Apparent diffusion coefficient and fractional anisotropy of newly diagnosed grade II gliomas

UCSF/UCB Joint Graduate Group in Bioengineering, University of California, San Francisco, CA, USA.
NMR in Biomedicine (Impact Factor: 3.04). 05/2009; 22(4):449-55. DOI: 10.1002/nbm.1357
Source: PubMed


Distinguishing between low-grade oligodendrogliomas (ODs) and astrocytomas (AC) is of interest for defining prognosis and stratifying patients to specific treatment regimens. The purpose of this study was to determine if the apparent diffusion coefficient (ADC) and fractional anisotropy (FA) from diffusion imaging can help to differentiate between newly diagnosed grade II OD and AC subtypes and to evaluate the ADC and FA values for the mixed population of oligoastrocytomas (OA). Fifty-three patients with newly diagnosed grade II gliomas were studied using a 1.5T whole body scanner (23 ODs, 16 ACs, and 14 OAs). The imaging protocol included post-gadolinium T1-weighted images, T2-weighted images, and either three and/or six directional diffusion imaging sequence with b = 1000 s/mm(2). Diffusion-weighted images were analyzed using in-house software to calculate maps of ADC and for six directional acquisitions, FA. The intensity values were normalized by values from normal appearing white matter (NAWM) to generate maps of normalized apparent diffusion coefficient (nADC) and normalized fractional anisotropy (nFA). The hyperintense region in the T2 weighted image was defined as the T2All region. A Mann-Whitney rank-sum test was performed on the 25th, median, and 75th nADC and nFA among the three subtypes. Logistic regression was performed to determine how well the nADC and nFA predict subtype. Lesions diagnosed as being OD had significantly lower nADC and significantly higher nFA, compared to AC. The nADC and nFA values individually classified the data with an accuracy of 87%. Combining the two did not enhance the classification. The patients with OA had nADC and nFA values between those of OD and AC. This suggests that ADC and FA may be helpful in directing tissue sampling to the most appropriate regions for taking biopsies in order to make a definitive diagnosis.

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Available from: Susan M Chang, Oct 04, 2015
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    • "Fifty-six patients with newly diagnosed grade 2 glioma according to the World Health Organization who had been scanned 1 day before surgery were selected for this study, including 24 with oligodendrogliomas (10 females and 14 males; median age, 43 years; range, 21- 71 years), 18 with astrocytomas (7 females and 11 males; median age, 33.5 years; range, 22-52 years), and 14 with oligoastrocytomas (5 females and 9 males; median age, 45 years; range, 18-62 years). The ADC values for a subset of this patient population had been reported in a previous study [20], but the correlation of these data with other imaging parameters had not been considered. Informed consent to participate in the study was obtained using a protocol that had been reviewed and approved by the committee on human research at our institution. "
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    ABSTRACT: The purpose of this study was to derive quantitative parameters from magnetic resonance (MR) spectroscopic, perfusion, and diffusion imaging of grade 2 gliomas according to the World Health Organization and to investigate how these multiple imaging modalities can contribute to evaluating their histologic subtypes and spatial characteristics. MR spectroscopic, perfusion, and diffusion images from 56 patients with newly diagnosed grade 2 glioma (24 oligodendrogliomas, 18 astrocytomas, and 14 oligoastrocytomas) were retrospectively studied. Metabolite intensities, relative cerebral blood volume (rCBV), and apparent diffusion coefficient (ADC) were statistically evaluated. The 75th percentile rCBV and median ADC were significantly different between oligodendrogliomas and astrocytomas (P < .0001) and between oligodendrogliomas and oligoastrocytomas (P < .001). Logistic regression analysis identified both 75th percentile rCBV and median ADC as significant variables in the differentiation of oligodendrogliomas from astrocytomas and oligoastrocytomas. Group differences in metabolite intensities were not significant, but there was a much larger variation in the volumes and maximum values of metabolic abnormalities for patients with oligodendroglioma compared with the other tumor subtypes. Perfusion and diffusion imaging provide quantitative MR parameters that can help to differentiate grade 2 oligodendrogliomas from grade 2 astrocytomas and oligoastrocytomas. The large variations in the magnitude and spatial extent of the metabolic lesions between patients and the fact that their values are not correlated with the other imaging parameters indicate that MR spectroscopic imaging may provide complementary information that is helpful in targeting therapy, evaluating residual disease, and assessing response to therapy.
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    ABSTRACT: To use normalized apparent diffusion coefficient (nADC) histograms from patients with grade II oligodendroglioma (OD) and astrocytoma (AC) to generate RGB color maps that emphasize the differences between normal-appearing white matter (NAWM), oligo-like, and astro-like regions. NAWM and nonenhancing lesion (NEL) ADC values from 19 ODs and 11 ACs were summed to generate oligo-like (red), NAWM (green), and astro-like (blue) nADC histograms. These nADC histograms were then used to map nADC values to an RGB matrix. Color maps of oligodendroglial tumor regions were generally visualized in pink, while color maps of astrocytic tumor regions showed various shades of blue. This technique was also applied to 23 patients with the more mixed subtype, oligoastrocytoma (OA), which showed a mixture of both blue and pink, which in many cases appeared to bleed into each other and were blotchy. This technique allows for the visualization of biologically different regions within the whole tumor mass, which may aid in directing image-guided biopsies. This can be used to ensure that the biopsy is directed to regions that can more accurately define the dominant tumor characteristics.
    Journal of Magnetic Resonance Imaging 07/2009; 30(1):209-13. DOI:10.1002/jmri.21810 · 3.21 Impact Factor
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