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Open Access Publications from the University of California

Scholarly Works (2 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2012)

Using monoexponential models, magnetic resonance (MR) parameters transverse relaxation time (T2) and apparent diffusion coefficient (ADC) have been used to non–invasively assess cell density, which characteristically increases with brain tumor malignancy. Multiexponential models of T2 and ADC may allow for a more accurate evaluation of cell density than the traditionally used monoexponential model. To better understand how cell density affects multicomponent T2 and ADC, tumor models with a range of cell densities were created by suspending astrocytoma cells in agarose at different densities. T2 was measured using a Carr–Purcell–Meiboom–Gill (CPMG) sequence with 64 echo times. ADC was measured using a diffusion–weighted sequence with 32 b–values. Three models were used to fit the data and determine the T2 values, ADC values, and associated fractions: monoexponential, biexponential, non–negative least squares (NNLS). Spearman Rank was used to test the correlation with cell density.

Both the monoexponential T2 and ADC were significantly negatively correlated with cell density. The biexponential model identified two T2 components, short and long. Both T2 components’ values and fractions were significantly correlated to cell density. The biexponential model identified two ADC components, slow and fast. Both ADC components’ values were significantly correlated with cell density, but not the fractions. The NNLS model identified up to three T2 and three ADC components. For the NNLS identified T2s, the components were labeled short, medium, and long. Both the NNLS short and medium T2 values and fractions were significantly correlated with cell density. For the NNLS identified ADCs, the components were labeled slow, intermediate, and fast. Only the fast fraction was significantly correlated with cell density.

The NNLS components obtained from samples of packed cells were further evaluated by washing the samples with a gadolinium contrast agent (Gd–DTPA) to shorten the T2 associated with the extracellular space. Gd–DTPA did not affect the short T2, which was considered to be associated with the intracellular space. The medium T2 component was no longer identified in samples with Gd–DTPA and was considered to be associated with the extracellular space. Overall, the results suggest that separately measurable components exist that could be utilized for compartment specific information.

    Cover page: Multiexponential T2 and Diffusion Magnetic Resonance Measurements of Glioma Cells
    • Article
    • Peer Reviewed
    UC Riverside Previously Published Works (2018)
    PURPOSE: Despite the intra- and intertumoral heterogeneity seen in glioblastoma multiforme (GBM), there is little definitive data on the underlying cause of the differences in patient survivals. Serial imaging assessment of tumor growth allows quantification of tumor growth kinetics (TGK) measured in terms of changes in the velocity of radial expansion seen on imaging. Because a systematic study of this entire TGK phenotype-growth before treatment and during each treatment to recurrence -has never been coordinately studied in GBMs, we sought to identify whether patients cluster into discrete groups on the basis of their TGK. PATIENTS AND METHODS: From our multi-institutional database, we identified 48 patients who underwent maximally safe resection followed by radiotherapy with imaging follow-up through the time of recurrence. The patients were then clustered into two groups through a k-means algorithm taking as input only the TGK before and during treatment. RESULTS: There was a significant survival difference between the clusters ( P = .003). Paradoxically, patients among the long-lived cluster had significantly larger tumors at diagnosis ( P = .027) and faster growth before treatment ( P = .003) but demonstrated a better response to adjuvant chemotherapy ( P = .048). A predictive model was built to identify which cluster patients would likely fall into on the basis of information that would be available to clinicians immediately after radiotherapy (accuracy, 90.3%). CONCLUSION: Dichotomizing the heterogeneity of GBMs into two populations-one faster growing yet more responsive with increased survival and one slower growing yet less responsive with shorter survival-suggests that many patients who receive standard-of-care treatments may get better benefit from select alternative treatments.
      Cover page: Distinct Phenotypic Clusters of Glioblastoma Growth and Response Kinetics Predict Survival.
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