Differentiating low- and high-grade pediatric brain tumors using a continuous-time random-walk diffusion model at high b-values

Magn Reson Med. 2016 Oct;76(4):1149-57. doi: 10.1002/mrm.26012. Epub 2015 Oct 31.

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.

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.

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).

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.

Keywords: continuous-time-random-walk; high b-value diffusion imaging; non-Gaussian diffusion; pediatric brain tumor; tumor grading.

Publication types

  • Evaluation Study

MeSH terms

  • Adolescent
  • Algorithms*
  • Brain Neoplasms / diagnostic imaging*
  • Brain Neoplasms / pathology*
  • Child
  • Child, Preschool
  • Computer Simulation
  • Data Interpretation, Statistical
  • Diagnosis, Differential
  • Female
  • Humans
  • Image Enhancement / methods
  • Image Interpretation, Computer-Assisted / methods*
  • Infant
  • Male
  • Models, Statistical*
  • Neoplasm Grading
  • Pattern Recognition, Automated / methods*
  • Reproducibility of Results
  • Sensitivity and Specificity