerebrospinal fluid and for accepting or rejecting lesions. The clinical parameters that were studied included the expanded disability status scale (EDSS) grade (15), ambulation index (AI) (16), and disease duration as measured from the date of the original diagnosis.

Data from the baseline studies obtained in patients were used to correlate T2 lesion volume with EDSS grade, AI, and duration of disease, and the Spearman rank test was used for analysis. To correlate change in T2 lesion volume with change in EDSS grade and change in AI, data from the baseline study and from the last study obtained in each patient, and the Spearman rank test were used. To correlate percent change in T2 lesion volume per year with EDSS grade, AI, duration of disease, and patient age, the Spearman rank test was used. To correlate EDSS grade and AI with duration of disease, the Spearman rank test was used. The Wilcoxon rank sum test was used to determine whether there was a significant difference in percent change in T2 lesion volume per year between the female and male patients. The Wilcoxon signed rank test was used to determine whether the percent change in T2 lesion volume on the last studies from the volume on the baseline studies was significantly different from zero.


   RESULTS
TOP
 Abstract
 Introduction
 MATERIALS AND METHODS
 RESULTS
DISCUSSION
 References
 
The results of our study demonstrated a substantial and consistent annual increase in T2 lesion volume from the baseline value in patients with relapsing-remitting MS. However, these changes did not correlate significantly with disease duration, change in EDSS grade, change in AI, age, or sex.

For all patients, the median baseline T2 lesion volume was 5,470 mm3 (average volume ± SD, 8,210 mm3 ± 7,390). The median percent change in T2 lesion volume over the 1st year was 7.1% (average ± SD, 4.3% ± 37.2; minimum, -95.4%; maximum, 46.9%). The median percent change in the 2nd year versus that in the 1st year was 8.7% (average, 19.3% ± 45.7; minimum, -23.0%; maximum, 169.2%) (Figure). The median baseline EDSS grade of the patients was 2.0, which increased to 2.5 during the more than 2-year duration of our study.



View larger version (25K):
[in this window]
[in a new window]
  		Figure 1. Graph shows T2 lesion volumes at baseline and in the 1st and 2nd years plotted on a patient-by-patient basis. Note the substantial variability in both the T2 lesion volume and the change in T2 lesion volume among patients.

 
Although we found that the change in T2 lesion volume from the baseline volume was significantly different from zero (P = .003), there was no significant correlation between T2 lesion volume and duration of disease (r = 0.42, P = .08), although the P value was close to having statistical significance. In addition, we found no significant correlation between duration of disease and percent change in T2 lesion volume per year (r = -0.15, P = .54).

Although median T2 lesion volumes increased 7%–8% per year, we found no significant correlation between T2 lesion volume and either EDSS grade (r = 0.25, P = .32) or AI (r = 0.32, P = .20), between change in T2 lesion volume and either change in EDSS grade (r = 0.20, P = .42) or change in AI (r = 0.22, P = .37), or between percent change in T2 lesion volume per year and either EDSS grade (r = 0.0021, P = .99) or AI (r = 0.31, P = .17). A significant correlation between EDSS grade and duration of disease (r = 0.49; P = .04) was noted, but no statistically significant correlation was found between AI and duration of disease (r = 0.33; P = .17). Finally, there was no correlation between patient age or sex and percent change in T2 lesion volume per year (r = -0.21; P = .40 and .66, respectively).


   DISCUSSION
TOP
 Abstract
 Introduction
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
References
 
The results of our study indicate that although the volume of lesions on T2-weighted images increases consistently and substantially in patients with relapsing-remitting MS who are followed up for several years, these changes are not predictive of clinical course. The results were generated by using a highly reproducible computerized volumetric program that has validated intrareader and interreader variability, with a coefficient of variation of less than 1.0%; thus, it is, to our knowledge, the most reliable means of quantitating T2 lesion volume in the brain reported to date (7). Conclusions about the natural history of MS, including response to drug treatments, are often based on the volumes of lesions on T2-weighted images as a primary or secondary end point and, by inference, a predictor of clinical outcome. To begin to better understand the evolution of MS lesions on T2-weighted images, we used our volumetric program for a cohort of longitudinally tracked patients with relapsing-remitting MS who had not received interferon beta or copaxone treatment.

