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Abstract #3693

7T MRSI-based k-means clustering of glioma

Cornelius Cadrien1, Philipp Lazen1, Huskic Sara1, Acharya Sagar1, Roxane Licandro1, Julia Furtner1, Lukas Hingerl1, Bernhard Strasser1, Preusser Matthias1, Kiesel Barbara1, Mischkulnig Mario1, Rötzer-Pejrimovsky Thomas1, Woehrer Adelheid1, Weber Michael1, Dorfer Christian1, Trattnig Siegfried1, Roessler Karl1, Bogner Wolfgang1, Georg Widhalm1, and Gilbert Hangel1
1medical university of vienna, vienna, Austria

Synopsis

Keywords: Tumors (Pre-Treatment), Brain

Motivation: Preoperative glioma classification and management is still a challenge to be solved.

Goal(s): To enhance the understanding of glioma characteristics and potentially improve patient outcomes, we analyzed 7T Magnetic Resonance Spectroscopic Imaging (MRSI) data in 36 glioma patients.

Approach: Our approach focused on k-Means clustering of 60 metabolic ratios in the tumor to identify an overlap with the WHO 2021 diagnosis. Important metabolic ratios identified include Glu+Gln/tCho and Ins/tCr ratios, highlighting their biomarker significance.

Results: Our 7T MRSI can add metabolic profiles across the entire glioma and brain, possibly contributing to future glioma research.

Impact: We performed k-Means clustering of preoperative 7T MRSI metabolic ratios in 36 glioma patients. Correlation with histological WHO 2021 diagnosis was identified. With this approach, we could potentially enhance surgery planning and optimize glioma treatment and targeted drug monitoring.

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