Prediction and Visualization of Non-Enhancing Tumor in Glioblastoma via T1w/T2w-Ratio Map

Thalamic Microstructural Alterations as Revealed by the T1/T2 Ratio in Chronic Pain Patients

Background/Objectives: Neuroimaging biomarkers could offer more objective measures of the pain experience. This study investigated rT1/T2 maps of the brain as a novel biomarker for chronic pain in patients with central post-stroke pain (PSP) and persistent spinal pain syndrome type 2 (PSPS-II). Methods: Patients with PSP and PSPS-II were retrospectively included alongside healthy controls. Bias correction and intensity normalization were applied to the T1-weighted and T2-weighted images to generate the rT1/T2 maps of the brain. Subsequently, rT1/T2 maps were spatially correlated with neurotransmitter atlases derived from molecular imaging. Results: In total, 15 PSPS-II patients, 11 PSP patients, and 18 healthy controls were included. No significant differences between patient and control demographics were found. Significant decreases in rT1/T2 signal intensity (p < 0.001) were observed in the dorsal and medial part of the thalamus, left caudate nucleus, cuneus, superior frontal gyrus, and dorsal cervicomedullary junction in PSP patients. No significant changes were found in rT1/T2 signal intensity in PSPS-II patients. Significant correlations were found with CB1-, 5HT2a-, and mGluR5-receptor maps (pFDR = 0.003, 0.030, and 0.030, respectively) for the PSP patients and with CB1-, 5HT1a-, 5HT2a-, KappaOp-, and mGluR5-receptor maps (pFDR = 0.003, 0.002, 0.002, 0.003, and 0.002, respectively) in PSPS-II patients. Conclusions: These findings suggest that microstructural alterations occur in the thalamus, cuneus, and dorsal cervicomedullary junction in patients with PSP. The lack of significant findings in rT1/T2 in PSPS-II patients combined with the significant correlations with multiple neurotransmitter maps suggests varying degrees of microstructural deterioration in both chronic pain syndromes, although further research is warranted.

Intensity scaling of conventional brain magnetic resonance images avoiding cerebral reference regions: A systematic review

BACKGROUND

Conventional brain magnetic resonance imaging (MRI) produces image intensities that have an arbitrary scale, hampering quantification. Intensity scaling aims to overcome this shortfall. As neurodegenerative and inflammatory disorders may affect all brain compartments, reference regions within the brain may be misleading. Here we summarize approaches for intensity scaling of conventional T1-weighted (w) and T2w brain MRI avoiding reference regions within the brain.

METHODS

Literature was searched in the databases of Scopus, PubMed, and Web of Science. We included only studies that avoided reference regions within the brain for intensity scaling and provided validating evidence, which we divided into four categories: 1) comparative variance reduction, 2) comparative correlation with clinical parameters, 3) relation to quantitative imaging, or 4) relation to histology.

RESULTS

Of the 3825 studies screened, 24 fulfilled the inclusion criteria. Three studies used scaled T1w images, 2 scaled T2w images, and 21 T1w/T2w-ratio calculation (with double counts). A robust reduction in variance was reported. Twenty studies investigated the relation of scaled intensities to different types of quantitative imaging. Statistically significant correlations with clinical or demographic data were reported in 8 studies. Four studies reporting the relation to histology gave no clear picture of the main signal driver of conventional T1w and T2w MRI sequences.

CONCLUSIONS

T1w/T2w-ratio calculation was applied most often. Variance reduction and correlations with other measures suggest a biologically meaningful signal harmonization. However, there are open methodological questions and uncertainty on its biological underpinning. Validation evidence on other scaling methods is even sparser.

Distinguishing Tumor Cell Infiltration and Vasogenic Edema in the Peritumoral Region of Glioblastoma at the Voxel Level via Conventional MRI Sequences

RATIONALE AND OBJECTIVES

The peritumoral region of glioblastoma (GBM) is composed of infiltrating tumor cells and vasogenic edema, which are difficult to distinguish manually on MRI. To distinguish tumor cell infiltration and vasogenic edema in GBM peritumoral regions, it is crucial to develop a method that is precise, effective, and widely applicable.

