Abstract 2821: Spectral classifier as a complement to magnetic resonance spectroscopy in the management of patients with brain tumors of the central nervous system
Authors: Acosta Milena , Malaver Gina Catalina , Gamboa Oscar , Rodriguez Cesar , Beltran Julian , Moreno-Acosta Pablo 
Published: July 2021

Introduction: In the last decades molecular imaging, more precisely Magnetic Resonance Spectroscopy (MRS), has taken an important role in medical decision-making. The MRS provides biochemical information on metabolites on tissues, it also provides information regarding tissue metabolism, allowing to characterize some brain metabolites of a certain volume of the brain. With due experience, patterns of relative metabolite levels can be visually interpreted to arrive at radiological guidance, but the most robust result is obtained from quantitative analysis of the spectra using pattern recognition methods. Within these methods we find the Spectral Classifier (SC), it is a Java solution for designing and implementing MRS based classifiers. The main goal of SC is to allow users with minimum background knowledge of multivariate statistics to perform a fully automated pattern recognition analysis, it is able to read low resolution in-vivo MRS (single-voxel and multi-voxel) and high resolution tissue MRS.

Objective: To implement the use of the SC as a tool for the processing of Magnetic Resonance Spectroscopy (MRE) studies in patients with brain tumors of the central nervous system treated at the National Cancer Institute, Bogotá, Colombia.

Methods: The spectral acquisition parameters in the Siemens 1.5T resonator were homogenized, the spectra acquisition protocol for ERM was modified by including new single and multivoxel voxel sequences (10x10 voxel mesh). 63 studies were carried out using single and multivoxel voxel, which were processed with the jMRUI and jMRUI2xml plugin of the SC program, a pattern recognition program, which takes the information and introduces it into a glial tumor database, allowing it to classify the masses depending on the pattern of their metabolites, and the scVisualizer. The reading was performed by two neuroradiologists, to whom only the spectroscopic data and the reference image in the axial plane and T2-weighted were provided.

Results: From the first radiological reading without SC, 36.5% by single voxel and 36.5% by multivoxel corresponded to glial tumor patterns. By using SC, 76.2% per single voxel and 63.5% per multivoxel corresponded to glial tumor patterns. From the follow-up and post-radiological diagnosis, it was confirmed that of the 63 studies, 77.8% corresponded to recurrences. 100% and 87.2% of the lesions with glial tumor patterns corresponded to recurrences according to the post-radiological diagnosis for single-voxel and multi-voxel SC, respectively.

Conclusions: Through the implementation of the SC, additional information is obtained that allows to determine and confirm the presence of glial tumor patterns, which could contribute to better decision-making in the management of this type of patients.

Citation Format: Milena Acosta, Gina Catalina Malaver, Oscar Gamboa, Cesar Rodriguez, Julian Beltran, Pablo Moreno-Acosta. Spectral classifier as a complement to magnetic resonance spectroscopy in the management of patients with brain tumors of the central nervous system [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2821.