On the relation between MR spectroscopy features and the distance to MRI‐visible solid tumor in GBM patients

Barros, Nuno Pedrosa de; Meier, Raphael; Pletscher, Martin; Stettler, Samuel; Knecht, Urspeter; Herrmann, Evelyn; Schucht, Philippe; Gralla, Jan; Wiest, Roland; Slotboom, Johannes

doi:10.1002/mrm.27359

Cited by 5 publications

(11 citation statements)

References 51 publications

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“…This comparison was made to evaluate the ability of the introduced HGG sources' maps to predict future contrast enhancement. Besides the sources' maps, we also include Cho/NAA and expected distance to tumor (EDT) maps for comparison. As in Reference , the Cho/NAA maps were divided by the value of Cho/NAA on the healthy contralateral side.…”

Section: Resultsmentioning

confidence: 99%

“…The EDT maps show the predicted distance to the border of the solid tumor volume (necrosis, enhancing and non‐enhancing tumor) based solely on the spectroscopic information of each voxel. As shown in the original paper where the method was presented, the method allows recognition of spectroscopic patterns that are normally seen in the peritumoral region of GBM, where infiltration is most likely.…”

Section: Resultsmentioning

confidence: 99%

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Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

et al. 2019

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Clinical use of MRSI is limited by the level of experience required to properly translate MRSI examinations into relevant clinical information. To solve this, several methods have been proposed to automatically recognize a predefined set of reference metabolic patterns. Given the variety of metabolic patterns seen in glioma patients, the decision on the optimal number of patterns that need to be used to describe the data is not trivial. In this paper, we propose a novel framework to (1) separate healthy from abnormal metabolic patterns and (2) retrieve an optimal number of reference patterns describing the most important types of abnormality. Using 41 MRSI examinations (1.5 T, PRESS, T E 135 ms) from 22 glioma patients, four different patterns describing different types of abnormality were detected: edema, healthy without Glx, active tumor and necrosis. The identified patterns were then evaluated on 17 MRSI examinations from nine different glioma patients. The results were compared against BraTumIA, an automatic segmentation method trained to identify different tumor compartments on structural MRI data. Finally, the ability to predict future contrast enhancement using the proposed approach was also evaluated. KEYWORDS applications, cancer, head and neck cancer methods and engineering, MRS and MRSI methods, post-acquisition processing, spectroscopic imaging, visualization methods and engineering 1 | INTRODUCTION MRS provides relevant metabolic information for the assessment of brain tumors, allowing us to distinguish different tumor types and grades, 1-3 distinguish radiation effects (pseudoprogression) from true progression 4,5 and identify regions with high tumor cellularity that are not visible in structural MRI. 6-8 Many publications 9 focus on the translation of one or two MRS features, such as choline (Cho)/NAA (N-acetyl aspartate) andCho/Cr (creatine), into clinically meaningful information for the tasks described above. Regardless of what can already be achieved with the analysis of individual metabolite ratios, the use of metabolic patterns for the identification of tissue types and diseases has the potential to allow more precise characterization of brain tumors.Several approaches 10-21 have been suggested for the analysis of metabolic patterns present in brain tumor MRS data. Before such methods can be used to interpret new data, a library of metabolic patterns has to be defined. The decision on the order of the model used to interpret .3 + Lip0.9; Lip0.9, 0.9 ppm signals originating from the -CH3 moiety of lipid molecules (triglycerides); Lip1.3, 1.3 ppm signals originating from the -CH2 moiety of lipid molecules (triglycerides); LOH, loss of heterozygosity; LOOCV, leave-one-out cross-validation; NAA, N-acetyl aspartate; NNLS, non-negative least squares

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

et al. 2019

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“…Other authors have used similar CNI‐like indexes to simply mean direct tCho‐to‐NAA area ratios (Figure ) . These ‘metabolic abnormality regions’ have been shown to extend beyond structural MRI‐defined abnormality boundaries . This is helping to better define resection margins, radiosurgery limits and malignant transformation in recurrent glioma, as well as to associate CNI metrics to survival after various therapeutic protocols, and to identify tumour regions at risk for progression …”

Section: Mrs(i)‐detectable Metabolic Hallmarks Of Cancer: Applicationmentioning

confidence: 99%

“…As can be noticed from previous sections, even today, pattern recognition is not much in use for the diagnostic or prognostic evaluation of cancer, with most studies focusing on single metabolites (Cho, 2HG) or particular metabolite ratios ((Cho + PA + Cr)/Cit, Glx/NAA,) or indices (CNI), the paradigm being 2HG, a single‐metabolite MRS‐visible biomarker that allows clinicians to perform patient stratification and follow‐up of response to therapy in carriers of the IDH mutation. However, as elaborated in Section .3, even in that situation other MRS‐visible changes have been observed, which taken individually are non‐significant.…”

Section: Mrs Cancer Patternmentioning

confidence: 99%

“…According to the molecular/cellular heterogeneity described for tumours in the previous section, the spectral pattern can also be heterogeneous through the tumour volume, reflecting differences in molecular signatures. Recent studies in human GB tend to focus on metabolite ratios in the peritumoral area . This is indeed true not only for a snapshot of a tumour at a given time point, but also informative in longitudinal studies, for example, to follow treatment response.…”

Section: Mrs Cancer Patternmentioning

confidence: 99%

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Cancer metabolism in a snapshot: MRS(I)