MATERIALS AND METHODS

We retrieved the image characteristics of 379,730 voxels (marker of tumor infiltration) from 28 non-enhanced gliomas and 365,262 voxels (marker of edema) from the peritumoral edema region of 14 meningiomas on conventional MRI sequences (T1-weighted image, the contrast-enhancing T1-weighted image, the T2-weighted image, the T2-fluid attenuated inversion recovery image, and the apparent diffusion coefficient map). Using the SVM classifier, a model for predicting tumor cell infiltration and vasogenic edema at the voxel level was developed. The accuracy of the model's predictions was then evaluated using 15 GBM patients who underwent stereotactic biopsies.

RESULTS

The area under the curve (AUC), accuracy, sensitivity, and specificity of the prediction model were 0.93, 0.84, 0.83, and 0.85 in the training set, and 0.90, 0.82, 0.83, and 0.83 in the test set (704,992 voxels), respectively. The pathology verification of 28 biopsy points with an accuracy of 0.79.

CONCLUSION

At the voxel level, it seems possible to forecast tumor cell infiltration and vasogenic edema in the peritumoral region of GBM based on conventional MRI sequences.

Qualitative MR features to identify non-enhancing tumors within glioblastoma's T2-FLAIR hyperintense lesions

PURPOSE

To identify qualitative MRI features of non-(contrast)-enhancing tumor (nCET) in glioblastoma's T2-FLAIR hyperintense lesion.

METHODS

Thirty-three histologically confirmed glioblastoma patients whose T1-, T2- and contrast-enhanced T1-weighted MRI and 11C-methionine positron emission tomography (Met-PET) were available were included in this study. Met-PET was utilized as a surrogate for tumor burden. Imaging features for identifying nCET were searched by qualitative examination of 156 targets. A new scoring system to identify nCET was established and validated by two independent observers.

RESULTS

Three imaging features were found helpful for identifying nCET; "Bulky gray matter involvement", "Around the rim of contrast-enhancement (Around-rim)," and "High-intensity on T1WI and low-intensity on T2WI (HighT1LowT2)" resulting in an nCET score = 2 × Bulky gray matter involvement - 2 × Around-rim + HighT1LowT2 + 2. The nCET score's classification performances of two independent observers measured by AUC were 0.78 and 0.80, with sensitivities and specificities using a threshold of four being 0.443 and 0.771, and 0.916 and 0.768, respectively. The weighted kappa coefficient for the nCET score was 0.946.

CONCLUSION

The current investigation demonstrated that qualitative assessments of glioblastoma's MRI might help identify nCET in T2/FLAIR high-intensity lesions. The novel nCET score is expected to aid in expanding treatment targets within the T2/FLAIR high-intensity lesions.

Correlation of T1- to T2-weighted signal intensity ratio with T1- and T2-relaxation time and IDH mutation status in glioma

The current study aimed to test whether the ratio of T1-weighted to T2-weighted signal intensity (T1W/T2W ratio: rT1/T2) derived from conventional MRI could act as a surrogate relaxation time predictive of IDH mutation status in histologically lower-grade gliomas. Strong exponential correlations were found between rT1/T2 and each of T1- and T2-relaxation times in eight subjects (rT1/T2 = 1.63exp^{-0.0005T1-relax} + 0.30 and rT1/T2 = 1.27exp^{-0.0081T2-relax} + 0.48; R² = 0.64 and 0.59, respectively). In a test cohort of 25 patients, mean rT1/T2 (mrT1/T2) was significantly higher in IDHwt tumors than in IDHmt tumors (p < 0.05) and the optimal cut-off of mrT1/T2 for discriminating IDHmt was 0.666-0.677, (AUC = 0.75, p < 0.05), which was validated in an external domestic cohort of 29 patients (AUC = 0.75, p = 0.02). However, this result was not validated in an external international cohort derived from TCIA/TCGA (AUC = 0.63, p = 0.08). The t-Distributed Stochastic Neighbor Embedding analysis revealed a greater diversity in image characteristics within the TCIA/TCGA cohort than in the two domestic cohorts. The failure of external validation in the TCIA/TCGA cohort could be attributed to its wider variety of original imaging characteristics.