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Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values.
- Source :
-
Magnetic resonance in medicine [Magn Reson Med] 2016 Oct; Vol. 76 (4), pp. 1149-57. Date of Electronic Publication: 2015 Oct 31. - Publication Year :
- 2016
-
Abstract
- Purpose: To demonstrate that a continuous-time random-walk (CTRW) diffusion model can improve diagnostic accuracy of differentiating low- and high-grade pediatric brain tumors.<br />Methods: Fifty-four children with histopathologically confirmed brain tumors underwent diffusion MRI scans at 3Twith 12 b-values (0-4000 s/mm(2) ). The diffusion imageswere fit to a simplified CTRW model to extract anomalous diffusion coefficient, Dm , and temporal and spatial heterogeneity parameters, α and β, respectively. Using histopathology results as reference, a k-means clustering algorithm and a receiver operating characteristic (ROC) analysis were employed to determine the sensitivity, specificity, and diagnostic accuracy of the CTRW parameters in differentiating tumor grades.<br />Results: Significant differences between the low- and high-grade tumors were observed in the CTRW parameters (p-values<0.001). The k-means analysis showed that the combination of three CTRW parameters produced higher diagnostic accuracy (85% vs. 75%) and specificity (83% vs. 54%) than the apparent diffusion coefficient (ADC) from a mono-exponential model. The ROC analysis revealed that any combination of the CTRW parameters gave a larger area under the curve (0.90-0.96) than using ADC (0.80).<br />Conclusion: With its sensitivity to intravoxel heterogeneity, the simplified CTRW model is useful for non-invasive grading of pediatric brain tumors, particularly when surgical biopsy is not feasible. Magn Reson Med 76:1149-1157, 2016. © 2015 Wiley Periodicals, Inc.<br /> (© 2015 Wiley Periodicals, Inc.)
- Subjects :
- Adolescent
Child
Child, Preschool
Computer Simulation
Data Interpretation, Statistical
Diagnosis, Differential
Female
Humans
Image Enhancement methods
Infant
Male
Neoplasm Grading
Reproducibility of Results
Sensitivity and Specificity
Algorithms
Brain Neoplasms diagnostic imaging
Brain Neoplasms pathology
Image Interpretation, Computer-Assisted methods
Models, Statistical
Pattern Recognition, Automated methods
Subjects
Details
- Language :
- English
- ISSN :
- 1522-2594
- Volume :
- 76
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Magnetic resonance in medicine
- Publication Type :
- Academic Journal
- Accession number :
- 26519663
- Full Text :
- https://doi.org/10.1002/mrm.26012