Novel application of chemical shift gradient echo in- and opposed-phase sequences in 3 T MRI for the detection of H-MRS visible lipids and grading of glioma

Predicting cancer malignancy and proliferation in glioma patients: intra-subject inter-metabolite correlation analyses using MRI and MRSI contrast scans

Background

The non-invasive characterization of glioma metabolites would greatly assist the management of glioma patients in the clinical setting. This study investigated the applicability of intra-subject inter-metabolite correlation analyses for differentiating glioma malignancy and proliferation.

Methods

A total of 17 negative controls (NCs), 39 low-grade gliomas (LGGs) patients, and 25 high-grade gliomas (HGGs) subjects were included in this retrospective study. Amide proton transfer (APT) and magnetization transfer contrast (MTC) imaging contrasts, as well as total choline/total creatine (tCho/tCr) and total N-acetylaspartate/total creatine (tNAA/tCr) ratios quantified from magnetic resonance spectroscopic imaging (MRSI) were co-registered voxel-wise and used to produce three intra-subject inter-metabolite correlation coefficients (IMCCs), namely, R_{APT vs . MTC}, R_{APT vs . tCho/tCr}, and R_{MTC vs . tNAA/tCr}. The correlation between the IMCCs and tumor grade and Ki-67 labeling index (LI) for tumor proliferation were explored. The differences in the IMCCs between the three groups were compared with one-way analysis of variance (ANOVA). Finally, regression analysis was used to build a combined model with multiple IMCCs to improve the diagnostic performance for tumor grades based on receiver operator characteristic curves.

Results

Compared with the NCs, gliomas showed stronger inter-metabolic correlations. R_{APT vs . MTC} was significantly different among the three groups (NC vs. LGGs vs. HGGs: -0.18±0.38 vs. -0.40±0.34 vs. -0.70±0.29, P<0.0001). No significant differences were detected in R_{MTC vs . tNAA/tCr} among the three groups. R_{APT vs . MTC} and R_{APT vs . tCho/tCr} correlated significantly with tumor grade (R=-0.41, P=0.001 and R=0.448, P<0.001, respectively). However, only R_{APT vs . MTC} was mildly correlated with Ki-67 (R=-0.33, P=0.02). R_{APT vs . MTC} and R_{APT vs . tCho/tCr} achieved areas under the curve (AUCs) of 0.754 and 0.71, respectively, for differentiating NCs from gliomas; and 0.77 and 0.78, respectively, for differentiating LGGs from HGGs. The combined multi-IMCCs model improved the correlation with the Ki-67 LI (R=0.46, P=0.0008) and the tumor-grade stratification with AUC increased to 0.85 (sensitivity: 80.0%, specificity: 79.5%).

Conclusions

This study demonstrated that glioma patients showed stronger inter-metabolite correlations than control subjects, and the IMCCs were significantly correlated with glioma grade and proliferation. The multi-IMCCs combined model further improved the performance of clinical diagnosis.

Quantitative magnetic resonance imaging and radiogenomic biomarkers for glioma characterisation: a systematic review

OBJECTIVE:

The diversity of tumour characteristics among glioma patients, even within same tumour grade, is a big challenge for disease outcome prediction. A possible approach for improved radiological imaging could come from combining information obtained at the molecular level. This review assembles recent evidence highlighting the value of using radiogenomic biomarkers to infer the underlying biology of gliomas and its correlation with imaging features.

METHODS:

A literature search was done for articles published between 2002 and 2017 on Medline electronic databases. Of 249 titles identified, 38 fulfilled the inclusion criteria, with 14 articles related to quantifiable imaging parameters (heterogeneity, vascularity, diffusion, cell density, infiltrations, perfusion, and metabolite changes) and 24 articles relevant to molecular biomarkers linked to imaging.

RESULTS:

Genes found to correlate with various imaging phenotypes were EGFR, MGMT, IDH1, VEGF, PDGF, TP53, and Ki-67. EGFR is the most studied gene related to imaging characteristics in the studies reviewed (41.7%), followed by MGMT (20.8%) and IDH1 (16.7%). A summary of the relationship amongst glioma morphology, gene expressions, imaging characteristics, prognosis and therapeutic response are presented.

CONCLUSION:

The use of radiogenomics can provide insights to understanding tumour biology and the underlying molecular pathways. Certain MRI characteristics that show strong correlations with EGFR, MGMT and IDH1 could be used as imaging biomarkers. Knowing the pathways involved in tumour progression and their associated imaging patterns may assist in diagnosis, prognosis and treatment management, while facilitating personalised medicine.

ADVANCES IN KNOWLEDGE:

Radiogenomics can offer clinicians better insight into diagnosis, prognosis, and prediction of therapeutic responses of glioma.

Quantification and visualization of lipid landscape in glioma using in -and opposed-phase imaging

Objectives

This study maps the lipid distributions based on magnetic resonance imaging (MRI) in-and opposed-phase (IOP) sequence and correlates the findings generated from lipid map to histological grading of glioma.

Methods

Forty histologically proven glioma patients underwent a standard MRI tumour protocol with the addition of IOP sequence. The regions of tumour (solid enhancing, solid non-enhancing, and cystic regions) were delineated using snake model (ITK-SNAP) with reference to structural and diffusion MRI images. The lipid distribution map was constructed based on signal loss ratio (SLR) obtained from the IOP imaging. The mean SLR values of the regions were computed and compared across the different glioma grades.

Results

The solid enhancing region of glioma had the highest SLR for both Grade II and III. The mean SLR of solid non-enhancing region of tumour demonstrated statistically significant difference between the WHO grades (grades II, III & IV) (mean SLR_II = 0.04, mean SLR_III = 0.06, mean SLR_IV = 0.08, & p < .01). A strong positive correlation was seen between WHO grades with mean SLR on lipid map of solid non-enhancing (ρ=0.68, p < .01).

Conclusion

Lipid quantification via lipid map provides useful information on lipid landscape in tumour heterogeneity characterisation of glioma. This technique adds to the surgical diagnostic yield by identifying biopsy targets. It can also be used as an adjunct grading tool for glioma as well as to provide information about lipidomics landscape in glioma development.

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In- and opposed-phase imaging is useful in gliomas characterisation and grading.

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Signal loss ratio in the solid non-enhancing region is a potential imaging marker for discriminating between the WHO grades.

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Lipid quantification via lipid distribution mapping provides useful information on lipid landscape in tumour heterogeneity.