Our volumetric program is unique in that the delineation of lesions is fully automated; intrareader and interreader variabilities arise from the recognition task only and not from the process of delineation. This is quite different from the other volumetric computer techniques that are widely used in North America and Europe, such as manual tracing (17–26) and thresholding techniques in which the threshold is manually determined (25–31). Our volumetric method is based on the notion that computer algorithms are superior to human analysis in the delineation of objects and human analysis is superior to computer algorithms in most recognition tasks (7,10–12). Human delineation of ill-defined objects such as MS lesions can be especially variable. Most of the computer-detected false lesion sites in our study were artifacts and/or the choroid plexus, and it is easy to distinguish these from true lesions.

The pulse sequence used in this study also was different from that in other reported series. We used a thin-section, fast spin-echo pulse sequence rather than a conventional spin-echo technique. Compared with conventional spin-echo sequences, fast spin-echo sequences have been reported to have equivalent sensitivity in the detection of MS lesions while dramatically reducing the acquisition time (31–33). We chose this technique to minimize volume averaging of MS lesions. Our section thickness was 3 mm, and the sections were contiguous. This section thickness is different from that in other series, in which the section thicknesses ranged from 5 mm to 1 cm (18,19,26,27,31,34–40).

An issue in this study, and perhaps in other studies of the ongoing natural history of MS, was the confounding problem of patients treated with drugs, including interferon beta. Our initial patient cohort was considerably larger than that reported herein. However, in this particular study, we confined our analysis to those patients with relapsing-remitting MS who had not received any therapy that has been reported to have an effect on T2 lesion volume.

The data presented indicate a lack of correlation between T2 lesion volume and either EDSS grade or AI and are consistent with those in some reports (3,4,18,20,41–45) but in contrast to those in others (22,36). There are several explanations for such inconsistencies. First, the EDSS grade and AI are motor-weighted measures with substantial intrareader and interreader variability (46–48), and they may be heavily influenced by spinal cord lesions, which were excluded in this study. Second, although T2 lesion volume is a marker of disease burden, it is not specific to different lesion types. For example, inflammation, demyelination, and the combinations of complex pathologic processes that occur in MS have a similar appearance on T2-weighted images. Yet, it is reasonable to conclude that not all of these processes have the same effect on neurologic function. One could expect to find the same high-signal-intensity T2 lesion in the internal capsule producing quite different neurologic symptoms, depending on the pathologic substrate. A related issue was the occurrence of lesions in "silent" regions of the brain (42,49). Lesions in these locations may not be identifiable with the standard clinical measurements that were used in our study, but they may produce disability that can be assessed by using more sophisticated examinations such as neuropsychologic testing (14,50–54).

Third, MR imaging is unable to demonstrate a burden of disease below its contrast and/or spatial resolution (MR occult lesions). This level of disease has been demonstrated by using a variety of techniques, including magnetization transfer imaging (55,56) and MR spectroscopy (57). Our volumetric program was designed to measure only the visible components of the disease burden. However, MR imaging–occult lesions have been deemed by some (2,55–57) to represent a very important component of disease burden. These points suggest that inclusion of MR imaging–occult lesions in measurements of total disease burden may be necessary to assess the relationship between EDSS grade and disease burden.

The results of MR imaging in our study clearly demonstrated an annual median volumetric change in T2 lesion volume of about 8% in patients with relapsing-remitting MS who were not taking immunomodulating medications. With this median annual rate, one could expect a 9-year doubling time in T2 lesion volume (ln2/ln1.08). One implication of this rate would be that patients who present with a high T2 lesion volume might be expected to develop a greater neurologic disability during a fixed observation period than that in patients who present with lower T2 lesion volumes. However, the results of our study suggested that there was no significant correlation between T2 lesion volume and duration of disease, whereas there was a substantial increase in T2 lesion volume over time. These findings suggest that other factors (eg, MR imaging–occult lesions, precise lesion histopathologic features, and spinal cord lesions) may have a major role in the disability in relapsing-remitting MS. In our study, the EDSS grade correlated significantly with the duration of disease, but the AI did not. This may be because the AI reflects more limited aspects of disabilities than does the EDSS grade (58).

Although we found a consistent annual median increase in T2 lesion volume, we found substantial variability in both the T2 lesion volume among patients and the percent change in T2 lesion volume among all patients (Figure). In addition, the percent change in T2 lesion volume had no significant correlation with physical disability, duration of disease, age, or sex, which suggests that the generation of T2 lesion disease is independent of these factors.

