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

Metabolic Profile of Cerebellum in Posterior Fossa Tumor Survivors: Correlation With Memory Impairment

AIMS

The cerebellum is a key structure in working and procedural memory. The aim of the present prospective exploratory study was to investigate, the metabolic characteristics of the cerebellum in posterior fossa tumor (PFT) survivors using 3D proton magnetic resonance spectroscopy imaging (3D MRSI), to determine whether metabolites could be useful biomarkers of memory impairment.

MATERIALS AND METHODS

Sixty participants were included in the IMPALA study, divided into three groups: 22 irradiated PFT, 17 nonirradiated PFT, and 21 healthy controls matched with irradiated PFT for age, sex, and handedness. PFT survivors were treated at least 5 years ago, either by surgery or a combination of surgery, chemotherapy, and radiotherapy. All participants underwent working and procedural memory tests and multimodal MRI including a 3D MRSI sequence. N-acetylaspartate (NAA), choline (Cho), creatine (Cr), and lactate (Lac) metabolite values were extracted from the cerebellum for comparisons between groups, correlations with neurocognitive test scores, and radiotherapy doses.

RESULTS

Median (range) age at neurocognitive tests was 18 (7-26) years. Median Cho, Cr, NAA, and Lac values, and the ratio of NAA to the sum of metabolites were significantly lower for PFT survivors than for healthy controls (p < 0.05). Scores on working and procedural memory tests were significantly lower for PFT survivors (p < 0.004) and correlated with median and maximum Cho and NAA values (0.28

CONCLUSION

Results revealed changes in cerebellar metabolic values in PFT survivors that were closely correlated with memory deficits, suggesting that some metabolites could be used as markers of cognitive decline, but this will require validation on a larger sample size.

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Key Words

• Cerebellum
• Memory
• Metabolic
• Paediatric posterior fossa tumor
• Radiotherapy
• Spectroscopy imaging

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MESH Headings

• Humans
• Female
• Male
• Cerebellum/metabolism
• Cerebellum/radiation effects
• Cerebellum/diagnostic imaging
• Adult
• Infratentorial Neoplasms/metabolism
• Prospective Studies
• Memory Disorders/etiology
• Memory Disorders/metabolism
• Cancer Survivors
• Young Adult
• Adolescent
• Case-Control Studies
• Metabolome
• Choline/metabolism

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Affiliation(s)

F Tensaouti Radiation Oncology Department, Oncopole Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France.
N Courbière ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
B Cabarrou Biostatistics & Health Data Science Unit, Oncopole Claudius Regaud/Institut Universitaire du Cancer de Toulouse - Oncopôle, Toulouse, France
L Pollidoro ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
M Roques Radiology Department, Toulouse University Hospital, Toulouse, France
A Sévely Radiology Department, Toulouse University Hospital, Toulouse, France
P Péran ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France
E Baudou ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France; Pediatric Neurology Department, Children's Hospital, Toulouse University Hospital, Toulouse, France
A Laprie Radiation Oncology Department, Oncopole Claudius Regaud- Institut Universitaire du Cancer de Toulouse-Oncopole, Toulouse, France; ToNIC, Toulouse NeuroImaging Center, Université de Toulouse, Inserm, UPS, France

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

PURPOSE

Reliable detection and fitting of macromolecules (MM) are crucial for accurate quantification of brain short-echo time (TE) ¹ H-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 ¹ H-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.

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

Proton MR spectra of the brain, especially those measured at short and intermediate echo times, contain signals from mobile macromolecules (MM). A description of the main MM is provided in this consensus paper. These broad peaks of MM underlie the narrower peaks of metabolites and often complicate their quantification but they also may have potential importance as biomarkers in specific diseases. Thus, separation of broad MM signals from low molecular weight metabolites enables accurate determination of metabolite concentrations and is of primary interest in many studies. Other studies attempt to understand the origin of the MM spectrum, to decompose it into individual spectral regions or peaks and to use the components of the MM spectrum as markers of various physiological or pathological conditions in biomedical research or clinical practice. The aim of this consensus paper is to provide an overview and some recommendations on how to handle the MM signals in different types of studies together with a list of open issues in the field, which are all summarized at the end of the paper.

Cancer metabolism in a snapshot: MRS(I)

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 non-responding 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.

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

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.