Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

Dou, Wei; Zhang, Mingyu; Zhang, Xiaojie; Li, Yuan; Chen, Hongyan; Li, Shaowu; Lü, Min; Dai, Jianping; Constans, Jean-Marc

doi:10.1371/journal.pone.0137850

Cited by 11 publications

(4 citation statements)

References 37 publications

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“…The Wartburg effect shows that cancer is a metabolic disease and that many tumor cells produce energy via glycolysis during cell proliferation [ 25 – 27 ]. Many researchers have attempted to use metabolites to aid in the monitoring of tumor cell treatment in the central nervous system with some success [ 15 , 16 ]. Glycolysis provides energy for tumor cells and produces lactate [ 17 ].…”

Section: Discussionmentioning

confidence: 99%

“…Glycolysis provides energy for tumor cells and produces lactate [ 17 ]. Changes in lactate levels are closely connected with the proliferation and apoptosis rates of glioma cell lines [ 9 , 15 ]. Creatineis a stable molecule in nerve cells and nervous system tumors, and is employed here as an internal reference.…”

Section: Discussionmentioning

confidence: 99%

“…The lactate-to-creatine (Lac/Cr) ratio can be used as a biomarker of various cancer cell processes, because the lactate level rises with the proliferation of tumor cells [ 15 – 17 ]. In this study, to establish whether the Lac/Cr ratio can be used as a noninvasive monitor for radiotherapy, we evaluated the correlation between this ratio, the PKC-ι expression level and apoptosis in glioma cells.…”

Section: Introductionmentioning

confidence: 99%

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Measuring the lactate-to-creatine ratio via 1H NMR spectroscopy can be used to noninvasively evaluate apoptosis in glioma cells after X-ray irradiation

Cui

Li³

et al. 2018

Cell Mol Biol Lett

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BackgroundRadiotherapy is among the commonly applied treatment options for glioma, which is one of the most common types of primary brain tumor. To evaluate the effect of radiotherapy noninvasively, it is vital for oncologists to monitor the effects of X-ray irradiation on glioma cells. Preliminary research had showed that PKC-ι expression correlates with tumor cell apoptosis induced by X-ray irradiation. It is also believed that the lactate-to-creatine (Lac/Cr) ratio can be used as a biomarker to evaluate apoptosis in glioma cells after X-ray irradiation. In this study, we evaluated the relationships between the Lac/Cr ratio, apoptotic rate, and protein kinase C iota (PKC-ι) expression in glioma cells.MethodsCells of the glioma cell lines C6 and U251 were randomly divided into 4 groups, with every group exposed to X-ray irradiation at 0, 1, 5, 10 and 15 Gy. Single cell gel electrophoresis (SCGE) was conducted to evaluate the DNA damage. Flow cytometry was performed to measure the cell cycle blockage and apoptotic rates. Western blot analysis was used to detect the phosphorylated PKC-ι (p-PKC-ι) level. 1H NMR spectroscopy was employed to determine the Lac/Cr ratio.ResultsThe DNA damage increased in a radiation dose-dependent manner (p < 0.05). With the increase in X-ray irradiation, the apoptotic rate also increased (C6, p < 0.01; U251, p < 0.05), and the p-PKC-ι level decreased (C6, p < 0.01; U251, p < 0.05). The p-PKC-ι level negatively correlated with apoptosis, whereas the Lac/Cr ratio positively correlated with the p-PKC-ι level.ConclusionThe Lac/Cr ratio decreases with an increase in X-ray irradiation and thus can be used as a biomarker to reflect the effects of X-ray irradiation in glioma cells.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Measuring the lactate-to-creatine ratio via 1H NMR spectroscopy can be used to noninvasively evaluate apoptosis in glioma cells after X-ray irradiation

Cui

Li³

et al. 2018

Cell Mol Biol Lett

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“…It can provide information on the neuronal integrity and on the levels of neurotransmitters including N -acetylaspartate (NAA), a neuronal marker, choline (Cho), which is involved in the synthesis and degradation of cell membranes, creatine (Cr), which reflects the energetic activity in the cells, lipid (Lip), a product of membrane phospholipids and necrotic debris, and lactate (Lac), which correlates with anaerobic glycolysis. In many studies, 1 H-MRS has been shown to determine the tumor type and grade (Dou et al, 2015). 1 H-MRS adds relevant diagnostic value exceeding that obtained from structural MR imaging alone.…”