The results of our study differ slightly from those reported by Paty and Li (22), in which the median change in T2 lesion volume in the placebo group was 10.9% during the 1st year and 16.5% during the 2nd year. The reason for the difference in change in total disease burden between their study and ours is uncertain, but it may relate to differences in technique. Although we used our automated algorithm and 3-mm brain section images, Paty and Li (22) used several different MR imaging machines with varied magnetic field strengths and 10-mm-thick sections, and the lesion volume was calculated by using a manual tracing method (3,19,22). It is important, however, that the trend of increasing T2 lesion volumes over time in patients with relapsing-remitting MS was confirmed in both studies.

In summary, we quantitated the lesion volumes demonstrated on T2-weighted MR images of the brain in patients with a natural course of MS by using a highly reproducible computer method. We found a substantial increase over time in T2 lesion volume in relapsing-remitting MS, and substantial variety and variability in T2 lesion volumes in individual patients and among different patients with a similar clinical status. Despite the use of a highly reproducible quantitative imaging technique, such variability in intracranial T2 lesion volume and clinical status among patients urges caution in the use of T2 lesion volumetric measures as primary or surrogate outcomes in therapeutic trials of MS management. The limitation of this study may be the insensitivity of the standard clinical examinations. We did not correlate the neuropsychologic test results with the T2 lesion volumes; however, such tests of diffuse brain function could potentially reveal a correlation with T2 lesion volume.


   Footnotes
 
Abbreviations: AI = ambulation index EDSS = expanded disability status scale MS = multiple sclerosis

Author contributions: Guarantors of integrity of entire study, R.I.G., Y.M.; study concepts and design, R.I.G.; definition of intellectual content, R.I.G.; literature research, Y.M.; clinical studies, D.L.K.; data acquisition, L.J.M.; data analysis, Y.M., J.K.U., L.W.; statistical analysis, M.P.; manuscript preparation, Y.M.; manuscript editing, L.J.M.; manuscript review, all authors.


   References
TOP
 Abstract
 Introduction
 MATERIALS AND METHODS
 RESULTS
 DISCUSSION
 References
 

