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Research ArticleBrain
Open Access

Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI

E.M. Sweeney, R.T. Shinohara, C.D. Shea, D.S. Reich and C.M. Crainiceanu
American Journal of Neuroradiology January 2013, 34 (1) 68-73; DOI: https://doi.org/10.3174/ajnr.A3172
E.M. Sweeney
aFrom the Department of Biostatistics (E.M.S., R.T.S., D.S.R., C.M.C.), Johns Hopkins University, Baltimore, Maryland
bTranslational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke (E.M.S., R.T.S., C.D.S., D.S.R.)
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R.T. Shinohara
aFrom the Department of Biostatistics (E.M.S., R.T.S., D.S.R., C.M.C.), Johns Hopkins University, Baltimore, Maryland
bTranslational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke (E.M.S., R.T.S., C.D.S., D.S.R.)
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C.D. Shea
bTranslational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke (E.M.S., R.T.S., C.D.S., D.S.R.)
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D.S. Reich
aFrom the Department of Biostatistics (E.M.S., R.T.S., D.S.R., C.M.C.), Johns Hopkins University, Baltimore, Maryland
bTranslational Neuroradiology Unit, Neuroimmunology Branch, National Institute of Neurological Disease and Stroke (E.M.S., R.T.S., C.D.S., D.S.R.)
cDiagnostic Radiology Department, Clinical Center (D.S.R.), National Institutes of Health, Bethesda, Maryland.
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C.M. Crainiceanu
aFrom the Department of Biostatistics (E.M.S., R.T.S., D.S.R., C.M.C.), Johns Hopkins University, Baltimore, Maryland
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    Fig 1.

    Areas with lesion incidence are indicated with red boxes. A, Neuroradiologist manual segmentation of an axial section of the brain. B, Selected voxels for SuBLIME modeling. C, Axial section of the probability map from the full model. D, Axial section of the probability map from the SuBLIME model fit with only the T2-weighted image. E, Binary segmentation of the probability map from the full model with false-positive rate of 0.01 overlaid on the FLAIR image. F, Binary segmentation of the probability map from the full model with false-positive rate of 0.001 overlaid on the FLAIR image.

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    Fig 2.

    A, ROC curves for the voxel-level detection of incident and enlarging lesions for different thresholds of the probability maps produced from SuBLIME, as well as different thresholds of the T2-weighted subtraction without the model and voxel selection procedure. The red ROC curve is for the full model and has an AUC of 99% (95% CI [97%, 100%]). The blue ROC curve is for the model fit with only the T2-weighted image and has an AUC of 97% (95% CI [88%, 99%]). The green ROC curve is for thresholding the T2-weighted subtraction image without the model and voxel selection procedure and has an AUC of 92% (95% CI [83%, 95%]). B, Partial ROC curves for false-positive rates up to 0.01. The red curve is for the full model and the blue curve is for the model fit with only the T2-weighted image. The green curve is for thresholding the T2-weighted subtraction image without the model and voxel selection procedure.

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    Table 1:

    Subject demographic, diagnosis, and treatment information

    SetSubtypeAgeSexEDSSTreatment with Disease-Modifying Therapy (Baseline)
    ValidationRRMS37Male1.5Yes
    ValidationRRMS38Female2.0Yes
    ValidationRRMS48Male3.0No
    TrainingRRMS38Female1.5No
    TrainingRRMS30Female1.0Yes
    TrainingRRMS43Female1.5Yes
    ValidationRRMS35Female1.5Yes
    TrainingRRMS37Female1.0Yes
    TrainingRRMS47Female3.0Yes
    ValidationRRMS56Female1.0No
    • Note:—EDSS indicates Expanded Disability Status Scale; RRMS, relaping-remitting multiple sclerosis.

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    Table 2:

    Study scanning parameters

    FA (degrees)TR (ms)TE (ms)TI (ms)
    FLAIR(90, 90)(10,000, 10,000)(77.8, 159.5)(2200, 2500)
    T2-weighted(90, 90)(3400, 6500)(94.6, 112.0)NA
    PD(90, 90)(3400, 6500)(11.8, 15.0)NA
    T1-weighted(13, 20)(7.68, 10.3)(1.88, 4.05)(450, 750)a
    • Note:—FA indicates flip angle; NA, not available.

    • ↵a 106 of the T1-weighted scans did not have an inversion time.

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    Table 3:

    Binary segmentations

    False- Positive RateThreshold ValueSpecificitySensitivityVolume Change (Actual 625 mm3)
    0.010.00220.990.9530454
    0.0010.02290.9990.833520
    0.000250.08150.999750.541082
    0.00010.13960.99990.35509
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American Journal of Neuroradiology: 34 (1)
American Journal of Neuroradiology
Vol. 34, Issue 1
1 Jan 2013
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Cite this article
E.M. Sweeney, R.T. Shinohara, C.D. Shea, D.S. Reich, C.M. Crainiceanu
Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI
American Journal of Neuroradiology Jan 2013, 34 (1) 68-73; DOI: 10.3174/ajnr.A3172

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Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI
E.M. Sweeney, R.T. Shinohara, C.D. Shea, D.S. Reich, C.M. Crainiceanu
American Journal of Neuroradiology Jan 2013, 34 (1) 68-73; DOI: 10.3174/ajnr.A3172
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  • Improved Detection of New MS Lesions during Follow-Up Using an Automated MR Coregistration-Fusion Method
  • An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions
  • Improved Automatic Detection of New T2 Lesions in Multiple Sclerosis Using Deformation Fields
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