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  • Author or Editor: Elias R. Melhem x
  • By Author: Krejza, Jaroslaw x
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Djamil Fertikh, Jaroslaw Krejza, Alain Cunqueiro, Shabbar Danish, Riyadh Alokaili and Elias R. Melhem

Object

The authors’ aim was to assess the ability of apparent diffusion coefficient (ADC) ratios in distinguishing brain abscesses from cystic or necrotic neoplasms.

Methods

Fifty-three patients with rim-enhancing masses in the brain observed on T1-weighted MR images were included: 26 had abscesses (14 bacterial, six nonbacterial, and six of unknown origin), 11 had glioblastoma multiforme, and 16 had rim-enhancing metastasis.

The ADC values, derived from diffusion-weighted imaging, were measured in the most homogeneous portion of the cystic component of the mass. The ADC ratios were calculated by dividing the ADC values from the nonenhancing cystic portion of the mass by the ADC values from contralateral normal-appearing white matter. Lesions were further differentiated based on presence, absence, or incompleteness of a T2 hypointensity rim.

The mean (± standard deviation) ADC ratios were significantly higher in neoplasms than in abscesses (2.45 ± 0.91 compared with 1.12 ± 0.53, p < 0.01). The accuracy of ADC ratios in discriminating abscesses from neoplasms, determined by the area under the receiver operating characteristic curve (Az), was high: 0.91 ± 0.04 (mean ± standard error of the mean [SEM]). The threshold of 1.7 was associated with highest efficiency (87%) in discriminating abscesses from neoplasms. If only bacterial abscesses were analyzed compared with neoplasms, the Az increased to 0.96 ± 0.03 (SEM). Using ADC ratios and T2 rim characteristics, 50 of 53 lesions were correctly classified (efficiency 94.3%).

Conclusions

The accuracy of ADC ratios in discriminating brain abscesses from cystic or necrotic neoplasms is very high and can be further improved using T2 rim characteristics.

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Robert G. Whitmore, Jaroslaw Krejza, Gurpreet S. Kapoor, Jason Huse, John H. Woo, Stephanie Bloom, Joanna Lopinto, Ronald L. Wolf, Kevin Judy, Myrna R. Rosenfeld, Jaclyn A. Biegel, Elias R. Melhem and Donald M. O'rourke

Object

Treatment of patients with oligodendrogliomas relies on histopathological grade and characteristic cytogenetic deletions of 1p and 19q, shown to predict radio- and chemosensitivity and prolonged survival. Perfusion weighted magnetic resonance (MR) imaging allows for noninvasive determination of relative tumor blood volume (rTBV) and has been used to predict the grade of astrocytic neoplasms. The aim of this study was to use perfusion weighted MR imaging to predict tumor grade and cytogenetic profile in oligodendroglial neoplasms.

Methods

Thirty patients with oligodendroglial neoplasms who underwent preoperative perfusion MR imaging were retrospectively identified. Tumors were classified by histopathological grade and stratified into two cytogenetic groups: 1p or 1p and 19q loss of heterozygosity (LOH) (Group 1), and 19q LOH only on intact alleles (Group 2). Tumor blood volume was calculated in relation to contralateral white matter. Multivariate logistic regression analysis was used to develop predictive models of cytogenetic profile and tumor grade.

Results

In World Health Organization Grade II neoplasms, the rTBV was significantly greater (p < 0.05) in Group 1 (mean 2.44, range 0.96–3.28; seven patients) compared with Group 2 (mean 1.69, range 1.27–2.08; seven patients). In Grade III neoplasms, the differences between Group 1 (mean 3.38, range 1.59–6.26; four patients) and Group 2 (mean 2.83, range 1.81–3.76; 12 patients) were not significant. The rTBV was significantly greater (p < 0.05) in Grade III neoplasms (mean 2.97, range 1.59–6.26; 16 patients) compared with Grade II neoplasms (mean 2.07, range 0.96–3.28; 14 patients). The models integrating rTBV with cytogenetic profile and grade showed prediction accuracies of 68 and 73%, respectively.

Conclusions

Oligodendroglial classification models derived from advanced imaging will improve the accuracy of tumor grading, provide prognostic information, and have potential to influence treatment decisions.