  1. Filippi M, Horsfield MA, Tofts PS, Barkhof F, Thompson AJ, Miller DH. Quantitative assessment of MRI lesion load in monitoring the evolution of multiple sclerosis. Brain 1995; 118:1601-1612.[Abstract/Free Full Text]
  2. Miller DH, Albert PS, Barkhof F, et al. Guidelines for the use of magnetic resonance techniques in monitoring the treatment of multiple sclerosis: US National MS Society Task Force. Ann Neurol 1996; 39:6-16.[Medline]
  3. Willoughby EW, Grochowski E, Li DK, Oger J, Kastrukoff LF, Paty DW. Serial magnetic resonance scanning in multiple sclerosis: a second prospective study in relapsing patients. Ann Neurol 1989; 25:43-49.[Medline]
  4. Kiel MK, Greenspun B, Grossman RI. Magnetic resonance imaging and degree of disability in multiple sclerosis. Arch Phys Med Rehabil 1988; 69:11-13.[Medline]
  5. Andersen O, Lycke J, Tollesson PO, et al. Linomide reduces the rate of active lesions in relapsing-remitting multiple sclerosis. Neurology 1996; 47:895-900.[Abstract/Free Full Text]
  6. Evans AC, Frank JA, Antel J, Miller DH. The role of MRI in clinical trials of multiple sclerosis: comparison of image processing techniques. Ann Neurol 1997; 41:125-132.[Medline]
  7. Udupa JK, Wei L, Samarasekera S, Miki Y, van Buchem MA, Grossman RI. Multiple sclerosis lesion quantification using fuzzy connectedness principles. IEEE Trans Med Imaging 1997; 16:598-609.[Medline]
  8. Poser CM, Paty DW, Scheinberg L, et al. New diagnostic criteria for multiple sclerosis: guidelines for research protocols. Ann Neurol 1983; 13:227-231.[Medline]
  9. Udupa JK, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms and applications in image segmentation. Graphical Models and Image Processing 1996; 58:246-261.
  10. Udupa JK, Wei L, Samarasekera S, Miki Y, van Buchem MA, Grossman RI. Detection and quantification of MS lesions using fuzzy topological principles. Proc SPIE 1996; 2710:81-91.
  11. Miki Y, Grossman RI, Udupa JK, et al. Computer-assisted quantitation of enhancing lesions in multiple sclerosis: correlation with clinical classification. AJNR 1997; 18:705-710.[Abstract/Free Full Text]
  12. Samarasekera S, Udupa JK, Miki Y, Luogang W, Grossman RI. A new computer-assisted method for the quantification of enhancing lesions in multiple sclerosis. J Comput Assist Tomogr 1997; 21:145-151.[Medline]
  13. Miki Y, Grossman RI, Udupa JK, et al. Differences between relapsing-remitting and chronic progressive multiple sclerosis as determined with quantitative MR imaging. Radiology 1999; 210:769-774.[Abstract/Free Full Text]
  14. Miki Y, Grossman RI, Udupa JK, et al. Isolated U-fiber involvement in MS: preliminary observations. Neurology 1998; 50:1301-1306.[Abstract/Free Full Text]
  15. Kurtzke JF. Rating neurological impairment in multiple sclerosis: an expanded disability status scale (EDSS). Neurology 1983; 33:1444-1452.[Abstract/Free Full Text]
  16. Hauser SL, Dawson DM, Lehrich JR, et al. Intensive immunosuppression in progressive multiple sclerosis: a randomized, three-arm study of high-dose intravenous cyclophosphamide, plasma exchange, and ACTH. N Engl J Med 1983; 308:173-180.[Abstract]
  17. Koopmans RA, Li DK, Oger JJ, et al. Chronic progressive multiple sclerosis: serial magnetic resonance brain imaging over six months. Ann Neurol 1989; 26:248-256.[Medline]
  18. Koopmans RA, Li DK, Grochowski E, Cutler PJ, Paty DW. Benign versus chronic progressive multiple sclerosis: magnetic resonance imaging features. Ann Neurol 1989; 25:74-81.[Medline]
  19. Kastrukoff LF, Oger JJ, Hashimoto SA, et al. Systemic lymphoblastoid interferon therapy in chronic progressive multiple sclerosis. I. Clinical and MRI evaluation.. Neurology 1990; 40:479-486.[Abstract/Free Full Text]
  20. Baumhefner RW, Tourtellotte WW, Syndulko K, et al. Quantitative multiple sclerosis plaque assessment with magnetic resonance imaging: its correlation with clinical parameters, evoked potentials, and intra-blood-brain barrier IgG synthesis. Arch Neurol 1990; 47:19-26.[Abstract/Free Full Text]
  21. The I FNB Multiple Sclerosis Study Group. Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. I. Clinical results of a multicenter, randomized, double-blind, placebo-controlled trial. Neurology 1993; 43:655-661.[Abstract/Free Full Text]
  22. Paty DW, Li DKB. Interferon beta-1b is effective in relapsing-remitting multiple sclerosis. II. MRI analysis results of a multicenter, randomized, double-blind, placebo-controlled trial: the UBC MS/MRI Study Group and the IFNB Multiple Sclerosis Study Group. Neurology 1993; 43:662-667.[Abstract/Free Full Text]
  23. Paty DW, Li DK, Oger JJ, et al. Magnetic resonance imaging in the evaluation of clinical trials in multiple sclerosis. Ann Neurol 1994; 36(suppl):95-96.
  24. The I FNB Multiple Sclerosis Study GroupThe University of British Columbia MS/MRI Analysis Group. Interferon beta-1b in the treatment of multiple sclerosis: final outcome of the randomized controlled trial. Neurology 1995; 45:1277-1285.[Abstract]
  25. Filippi M, Horsfield MA, Bressi S, et al. Intra- and inter-observer agreement of brain MRI lesion volume measurements in multiple sclerosis: a comparison of techniques. Brain 1995; 118:1593-1600.[Abstract/Free Full Text]
  26. Grimaud J, Lai M, Thorpe J, et al. Quantification of MRI lesion load in multiple sclerosis: a comparison of three computer-assisted techniques. Magn Reson Imaging 1996; 14:495-505.[Medline]
  27. Wicks DA, Tofts PS, Miller DH, et al. Volume measurement of multiple sclerosis lesions with magnetic resonance images: a preliminary study. Neuroradiology 1992; 34:475-479.[Medline]
  28. Filippi M, Barker GJ, Horsfield MA, et al. Benign and secondary progressive multiple sclerosis: a preliminary quantitative MRI study. J Neurol 1994; 241:246-251.[Medline]
