Is pre-radiotherapy metabolic heterogeneity of glioblastoma predictive of progression-free survival?

• Published in: Radiotherapy and oncology Vol. 183; p. 109665
• Main Authors: Tensaouti, Fatima, Desmoulin, Franck, Gilhodes, Julia, Roques, Margaux, Ken, Soleakhena, Lotterie, Jean-Albert, Noël, Georges, Truc, Gilles, Sunyach, Marie-Pierre, Charissoux, Marie, Magné, Nicolas, Lubrano, Vincent, Péran, Patrice, Cohen-Jonathan Moyal, Elizabeth, Laprie, Anne
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.06.2023; Elsevier
• Subjects: Glioblastoma; Hematology, Oncology, and Palliative Medicine; Life Sciences; MRI spectroscopy; Progression-free survival; MRI spectroscopy; Progression-free survival; Glioblastoma; progression-free survival
• ISSN: 0167-8140; 1879-0887; 1879-0887
• DOI: 10.1016/j.radonc.2023.109665

•Identification of glioblastoma components prior to radiotherapy is of great importance.•Clustering large MRSI data allows pathological tissue identification in this tumor.•Dominance of clusters with metabolic abnormalities in the tumor is predictive of poor PFS. All glioblastoma subtypes share the hallmark of aggressive invasion, meaning that it is crucial to identify their different components if we are to ensure effective treatment and improve survival. Proton MR spectroscopic imaging (MRSI) is a noninvasive technique that yields metabolic information and is able to identify pathological tissue with high accuracy. The aim of the present study was to identify clusters of metabolic heterogeneity, using a large MRSI dataset, and determine which of these clusters are predictive of progression-free survival (PFS). MRSI data of 180 patients acquired in a pre-radiotherapy examination were included in the prospective SPECTRO-GLIO trial. Eight features were extracted for each spectrum: Cho/NAA, NAA/Cr, Cho/Cr, Lac/NAA, and the ratio of each metabolite to the sum of all the metabolites. Clustering of data was performed using a mini-batch k-means algorithm. The Cox model and logrank test were used for PFS analysis. Five clusters were identified as sharing similar metabolic information and being predictive of PFS. Two clusters revealed metabolic abnormalities. PFS was lower when Cluster 2 was the dominant cluster in patients’ MRSI data. Among the metabolites, lactate (present in this cluster and in Cluster 5) was the most statistically significant predictor of poor outcome. Results showed that pre-radiotherapy MRSI can be used to reveal tumor heterogeneity. Groups of spectra, which have the same metabolic information, reflect the different tissue components representative of tumor burden proliferation and hypoxia. Clusters with metabolic abnormalities and high lactate are predictive of PFS.