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Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions.

Sweeney EM, Shinohara RT, Dewey BE, Schindler MK, Muschelli J, Reich DS, Crainiceanu CM, Eloyan A - Neuroimage Clin (2015)

Bottom Line: The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist).We then relate the biomarker to the clinical information in a mixed model framework.Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States ; Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States.

ABSTRACT
The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.

No MeSH data available.


Related in: MedlinePlus

Distributions of the ratings for the two raters. The first row of plots shows the distributions of the ratings for the lesion segmentation, and the second row shows the ratings for the biomarker. Plots in the left column are ratings by the neuroradiologist, and plots on the right column are ratings by the neurologist. Each plot shows the number of studies that failed miserably (1), had some redeeming features (2), passed with minor errors (3), and passed (4) along with the percentage of each rating.
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f0025: Distributions of the ratings for the two raters. The first row of plots shows the distributions of the ratings for the lesion segmentation, and the second row shows the ratings for the biomarker. Plots in the left column are ratings by the neuroradiologist, and plots on the right column are ratings by the neurologist. Each plot shows the number of studies that failed miserably (1), had some redeeming features (2), passed with minor errors (3), and passed (4) along with the percentage of each rating.

Mentions: We use expert validation to determine the quality of the lesion segmentation (excluding edema tissue) and the ability of the biomarker to identify areas of slow, long-term intensity change. The distributions of the ratings for the two raters for both the lesion segmentation and the biomarker are shown in Fig. 5. The first row of plots in Fig. 5 shows the distribution of the ratings for the lesion segmentation and the second row shows the ratings for the biomarker. Plots in the left column are ratings by the neuroradiologist, and plots on the right column are ratings by the neurologist. The median rating for both the lesion segmentation and the biomarker by the neuroradiologist is 4 (95% CI: [4,4]), which is a rating of passed, the highest possible rating. The median rating for both the lesion segmentation and the biomarker by the neurologist are 3 (95% CI: [3,3]), which is a rating of passed with minor errors. Note that criteria for assigning scores were not discussed between the two raters prior to their respective analyses.


Relating multi-sequence longitudinal intensity profiles and clinical covariates in incident multiple sclerosis lesions.

Sweeney EM, Shinohara RT, Dewey BE, Schindler MK, Muschelli J, Reich DS, Crainiceanu CM, Eloyan A - Neuroimage Clin (2015)

Distributions of the ratings for the two raters. The first row of plots shows the distributions of the ratings for the lesion segmentation, and the second row shows the ratings for the biomarker. Plots in the left column are ratings by the neuroradiologist, and plots on the right column are ratings by the neurologist. Each plot shows the number of studies that failed miserably (1), had some redeeming features (2), passed with minor errors (3), and passed (4) along with the percentage of each rating.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4660378&req=5

f0025: Distributions of the ratings for the two raters. The first row of plots shows the distributions of the ratings for the lesion segmentation, and the second row shows the ratings for the biomarker. Plots in the left column are ratings by the neuroradiologist, and plots on the right column are ratings by the neurologist. Each plot shows the number of studies that failed miserably (1), had some redeeming features (2), passed with minor errors (3), and passed (4) along with the percentage of each rating.
Mentions: We use expert validation to determine the quality of the lesion segmentation (excluding edema tissue) and the ability of the biomarker to identify areas of slow, long-term intensity change. The distributions of the ratings for the two raters for both the lesion segmentation and the biomarker are shown in Fig. 5. The first row of plots in Fig. 5 shows the distribution of the ratings for the lesion segmentation and the second row shows the ratings for the biomarker. Plots in the left column are ratings by the neuroradiologist, and plots on the right column are ratings by the neurologist. The median rating for both the lesion segmentation and the biomarker by the neuroradiologist is 4 (95% CI: [4,4]), which is a rating of passed, the highest possible rating. The median rating for both the lesion segmentation and the biomarker by the neurologist are 3 (95% CI: [3,3]), which is a rating of passed with minor errors. Note that criteria for assigning scores were not discussed between the two raters prior to their respective analyses.

Bottom Line: The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist).We then relate the biomarker to the clinical information in a mixed model framework.Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue.

View Article: PubMed Central - PubMed

Affiliation: Department of Biostatistics, The Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, United States ; Translational Neuroradiology Unit, Division of Neuroimmunology and Neurovirology, National Institute of Neurological Disease and Stroke, National Institute of Health, Bethesda, MD 20892, United States.

ABSTRACT
The formation of multiple sclerosis (MS) lesions is a complex process involving inflammation, tissue damage, and tissue repair - all of which are visible on structural magnetic resonance imaging (MRI) and potentially modifiable by pharmacological therapy. In this paper, we introduce two statistical models for relating voxel-level, longitudinal, multi-sequence structural MRI intensities within MS lesions to clinical information and therapeutic interventions: (1) a principal component analysis (PCA) and regression model and (2) function-on-scalar regression models. To do so, we first characterize the post-lesion incidence repair process on longitudinal, multi-sequence structural MRI from 34 MS patients as voxel-level intensity profiles. For the PCA regression model, we perform PCA on the intensity profiles to develop a voxel-level biomarker for identifying slow and persistent, long-term intensity changes within lesion tissue voxels. The proposed biomarker's ability to identify such effects is validated by two experienced clinicians (a neuroradiologist and a neurologist). On a scale of 1 to 4, with 4 being the highest quality, the neuroradiologist gave the score on the first PC a median quality rating of 4 (95% CI: [4,4]), and the neurologist gave the score a median rating of 3 (95% CI: [3,3]). We then relate the biomarker to the clinical information in a mixed model framework. Treatment with disease-modifying therapies (p < 0.01), steroids (p < 0.01), and being closer to the boundary of abnormal signal intensity (p < 0.01) are all associated with return of a voxel to an intensity value closer to that of normal-appearing tissue. The function-on-scalar regression model allows for assessment of the post-incidence time points at which the covariates are associated with the profiles. In the function-on-scalar regression, both age and distance to the boundary were found to have a statistically significant association with the lesion intensities at some time point. The two models presented in this article show promise for understanding the mechanisms of tissue damage in MS and for evaluating the impact of treatments for the disease in clinical trials.

No MeSH data available.


Related in: MedlinePlus