Literature DB >> 8168042

Perfusion and metabolism in predicting the survival of patients with cerebral gliomas.

K Mineura1, T Sasajima, M Kowada, T Ogawa, J Hatazawa, F Shishido, K Uemura.   

Abstract

BACKGROUND: Noninvasive measurements of cerebral circulation and metabolism may be useful for diagnosis in patients with brain tumors. The authors tested the prognostic significance of circulatory and metabolic values or ratios determined by positron emission tomography (PET) in patients with gliomas.
METHODS: The subjects were 23 patients, who underwent a complete PET study of cerebral circulation and metabolism with long-term follow-up of at least 57 months. Regional cerebral blood flow (rCBF), blood volume (rCBV), oxygen extraction fraction, and the metabolic rates of oxygen (rCMRO2) and glucose (rCMRGl) were measured before treatment. Data regarding tumors, the contralateral gray matter and white matter, and the ratio between tumor and gray matter or between tumor and white matter were compared with survival time from the time of the PET study. Prognostic factors were tested using Cox's regression analysis.
RESULTS: Among clinical parameters, histologic grade and performance status were important variables regulating survival. When survival times of patients with values or ratios equal to or higher than the median were compared to those of patients with values or ratios lower than the median, significant determinant PET measurements were tumor rCMRGl and the ratios of tumor:gray rCMRGl and tumor:white rCMRGl. Median survival time in the patients with an rCMRGl value of 4.4 mg/100ml/minute (median value) or more indicated 9 months, which was significantly shorter than greater than 113 months in the patients with a value lower than the median (P = 0.003 by the generalized Wilcoxon test). Patients with a higher value of gray rCBF, rCBV, or rCMRO2 had significantly longer survival times than those with a lower value.
CONCLUSIONS: Cerebral circulation and metabolism as determined by PET can be of ancillary significance in predicting the prognosis of patients with gliomas.

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Year:  1994        PMID: 8168042     DOI: 10.1002/1097-0142(19940501)73:9<2386::aid-cncr2820730923>3.0.co;2-w

Source DB:  PubMed          Journal:  Cancer        ISSN: 0008-543X            Impact factor:   6.860


  25 in total

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