  29. Filippi M, Horsfield MA, Campi A, Mammi S, Pereira C, Comi G. Resolution-dependent estimates of lesion volumes in magnetic resonance imaging studies of the brain in multiple sclerosis. Ann Neurol 1995; 38:749-754.[Medline]
  30. Stone LA, Albert PS, Smith ME, et al. Changes in the amount of diseased white matter over time in patients with relapsing-remitting multiple sclerosis. Neurology 1995; 45:1808-1814.[Abstract/Free Full Text]
  31. Rovaris M, Gawne-Cain ML, Wang L, Miller DH. A comparison of conventional and fast spin-echo sequences for the measurement of lesion load in multiple sclerosis using a semi-automated contour technique. Neuroradiology 1997; 39:161-165.[Medline]
  32. Thorpe JW, Halpin SF, MacManus DG, Barker GJ, Kendall BE, Miller DH. A comparison between fast and conventional spin-echo in the detection of multiple sclerosis lesions. Neuroradiology 1994; 36:388-392.[Medline]
  33. Miller DH. Magnetic resonance in monitoring the treatment of multiple sclerosis. Ann Neurol 1994; 36(suppl):91- 94.
  34. Kappos L, Stadt D, Ratzka M, et al. Magnetic resonance imaging in the evaluation of treatment in multiple sclerosis. Neuroradiology 1988; 30:299-302.[Medline]
  35. Pannizzo F, Stallmeyer MJ, Friedman J, et al. Quantitative MRI studies for assessment of multiple sclerosis. Magn Reson Med 1992; 24:90-99.[Medline]
  36. Filippi M, Horsfield MA, Morrissey SP, et al. Quantitative brain MRI lesion load predicts the course of clinically isolated syndromes suggestive of multiple sclerosis. Neurology 1994; 44:635-641.[Abstract/Free Full Text]
  37. Johnston B, Atkins MS, Mackiewich B, Anderson M. Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Trans Med Imaging 1996; 15:154-169.
  38. Broderick JP, Narayan S, Gaskill M, Dhawan AP, Khoury J. Volumetric measurement of multifocal brain lesions: implications for treatment trials of vascular dementia and multiple sclerosis. J Neuroimaging 1996; 6:36-43.[Medline]
  39. Bastianello S, Bozzao A, Paolillo A, et al. Fast spin-echo and fast fluid-attenuated inversion-recovery versus conventional spin-echo sequences for MR quantification of multiple sclerosis lesions. AJNR 1997; 18:699-704.[Abstract/Free Full Text]
  40. Mitchell JR, Jones C, Karlik SJ, et al. MR multispectral analysis of multiple sclerosis lesions. JMRI 1997; 7:499-511.
  41. Thompson AJ, Miller D, Youl B, et al. Serial gadolinium-enhanced MRI in relapsing/remitting multiple sclerosis of varying disease duration. Neurology 1992; 42:60-63.[Abstract/Free Full Text]
  42. Miller DH, Rudge P, Johnson G, et al. Serial gadolinium enhanced magnetic resonance imaging in multiple sclerosis. Brain 1988; 112:1057-1090.[Abstract/Free Full Text]
  43. Isaac C, Li DK, Genton M, et al. Multiple sclerosis: a serial study using MRI in relapsing patients. Neurology 1988; 38:1511-1515.[Abstract/Free Full Text]
  44. Kermode AG, Thompson AJ, Tofts P, et al. Breakdown of the blood-brain barrier precedes symptoms and other MRI signs of new lesions in multiple sclerosis: pathogenetic and clinical implications. Brain 1990; 113:1477-1489.[Abstract/Free Full Text]
  45. Harris JO, Frank JA, Patronas N, McFarlin DE, McFarland HF. Serial gadolinium-enhanced magnetic resonance imaging scans in patients with early, relapsing-remitting multiple sclerosis: implications for clinical trials and natural history. Ann Neurol 1991; 29:548-555.[Medline]
  46. Noseworthy JH, Vandervoort MK, Wong C, Ebers GC. Interrater variability with the expanded disability status scale (EDSS) and functional systems (FS) in a multiple sclerosis clinical trial. Neurology 1990; 1990:971-975.
  47. Willoughby E, Paty D. Scales for rating impairment in multiple sclerosis: a critique. Neurology 1988; 38:1793-1798.[Abstract/Free Full Text]
  48. Goodkin D, Cookfair D, Wende K, et al. Inter- and intrarater scoring agreement using grades 1.0 to 3.5 of the Kurtzke expanded disability status scale (EDSS). Neurology 1992; 42:859-863.[Abstract/Free Full Text]
  49. Paty DW. Multiple sclerosis: assessment of disease progression and effects of treatment. Can J Neurol Sci 1987; 14:518-520.[Medline]
  50. Huber SJ, Paulson GW, Shuttleworth EC, et al. Magnetic resonance imaging correlates of dementia in multiple sclerosis. Arch Neurol 1987; 44:732-736.[Abstract/Free Full Text]
  51. Rao SM, Leo GJ, Haughton VM, St Aubin-Faubert P, Bernardin L. Correlation of magnetic resonance imaging with neuropsychological testing in multiple sclerosis. Neurology 1989; 39:161-166.[Abstract/Free Full Text]
  52. Anzola GP, Bevilacqua L, Cappa SF, et al. Neuropsychological assessment in patients with relapsing-remitting multiple sclerosis and mild functional impairment: correlation with magnetic resonance imaging. J Neurol Neurosurg Psychiatry 1990; 53:142-145.[Abstract/Free Full Text]
  53. Swirsky ST, Mitchell DR, Seward J, et al. Neuropsychological and structural brain lesions in multiple sclerosis: a regional analysis. Neurology 1992; 42:1291-1295.[Abstract/Free Full Text]
  54. Grossman M, Armstrong C, Onishi K, et al. Patterns of cognitive impairment in relapsing-remitting and chronic progressive multiple sclerosis. Neuropsychiatry, Neuropsychol Behav Neurol 1994; 7:194-210.
  55. Dousset V, Grossman RI, Ramer KN, et al. Experimental allergic encephalomyelitis and multiple sclerosis: lesion characterization with magnetization transfer imaging. Radiology 1992; 182:483-491[Erratum: Radiology 1992; 183:878.].[Abstract/Free Full Text]
  56. Filippi M, Campi A, Dousset V, et al. A magnetization transfer imaging study of normal-appearing white matter in multiple sclerosis. Neurology 1995; 45:478-482.[Abstract/Free Full Text]
  57. Husted CA, Goodin DS, Hugg JW, et al. Biochemical alterations in multiple sclerosis lesions and normal-appearing white matter detected by in vivo 31P and 1H spectroscopic imaging. Ann Neurol 1994; 36:157-165.[Medline]
  58. Sharrack B, Hughes RA. Clinical scales for multiple sclerosis. J Neurol Sci 1996; 135:1-9.[Medline]



