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Updated: Mar 25, 2026
Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
Published on: July 28, 2013
Magnetic resonance analysis of malignant transformation in recurrent glioma
Llewellyn E Jalbert1, Evan Neill1, Joanna J Phillips1
1Joint Graduate Program in Bioengineering (L.E.J., S.J.N.), Department of Radiology & Biomedical Imaging (E.N., J.M.L., M.P.O., S.J.N.), Department of Pathology (J.J.P.), Department of Neurological Surgery (J.J.P., A.M.M., M.S.B., S.M.C.), Department of Biostatistics and Epidemiology (A.M.M.), University of California, San Francisco, San Francisco, California.
Background:
Patients with low-grade glioma (LGG) have a relatively long survival, and a balance is often struck between treating the tumor and impacting quality of life. While lesions may remain stable for many years, they may also undergo malignant transformation (MT) at the time of recurrence and require more aggressive intervention. Here we report on a state-of-the-art multiparametric MRI study of patients with recurrent LGG.
Methods:
One hundred and eleven patients previously diagnosed with LGG were scanned at either 1.5 T or 3 T MR at the time of recurrence. Volumetric and intensity parameters were estimated from anatomic, diffusion, perfusion, and metabolic MR data. Direct comparisons of histopathological markers from image-guided tissue samples with metrics derived from the corresponding locations on the in vivo images were made. A bioinformatics approach was applied to visualize and interpret these results, which included imaging heatmaps and network analysis. Multivariate linear-regression modeling was utilized for predicting transformation.
Results:
Many advanced imaging parameters were found to be significantly different for patients with tumors that had undergone MT versus those that had not. Imaging metrics calculated at the tissue sample locations highlighted the distinct biological significance of the imaging and the heterogeneity present in recurrent LGG, while multivariate modeling yielded a 76.04% accuracy in predicting MT.
Conclusions:
The acquisition and quantitative analysis of such multiparametric MR data may ultimately allow for improved clinical assessment and treatment stratification for patients with recurrent LGG.
