Neurosurgery · 2003
Proton magnetic resonance spectroscopic imaging can predict length of survival in patients with supratentorial gliomas.
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Kuznetsov Yevgeniy E, Caramanos Zografos, Antel Samson B, Preul Mark C, Leblanc Richard, Villemure Jean-Guy, Pokrupa Ronald, Olivier Andre, Sadikot Abbas, Arnold Douglas L
Аннотация
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.
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.