Section: Introductionmentioning

confidence: 99%

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas

Fan

et al. 2019

NeuroImage: Clinical

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Abstrct Objectives To investigate the association between proton magnetic resonance spectroscopy ( 1 H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade. Methods This study included 112 glioma patients who were divided into the training ( n = 74) and validation ( n = 38) sets based on the time of hospitalization. Twenty-six metabolic features were extracted from the preoperative 1 H-MRS image. The Student's t -test was conducted to screen for differentially expressed features between low- and high-grade gliomas (WHO grades II and III/IV, respectively). Next, the minimum Redundancy Maximum Relevance (mRMR) algorithm was performed to further select features for a support vector machine (SVM) classifier building. Performance of the predictive model was evaluated both in the training and validation sets using ROC curve analysis. Results Among the extracted 1 H-MRS metabolic features, thirteen features were differentially expressed. Four features were further selected as grade-predictive imaging signatures using the mRMR algorithm. The predictive performance of the machine-learning model measured by the AUC was 0.825 and 0.820 in the training and validation sets, respectively. This was better than the predictive performances of individual metabolic features, the best of which was 0.812. Conclusions 1 H-MRS metabolic features could help in predicting the grade of gliomas. The machine-learning model achieved a better prediction performance in grading gliomas than individual features, indicating that it could complement the traditionally used metabolic features.

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Comparison of baseline correction algorithms for in vivo ¹H‐MRS

Pasmiño,

Slotboom,

Schweisthal

et al. 2024

NMR in Biomedicine

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Proton MRS is used clinically to collect localized, quantitative metabolic data from living tissues. However, the presence of baselines in the spectra complicates accurate MRS data quantification. The occurrence of baselines is not specific to short‐echo‐time MRS data. In short‐echo‐time MRS, the baseline consists typically of a dominating macromolecular (MM) part, and can, depending on B0 shimming, poor voxel placement, and/or localization sequences, also contain broad water and lipid resonance components, indicated by broad components (BCs). In long‐echo‐time MRS, the MM part is usually much smaller, but BCs may still be present. The sum of MM and BCs is denoted by the baseline. Many algorithms have been proposed over the years to tackle these artefacts. A first approach is to identify the baseline itself in a preprocessing step, and a second approach is to model the baseline in the quantification of the MRS data themselves. This paper gives an overview of baseline handling algorithms and also proposes a new algorithm for baseline correction. A subset of suitable baseline removal algorithms were tested on in vivo MRSI data (semi‐LASER at TE = 40 ms) and compared with the new algorithm. The baselines in all datasets were removed using the different methods and subsequently fitted using spectrIm‐QMRS with a TDFDFit fitting model that contained only a metabolite basis set and lacked a baseline model. The same spectra were also fitted using a spectrIm‐QMRS model that explicitly models the metabolites and the baseline of the spectrum. The quantification results of the latter quantification were regarded as ground truth. The fit quality number (FQN) was used to assess baseline removal effectiveness, and correlations between metabolite peak areas and ground truth models were also examined. The results show a competitive performance of our new proposed algorithm, underscoring its automatic approach and efficiency. Nevertheless, none of the tested baseline correction methods achieved FQNs as good as the ground truth model. All separately applied baseline correction methods introduce a bias in the observed metabolite peak areas. We conclude that all baseline correction methods tested, when applied as a separate preprocessing step, yield poorer FQNs and biased quantification results. While they may enhance visual display, they are not advisable for use before spectral fitting.

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Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

Cited by 11 publications

References 37 publications

Measuring the lactate-to-creatine ratio via 1H NMR spectroscopy can be used to noninvasively evaluate apoptosis in glioma cells after X-ray irradiation

Measuring the lactate-to-creatine ratio via 1H NMR spectroscopy can be used to noninvasively evaluate apoptosis in glioma cells after X-ray irradiation

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas

Comparison of baseline correction algorithms for in vivo ¹H‐MRS

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Convex-Envelope Based Automated Quantitative Approach to Multi-Voxel 1H-MRS Applied to Brain Tumor Analysis

Cited by 11 publications

References 37 publications

Measuring the lactate-to-creatine ratio via 1H NMR spectroscopy can be used to noninvasively evaluate apoptosis in glioma cells after X-ray irradiation

Measuring the lactate-to-creatine ratio via 1H NMR spectroscopy can be used to noninvasively evaluate apoptosis in glioma cells after X-ray irradiation

A quantitative SVM approach potentially improves the accuracy of magnetic resonance spectroscopy in the preoperative evaluation of the grades of diffuse gliomas

Comparison of baseline correction algorithms for in vivo 1H‐MRS

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Comparison of baseline correction algorithms for in vivo ¹H‐MRS