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Non-invasive monitoring of response to treatment of glioblastoma (GB) is nowadays carried out using MRI. MRS and MR spectroscopic imaging (MRSI) constitute promising tools for this undertaking. A temozolomide (TMZ) protocol was optimized for GL261 GB. Sixty-three mice were studied by MRI/MRS/MRSI. The spectroscopic information was used for the classification of control brain and untreated and responding GB, and validated against post-mortem immunostainings in selected animals. A classification system was developed, based on the MRSI-sampled metabolome of normal brain parenchyma, untreated and responding GB, with a 93% accuracy. Classification of an independent test set yielded a balanced error rate of 6% or less. Classifications correlated well both with tumor volume changes detected by MRI after two TMZ cycles and with the histopathological data: a significant decrease (p < 0.05) in the proliferation and mitotic rates and a 4.6-fold increase in the apoptotic rate. A surrogate response biomarker based on the linear combination of 12 spectral features has been found in the MRS/MRSI pattern of treated tumors, allowing the non-invasive classification of growing and responding GL261 GB. The methodology described can be applied to preclinical treatment efficacy studies to test new antitumoral drugs, and begets translational potential for early response detection in clinical studies.

Original publication




Journal article


NMR Biomed

Publication Date





1333 - 1345


DMSO, GL261 glioblastoma, pattern recognition, perturbation-enhanced MRSI, therapy response detection, Animals, Antineoplastic Agents, Alkylating, Apoptosis, Brain, Brain Neoplasms, Cell Line, Tumor, Dacarbazine, Drug Administration Schedule, Female, Glioblastoma, Magnetic Resonance Imaging, Magnetic Resonance Spectroscopy, Metabolome, Mice, Mice, Inbred C57BL, Mitosis, Pattern Recognition, Automated, Temozolomide, Tumor Burden