2019

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The contribution of MRS(I) to the in vivo evaluation of cancer-metabolism-derived metrics, mostly since 2016, is reviewed here. Increased carbon consumption by tumour cells, which are highly glycolytic, is now being sampled by 13 C magnetic resonance spectroscopic imaging (MRSI) following the injection of hyperpolarized [1-13 C] pyruvate (Pyr). Hot-spots of, mostly, increased lactate dehydrogenase activity or flow between Pyr and lactate (Lac) have been seen with cancer progression in prostate (preclinical and in humans), brain and pancreas (both preclinical) tumours. Therapy response is usually signalled by decreased Lac/Pyr 13 C-labelled ratio with respect to untreated or nonresponding tumour. For therapeutic agents inducing tumour hypoxia, the 13 C-labelled Lac/bicarbonate ratio may be a better metric than the Lac/Pyr ratio. 31 P MRSI may sample intracellular pH changes from brain tumours (acidification upon antiangiogenic treatment, basification at fast proliferation and relapse). The steady state tumour metabolome pattern is still in use for cancer evaluation. Metrics used for this range from quantification of single oncometabolites (such as 2-hydroxyglutarate in mutant IDH1 glial brain tumours) to selected metabolite ratios (such as total choline to N-acetylaspartate (plain ratio or CNI index)) or the whole 1 H MRSI(I) pattern through pattern recognition analysis. These approaches have been applied to address different questions such as tumour subtype definition, following/predicting the response to therapy or defining better resection or radiosurgery limits. KEYWORDSanimal model study, cancer, cellular and molecular cancer imaging, hyperpolarized C-13, MRS and MRSI methods, phosphorus MRS/MRSI | TEMPORAL AND THEMATIC BOUNDARIES OF THE TOPICS COVEREDThe authors of this review declare at the outset that there is no intention of being exhaustive or systematic. The temporal boundaries reviewed will be modest: only relevant literature since 2016, chosen according to our bias. Earlier work or reviews will only be referred to in order to put recent findings in due perspective. Authors hope that this summary will give readers a picture of how far cancer metabolism analysis with MRS(I) has advanced and, perhaps, why it is not advancing further.For quite a long time MRS, rather than MRI, has been the only approach able to evaluate cancer metabolism. Nevertheless, the MRI-derived information related to cancer metabolism should not be forgotten: consider for example the chemical exchange saturation transfer (CEST) effect caused by exchangeable protons in water-derived images of glucose uptake and metabolism in tumours. 1,2 However, apart from these CESTrelated studies, we should acknowledge that most information about cancer metabolism using MR still refers to MRS(I) acquisitions, which are the ones dealt with in our text.Supplementary Figure 1 shows a PUBMED search illustrating the different proportions of articles investigating several cancer types through MRS(I) approaches. The retrieved information can help unders...

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In vivo macromolecule signals in rat brain ¹H‐MR spectra at 9.4T: Parametrization, spline baseline estimation, and T₂ relaxation times

Simicic

Račkayová

Xin

et al. 2021

Magnetic Resonance in Med

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Purpose Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short‐echo time (TE) 1H‐MR spectra. An experimentally acquired single MM spectrum is commonly used. Higher spectral resolution at ultra‐high field (UHF) led to increased interest in using a parametrized MM spectrum together with flexible spline baselines to address unpredicted spectroscopic components. Herein, we aimed to: (1) implement an advanced methodological approach for post‐processing, fitting, and parametrization of 9.4T rat brain MM spectra; (2) assess the concomitant impact of the LCModel baseline and MM model (ie, single vs parametrized); and (3) estimate the apparent T2 relaxation times for seven MM components. Methods A single inversion recovery sequence combined with advanced AMARES prior knowledge was used to eliminate the metabolite residuals, fit, and parametrize 10 MM components directly from 9.4T rat brain in vivo 1H‐MR spectra at different TEs. Monte Carlo simulations were also used to assess the concomitant influence of parametrized MM and DKNTMN parameter in LCModel. Results A very stiff baseline (DKNTMN ≥ 1 ppm) in combination with a single MM spectrum led to deviations in metabolite concentrations. For some metabolites the parametrized MM showed deviations from the ground truth for all DKNTMN values. Adding prior knowledge on parametrized MM improved MM and metabolite quantification. The apparent T2 ranged between 12 and 24 ms for seven MM peaks. Conclusion Moderate flexibility in the spline baseline was required for reliable quantification of real/experimental spectra based on in vivo and Monte Carlo data. Prior knowledge on parametrized MM improved MM and metabolite quantification.

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On the relation between MR spectroscopy features and the distance to MRI‐visible solid tumor in GBM patients

Abstract: The proposed method enables the automatic detection of metabolic patterns associated with different distances to the STV border and may assist tumor delineation of infiltrative brain tumors such as GBM.

Cited by 5 publications

References 51 publications

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

Cancer metabolism in a snapshot: MRS(I)

In vivo macromolecule signals in rat brain ¹H‐MR spectra at 9.4T: Parametrization, spline baseline estimation, and T₂ relaxation times

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On the relation between MR spectroscopy features and the distance to MRI‐visible solid tumor in GBM patients

Abstract: The proposed method enables the automatic detection of metabolic patterns associated with different distances to the STV border and may assist tumor delineation of infiltrative brain tumors such as GBM.

Cited by 5 publications

References 51 publications

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

Analysis of metabolic abnormalities in high‐grade glioma using MRSI and convex NMF

Cancer metabolism in a snapshot: MRS(I)

In vivo macromolecule signals in rat brain 1H‐MR spectra at 9.4T: Parametrization, spline baseline estimation, and T2 relaxation times

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In vivo macromolecule signals in rat brain ¹H‐MR spectra at 9.4T: Parametrization, spline baseline estimation, and T₂ relaxation times