Paper
14 April 2005 Quantitative MRI assessments of white matter in children treated for acute lymphoblastic leukemia
Wilburn E. Reddick, John O. Glass, Kathleen J. Helton, Chin-Shang Li, Ching-Hon Pui
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Abstract
The purpose of this study was to use objective quantitative MR imaging methods to prospectively assess changes in the physiological structure of white matter during the temporal evolution of leukoencephalopathy (LE) in children treated for acute lymphoblastic leukemia. The longitudinal incidence, extent (proportion of white matter affect), and intensity (elevation of T1 and T2 relaxation rates) of LE was evaluated for 44 children. A combined imaging set consisting of T1, T2, PD, and FLAIR MR images and white matter, gray matter and CSF a priori maps from a spatially normalized atlas were analyzed with a neural network segmentation based on a Kohonen Self-Organizing Map (SOM). Quantitative T1 and T2 relaxation maps were generated using a nonlinear parametric optimization procedure to fit the corresponding multi-exponential models. A Cox proportional regression was performed to estimate the effect of intravenous methotrexate (IV-MTX) exposure on the development of LE followed by a generalized linear model to predict the probability of LE in new patients. Additional T-tests of independent samples were performed to assess differences in quantitative measures of extent and intensity at four different points in therapy. Higher doses and more courses of IV-MTX placed patients at a higher risk of developing LE and were associated with more intense changes affecting more of the white matter volume; many of the changes resolved after completion of therapy. The impact of these changes on neurocognitive functioning and quality of life in survivors remains to be determined.
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Wilburn E. Reddick, John O. Glass, Kathleen J. Helton, Chin-Shang Li, and Ching-Hon Pui "Quantitative MRI assessments of white matter in children treated for acute lymphoblastic leukemia", Proc. SPIE 5746, Medical Imaging 2005: Physiology, Function, and Structure from Medical Images, (14 April 2005); https://doi.org/10.1117/12.594622
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KEYWORDS
Image segmentation

Magnetic resonance imaging

Tissues

Leukemia

Statistical analysis

Cancer

Error analysis

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