Multivoxel 1H-MR Spectroscopy Biometrics for Preoprerative Differentiation between Brain Tumors

Abstract

We investigated multivoxel proton magnetic resonance spectroscopy (1H-MRS) biometrics for preoperative differentiation and prognosis of patients with brain metastases (MET), low-grade glioma (LGG) and high-grade glioma (HGG). In total, 33 patients (HGG, 14; LGG, 9; and 10 MET) were included. 1H-MRS imaging (MRSI) data were assessed and neurochemical profiles for metabolites N-acetyl aspartate (NAA) + NAAG(NAA), Cr + PCr(total creatine, tCr), Glu + Gln(Glx), lactate (Lac), myo-inositol(Ins), GPC + PCho(total choline, tCho), and total lipids, and macromolecule (tMM) signals were estimated. Metabolites were reported as absolute concentrations or ratios to tCho or tCr levels. Voxels of interest in an MRSI matrix were labeled according to tissue. Logistic regression, receiver operating characteristic, and Kaplan–Meier survival analysis was performed. Across HGG, LGG, and MET, average Ins/tCho was shown to be prognostic for overall survival (OS): low values (≤1.29) in affected hemisphere predicting worse OS than high values (>1.29), (log rank < 0.007). Lip/tCho and Ins/tCho combined showed 100% sensitivity and specificity for both HGG/LGG (P < .001) and LGG/MET (P < .001) measured in nonenhancing/contrast-enhancing lesional tissue. Combining tCr/tCho in perilesional edema with tCho/tCr and NAA/tCho from ipsilateral normal- appearing tissue yielded 100% sensitivity and 81.8% specificity (P < .002) for HGG/MET. Best single biomarker: Ins/tCho for HGG/LGG and total lipid/tCho for LGG/MET showed 100% sensitivity and 75% and 100% specificity, respectively. HGG/MET; NAA/tCho showed 75% sensitivity and 84.6% specificity. Multivoxel 1H-MRSI provides prognostic information for OS for HGG/LGG/MET and a multibiometric approach for differentiation may equal or outperform single biometrics.

Mapping of Metabolic Heterogeneity of Glioma Using MR-Spectroscopy

“…We found that voxels, that were segmented as FLAIR-intensity, could be differentiated from vasogenic edema and tumor-infiltrating regions according to their metabolic profiles, with the first showing increased intensities of myo-inositol, which is a metabolite known as an “osmolyte” [ 30 ] and the last showing increased intensities of malignancy-related metabolites, such as macromolecules. These results are comparable to other studies conducted for preoperatively classification of brain tumors, in pediatric and adult population [ 6 , 7 , 15 , 16 , 28 , 31 ]. In our study, we analyzed and proved the accuracy with which MRS can discern between a brain lesion and normal brain tissue, which is in line with previous reports in the literature [ 3 , 7 , 24 , 28 , 29 , 31 , 32 ].…”

“…These results are comparable to other studies conducted for preoperatively classification of brain tumors, in pediatric and adult population [ 6 , 7 , 15 , 16 , 28 , 31 ]. In our study, we analyzed and proved the accuracy with which MRS can discern between a brain lesion and normal brain tissue, which is in line with previous reports in the literature [ 3 , 7 , 24 , 28 , 29 , 31 , 32 ]. Moreover, our data supports that MRS is a valid tool that can separate between edema regions with a suppressed intensity (ventricle-like signature) or tumor-infiltrative regions.…”

“…Recently, we investigated that creatine directly mediates resistance against hypoxic-stress in glioblastoma which causes a transcriptional subtype switch towards proneural gene expression [ 26 ]. The clusters with the highest levels of choline, lactate and macromolecules, characteristically present in cerebral hypoxia, inflammation, cellular membrane breakdown and necrosis [ 16 , 18 , 27 , 28 , 29 ], were the ones containing the voxels segmented to contrast-enhancement and necrotic tumor regions. We found that voxels, that were segmented as FLAIR-intensity, could be differentiated from vasogenic edema and tumor-infiltrating regions according to their metabolic profiles, with the first showing increased intensities of myo-inositol, which is a metabolite known as an “osmolyte” [ 30 ] and the last showing increased intensities of malignancy-related metabolites, such as macromolecules.…”

Contribution of macromolecules to brain 1H MR spectra: Experts' consensus recommendations

“…In low grade glioma, ML is low, whereas in high grade gliomas ML can dominate the spectrum, depending on the level of necrosis. Different types of brain tumors have been discriminated based on the spectrum profile, including MM + ML, and classifiers built for computer‐aided diagnostics 128,129 allowing the prediction of the tumor type and grade. In a study on the relationship between distance to the malignant glioma core and spectral pattern, ML + MM were an important factor for demarcation of the solid brain tumor 130 .…”

Differentiation Between Glioblastoma Multiforme and Metastasis From the Lungs and Other Sites Using Combined Clinical/Routine MRI Radiomics

“…The first tendency is to improve the performance of imaging modalities, and the second is to explore the differences among METs from different primary sites. For the first tendency, many imaging modalities have been proposed, including routine MRI and various advanced MRI modalities, such as magnetic resonance spectroscopy, diffusionweighted imaging (DWI), diffusion tensor imaging, diffusion kurtosis imaging, perfusion-weighted imaging (PWI), arterial spin labeling, and amide proton transfer-weighted imaging (Chen et al, 2012;Tan et al, 2015;Salice et al, 2016;Durmo et al, 2018;Holly et al, 2018;Kamimura et al, 2019;Xi et al, 2019). For the second tendency, the relative cerebral blood volume showed no difference among METs from the lungs (MET-lung), breasts, gastrointestinal tract, and skin (Askaner et al, 2019).…”