This article has been cited by other articles:


Home page
 NeurologyHome page
M. Daumer, A. Neuhaus, S. Morrissey, R. Hintzen, and G. C. Ebers
MRI as an outcome in multiple sclerosis clinical trials
Neurology, February 24, 2009; 72(8): 705 - 711.
[Abstract] [Full Text] [PDF]

Home page
 Mult SclerHome page
M C. Otaduy, D Callegaro, L A Bacheschi, and C C Leite
Correlation of magnetization transfer and diffusion magnetic resonance imaging in multiple sclerosis
Multiple Sclerosis, November 1, 2006; 12(6): 754 - 759.
[Abstract] [PDF]

Home page
 NeurologyHome page
D.K.B. Li, U. Held, J. Petkau, M. Daumer, F. Barkhof, F. Fazekas, J. A. Frank, L. Kappos, D. H. Miller, J. H. Simon, et al.
MRI T2 lesion burden in multiple sclerosis: A plateauing relationship with clinical disability
Neurology, May 9, 2006; 66(9): 1384 - 1389.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Neuroradiol.Home page
J.-P. Ranjeva, B. Audoin, M. Van Au Duong, D. Ibarrola, S. Confort-Gouny, I. Malikova, E. Soulier, P. Viout, A. Ali-Cherif, J. Pelletier, et al.
Local Tissue Damage Assessed with Statistical Mapping Analysis of Brain Magnetization Transfer Ratio: Relationship with Functional Status of Patients in the Earliest Stage of Multiple Sclerosis
AJNR Am. J. Neuroradiol., January 1, 2005; 26(1): 119 - 127.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Neuroradiol.Home page
Y. Ge, R. I. Grossman, J. S. Babb, J. He, and L. J. Mannon
Dirty-Appearing White Matter in Multiple Sclerosis: Volumetric MR Imaging and Magnetization Transfer Ratio Histogram Analysis
AJNR Am. J. Neuroradiol., November 1, 2003; 24(10): 1935 - 1940.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Neuroradiol.Home page
B. S.Y. Li, J. Regal, B. J. Soher, L. J. Mannon, R. I. Grossman, and O. Gonen
Brain Metabolite Profiles of T1-Hypointense Lesions in Relapsing-Remitting Multiple Sclerosis
AJNR Am. J. Neuroradiol., January 1, 2003; 24(1): 68 - 74.
[Abstract] [Full Text] [PDF]

