Beyond mean value analysis - a voxel-based analysis of the quantitative MR biomarker water T2 in the presence of fatty infiltration in skeletal muscle tissue of patients with neuromuscular diseases

The main pathologies in the muscles of patients with neuromuscular diseases (NMD) are fatty infiltration and edema. Recently, quantitative magnetic resonance (MR) imaging for determination of the MR biomarkers proton density fat fraction (PDFF) and water T₂ (T_2w ) has been advanced. Biophysical effects or pathology can have different effects on MR biomarkers. Thus, for heterogeneously affected muscles, the routinely performed mean or median value analyses of MR biomarkers are questionable. Our work presents a voxel-based histogram analysis of PDFF and T_2w images to point out potential quantification errors. In 12 patients with NMD, chemical-shift encoding-based water-fat imaging for PDFF and T₂ mapping with spectral adiabatic inversion recovery (SPAIR) for T_2w determination was performed. Segmentation of nine thigh muscles was performed bilaterally (n = 216). PDFF and T₂ maps were coregistered. A voxel-based comparison of PDFF and T_2w showed a decreased T_2w with increasing PDFF. Mean T_2w and mean T_2w ^{without fatty voxels} (PDFF < 10%) show good agreement, whereas standard deviation (σ) T_2w and σ T_2w ^{without fatty voxels} show increasing difference with increasing values of σ. Thereby two subgroups can be observed, referring to muscles in which the exclusion of fatty voxels has a negligible influence versus muscles in which a strong dependency of the T_2w value distribution on the exclusion of fatty voxels is present. Because of the two opposite effects that influence T_2w in a voxel, namely, (i) a pathophysiologically increased water mobility leading to T_2w elevation, and (ii) a dependency of T_2w on the PDFF leading to decreased T_2w , the T_2w distribution within a muscle might be heterogenous and the routine mean or median analysis can lead to a misinterpretation of the muscle health. It was concluded that muscle T_2w mean values can wrongly suggest healthy muscle tissue. A deeper analysis of the underlying value distribution is necessary. Therefore, a quantitative analysis of T_2w histograms is a potential alternative.

Radiogenomics correlation between MR imaging features and mRNA-based subtypes in lower-grade glioma

Background

To investigate associations between lower-grade glioma (LGG) mRNA-based subtypes (R1-R4) and MR features.

Methods

mRNA-based subtyping was obtained from the LGG dataset in The Cancer Genome Atlas (TCGA). We identified matching patients (n = 145) in The Cancer Imaging Archive (TCIA) who underwent MR imaging. The associations between mRNA-based subtypes and MR features were assessed.

Results

In the TCGA-LGG dataset, patients with the R2 subtype had the shortest median OS months (P < 0.05). The time-dependent ROC for the R2 subtype was 0.78 for survival at 12 months, 0.76 for survival at 24 months, and 0.76 for survival at 36 months. In the TCIA-LGG dataset, 41 (23.7%) R1 subtype, 40 (23.1%) R2 subtype, 19 (11.0%) R3 subtype and 45 (26.0%) R4 subtype cases were identified. Multivariate analysis revealed that enhancing margin (ill-defined, OR: 9.985; P = 0.003) and T1 + C/T2 mismatch (yes, OR: 0.091; P = 0.023) were associated with the R1 subtype (AUC: 0.708). The average accuracy of the ten-fold cross validation was 71%. Proportion of contrast-enhanced (CE) tumour (> 5%, OR: 14.733; P < 0.001) and necrosis/cystic changes (yes, OR: 0.252; P = 0.009) were associated with the R2 subtype (AUC: 0.832). The average accuracy of the ten-fold cross validation was 82%. Haemorrhage (yes, OR: 8.55; P < 0.001) was positively associated with the R3 subtype (AUC: 0.689). The average accuracy of the ten-fold cross validation was 87%. Proportion of CE tumour (> 5%, OR: 0.14; P < 0.001) was negatively associated with the R4 subtype (AUC: 0.672). The average accuracy of the ten-fold cross validation was 71%. For the prediction of the R2 subtype, the nomogram showed good discrimination and calibration. Decision curve analysis demonstrated that prediction with the R2 model was clinically useful.

Conclusions

Patients with the R2 subtype had the worst prognosis. We demonstrated that MRI features can identify distinct LGG mRNA-based molecular subtypes.