The metabolic epicenter of supratentorial gliomas: a 1H-MRSI study.

BACKGROUND: Assessing the impact of glioma location on prognosis remains elusive. We approached the problem using multivoxel proton magnetic resonance spectroscopic imaging (1H-MRSI) to define a tumor "metabolic epicenter", and examined the relationship of metabolic epicenter location to survival and histopathological grade. METHODS: We studied 54 consecutive patients with a supratentorial glioma (astrocytoma or oligodendroglioma, WHO grades II-IV). The metabolic epicenter in each tumor was defined as the 1H-MRSI voxel containing maximum intra-tumoral choline on preoperative imaging. Tumor location was considered the X-Y-Z coordinate position, in a standardized stereotactic space, of the metabolic epicenter. Correlation between epicenter location and survival or grade was assessed. RESULTS: Metabolic epicenter location correlated significantly with patient survival for all tumors (r2 = 0.30, p = 0.0002) and astrocytomas alone (r2 = 0.32, p = 0.005). A predictive model based on both metabolic epicenter location and histopathological grade accounted for 70% of the variability in survival, substantially improving on histology alone to predict survival. Location also correlated significantly with grade (r2 = 0.25, p = 0.001): higher grade tumors had a metabolic epicenter closer to the midpoint of the brain. CONCLUSIONS: The concept of the metabolic epicenter eliminates several problems related to existing methods of classifying glioma location. The location of the metabolic epicenter is strongly correlated with overall survival and histopathological grade, suggesting that it reflects biological factors underlying glioma growth and malignant dedifferentiation. These findings may be clinically relevant to predicting patterns of local glioma recurrence, and in planning resective surgery or radiotherapy.

Proton magnetic resonance spectroscopic imaging can predict length of survival in patients with supratentorial gliomas.

OBJECTIVE: We compared the ability of proton magnetic resonance spectroscopic imaging ((1)H-MRSI) measures with that of standard clinicopathological measures to predict length of survival in patients with supratentorial gliomas. METHODS: We developed two sets of leave-one-out logistic regression models based on either 1) intratumoral (1)H-MRSI features, including maximum values of a) choline and b) lactate-lipid, c) number of (1)H-MRSI voxels with low N-acetyl group values, and d) number of (1)H-MRSI voxels with high lactate-lipid values, all (a-d) of which were normalized to creatine in normal-appearing brain, or 2) standard clinicopathological features, including a) tumor histopathological grade, b) patient age, c) performance of surgical debulking, and d) tumor diagnosis (i.e., oligodendroglioma, astrocytoma). We assessed the accuracy of these two models in predicting patient survival for 6, 12, 24, and 48 months by performing receiver operating characteristic curve analysis. Cox proportional hazards analysis was performed to assess the extent to which patient survival could be explained by the above predictors. We then performed a series of leave-one-out linear multiple regression analyses to determine how well patient survival could be predicted in a continuous fashion. RESULTS: The results of using the models based on (1)H-MRSI and clinicopathological features were equally good, accounting for 81 and 64% of the variability (r(2)) in patients' actual survival durations. All features except number of (1)H-MRSI voxels with lactate-lipid/creatine values of at least 1 were significant predictors of survival in the (1)H-MRSI model. Two features (tumor grade and debulking) were found to be significant predictors in the clinicopathological model. Survival as a continuous variable was predicted accurately on the basis of the (1)H-MRSI data (r = 0.77, P < 0.001; median prediction error, 1.7 mo). CONCLUSION: Our results suggest that appropriate analysis of (1)H-MRSI data can predict survival in patients with supratentorial gliomas at least as accurately as data derived from more invasive clinicopathological features.