Home page
 RadiologyHome page
O. Gonen, D. M. Moriarty, B. S. Y. Li, J. S. Babb, J. He, J. Listerud, D. Jacobs, C. E. Markowitz, and R. I. Grossman
Relapsing-remitting Multiple Sclerosis and Whole-Brain N-Acetylaspartate Measurement: Evidence for Different Clinical Cohorts—Initial Observations
Radiology, October 1, 2002; 225(1): 261 - 268.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Neuroradiol.Home page
F. Bonneville, D. M. Moriarty, B. S.Y. Li, J. S. Babb, R. I. Grossman, and O. Gonen
Whole-Brain N-Acetylaspartate Concentration: Correlation with T2-Weighted Lesion Volume and Expanded Disability Status Scale Score in Cases of Relapsing-Remitting Multiple Sclerosis
AJNR Am. J. Neuroradiol., March 1, 2002; 23(3): 371 - 375.
[Abstract] [Full Text] [PDF]

Home page
 NeurologyHome page
R. Zivadinov, R. A. Rudick, R. De Masi, D. Nasuelli, M. Ukmar, R. S. Pozzi-Mucelli, A. Grop, G. Cazzato, and M. Zorzon
Effects of IV methylprednisolone on brain atrophy in relapsing-remitting MS
Neurology, October 9, 2001; 57(7): 1239 - 1247.
[Abstract] [Full Text] [PDF]

Home page
 RadiologyHome page
D. W. Morton, K. R. Maravilla, J. R. Meno, and H. R. Winn
Clinically Relevant Rat Model for Testing BOLD Functional MR Imaging Techniques by Using Single-Shot Echo-planar Imaging at 1.5 T
Radiology, February 1, 2001; 218(2): 598 - 601.
[Abstract] [Full Text]

Home page
 Am. J. Neuroradiol.Home page
G. Sinson, L. J. Bagley, K. M. Cecil, M. Torchia, J. C. McGowan, R. E. Lenkinski, T. K. McIntosh, and R. I. Grossman
Magnetization Transfer Imaging and Proton MR Spectroscopy in the Evaluation of Axonal Injury: Correlation with Clinical Outcome after Traumatic Brain Injury
AJNR Am. J. Neuroradiol., January 1, 2001; 22(1): 143 - 151.
[Abstract] [Full Text] [PDF]

Home page
 Am. J. Roentgenol.Home page
A. O. Nusbaum, D. Lu, C. Y. Tang, and S. W. Atlas
Quantitative Diffusion Measurements in Focal Multiple Sclerosis Lesions: Correlations with Appearance on TI-Weighted MR Images
Am. J. Roentgenol., September 1, 2000; 175(3): 821 - 825.
[Abstract] [Full Text] [PDF]

Home page
 RadiologyHome page
Y. Ge, R. I. Grossman, J. K. Udupa, L. Wei, L. J. Mannon, M. Polansky, and D. L. Kolson
Brain Atrophy in Relapsing-Remitting Multiple Sclerosis and Secondary Progressive Multiple Sclerosis: Longitudinal Quantitative Analysis
Radiology, March 1, 2000; 214(3): 665 - 670.
[Abstract] [Full Text]

This Article
Right arrow Abstract Freely available
Right arrow Figures Only
Right arrow Full Text (PDF)
Right arrow Submit a response
Right arrow Alert me when this article is cited
Right arrow Alert me when eLetters are posted
Right arrow Alert me if a correction is posted
Right arrow Citation Map
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Download to citation manager
Right arrow reprints & permissions
Citing Articles
Right arrow Citing Articles via HighWire
Right arrow Citing Articles via Google Scholar
Google Scholar
Right arrow Articles by Miki, Y.
Right arrow Articles by Kolson, D. L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Miki, Y.
Right arrow Articles by Kolson, D. L.

HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS
RADIOLOGY RADIOGRAPHICS RSNA JOURNALS ONLINE