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Abstract #2403

Distinct Amino Acid Profiles in IDH Wild-Type and IDH-Mutant Glioma Subregions: In Vivo 1H MRSI with Optimized Long-TE sLASER at 3T

Seyma Alcicek1,2,3,4, Michael W. Ronellenfitsch2,3,4,5, Joachim P. Steinbach2,3,4,5, Katharina J. Weber2,3,4,6, Vincent Prinz7, Marie-Thérèse Forster7, Elke Hattingen1,2,3,4, Ulrich Pilatus1, and Katharina J. Wenger1,2,3,4
1Institute of Neuroradiology, Goethe University, University Hospital Frankfurt, Frankfurt am Main, Germany, 2University Cancer Center Frankfurt (UCT), Frankfurt am Main, Germany, 3Frankfurt Cancer Institute (FCI), Frankfurt am Main, Germany, 4German Cancer Research Center (DKFZ) Heidelberg, Germany and German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, Frankfurt am Main, Germany, 5Dr. Senckenberg Institute of Neurooncology, Goethe University, University Hospital Frankfurt, Frankfurt am Main, Germany, 6Institute of Neurology (Edinger-Institute), Goethe University, University Hospital Frankfurt, Frankfurt am Main, Germany, 7Department of Neurosurgery, Goethe University, University Hospital Frankfurt, Frankfurt am Main, Germany

Synopsis

Keywords: Tumors (Pre-Treatment), Cancer, Amino acids, Metabolic reprogramming, Glioma, IDH mutation, Metabolic Profiling

Motivation: Reprogramming of amino acid (AA) metabolism is important in glioma formation and growth. The AA pool could provide potential markers for diagnosis, prognosis, and personalized care based on patients’ unique tumor metabolic profiles.

Goal(s): To develop an MRSI protocol at 3T with clinically-feasible acquisition time to quantify/map AA metabolism in glioma subregions in IDH-wildtype and IDH-mutated glioma.

Approach: We measured 30 patients with suspected glioma using a long-TE sLASER MRSI protocol and performed automated brain tumor segmentation. Metabolite maps were analyzed taking tumor subregions into account.

Results: Distinct spectral patterns and AA maps between glioma subtypes were observed.

Impact: We used optimized long-TE-sLASER MRSI at 3T and automated brain tumor segmentation to evaluate distinct amino acid profiles in glioma molecular subtypes. Our study highlights the necessity for separate (e.g., glutamate-glutamine, glycine-myoinositol) and tumor-subregion-specific quantification of metabolites.

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Abstract

Keywords