Increased diffusion in the normal appearing white matter of brain tumor patients: is this just tumor infiltration?

Accurate low and high grade glioma classification using free water eliminated diffusion tensor metrics and ensemble machine learning

Glioma, a predominant type of brain tumor, can be fatal. This necessitates an early diagnosis and effective treatment strategies. Current diagnosis is based on biopsy, prompting the need for non invasive neuroimaging alternatives. Diffusion tensor imaging (DTI) is a promising method for studying the pathophysiological impact of tumors on white matter (WM) tissue. Single-shell DTI studies in brain glioma patients have not accounted for free water (FW) contamination due to tumors. This study aimed to (a) assess the efficacy of a two-compartment DTI model that accounts for FW contamination and (b) identify DTI-based biomarkers to classify low-grade glioma (LGG) and high-grade glioma (HGG) patients. DTI data from 86 patients (LGG n = 39, HGG n = 47) were obtained using a routine clinical imaging protocol. DTI metrics of tumorous regions and normal-appearing white matter (NAWM) were evaluated. Advanced stacked-based ensemble learning was employed to classify LGG and HGG patients using both single- and two-compartment DTI model measures. The DTI metrics of the two-compartment model outperformed those of the standard single-compartment DTI model in terms of sensitivity, specificity, and area under the curve of receiver operating characteristic (AUC-ROC) score in classifying LGG and HGG patients. Four features (out of 16 features), namely fractional anisotropy (FA) of the edema and core region and FA and mean diffusivity of the NAWM region, showed superior performance (sensitivity = 92%, specificity = 90%, and AUC-ROC = 90%) in classifying LGG and HGG. This demonstrates that both tumorous and NAWM regions may be differentially affected in LGG and HGG patients. Our results demonstrate the significance of using a two-compartment DTI model that accounts for FW contamination by improving diagnostic accuracy. This improvement may eventually aid in planning treatment strategies for glioma patients.

Paediatric supratentorial tumours do not cause microstructural alterations in contralateral white matter: a preliminary study

BACKGROUND AND PURPOSE

Intracranial tumours in children can exhibit different characteristics compared to those in adults. Understanding the microstructural changes in the contralateral normal-appearing white matter (NAWM) in children with primary intracranial masses is essential for optimizing treatment strategies. This study aimed to investigate the apparent diffusion coefficient (ADC) changes in contralateral NAWM using fully automated methods and deep learning algorithms.

METHODS

We included 22 paediatric patients with primary supratentorial intracranial masses (23% high-grade) in the study. ADC values of the contralateral NAWM in the patient group were compared to those of a control group. Deep learning algorithms were utilized to analyse diffusion changes in NAWM.

RESULTS

The mean ADC values of contralateral NAWM in the patient group were 0.80 ± 0.03 × 10-3 mm2/s, while the control group had a mean ADC value of 0.81 ± 0.03 × 10-3 mm2/s. There was no statistically significant difference between the groups (p = 0.39). Our findings indicate that there are no significant diffusion changes in the contralateral white matter of children with supratentorial intracranial masses.

CONCLUSION

Primary supratentorial intracranial masses in children do not cause microstructural changes in contralateral normal-appearing white matter. This could be attributed to the less infiltrative nature and different biochemical profile of these tumour groups in the paediatric population. Further studies using advanced imaging techniques could provide additional insights into the distinct characteristics of paediatric intracranial tumours and improve patient management.

Advanced Magnetic Resonance Imaging Techniques in Management of Brain Metastases

Brain metastases are the most common intracranial tumors and occur in 20–40% of all cancer patients. Lung cancer, breast cancer, and melanoma are the most frequent primary cancers to develop brain metastases. Treatment options include surgical resection, whole brain radiotherapy, stereotactic radiosurgery, and systemic treatment such as targeted or immune therapy. Anatomical magnetic resonance imaging (MRI) of the tumor (in particular post-Gadolinium T₁-weighted and T₂-weighted FLAIR) provide information about lesion morphology and structure, and are routinely used in clinical practice for both detection and treatment response evaluation for brain metastases. Advanced MRI biomarkers that characterize the cellular, biophysical, micro-structural and metabolic features of tumors have the potential to improve the management of brain metastases from early detection and diagnosis, to evaluating treatment response. Magnetic resonance spectroscopy (MRS), chemical exchange saturation transfer (CEST), quantitative magnetization transfer (qMT), diffusion-based tissue microstructure imaging, trans-membrane water exchange mapping, and magnetic susceptibility weighted imaging (SWI) are advanced MRI techniques that will be reviewed in this article as they pertain to brain metastases.

Perilesional apparent diffusion coefficient in the preoperative evaluation of glioma grade

Preoperative identification of high-grade gliomas is critical to optimize treatment strategy and to predict prognosis. To determine whether perilesional apparent diffusion coefficient (ADC) values differ between high- and low-grade tumors, we assessed water diffusivity within normal-appearing brain parenchyma (NABP) surrounding gliomas in twenty-one treatment-naïve patients. This showed significantly lower mean and 25th percentile (Q1) ADC values in high- grade compared to low-grade gliomas respectively in the range of 10-25 and 10-30 mm away from combined tumor and surrounding T2 signal. Thus, perilesional ADC measurement may reflect the extent of tumor infiltration beyond the abnormality seen on conventional MRI.

Glioblastoma (GBM) effects on quantitative MRI of contralateral normal appearing white matter

PURPOSE

The objective was to investigate (with quantitative MRI) whether the normal appearing white matter (NAWM) of glioblastoma (GBM) patients on the contralateral side (cNAWM) was different from NAWM of healthy controls.

METHODS

Thirteen patients with newly diagnosed GBM and nine healthy age-matched controls were MRI-scanned with quantitative magnetization transfer (qMT), chemical exchange saturation transfer (CEST), and transverse relaxation time (T₂)-mapping. MRI scans were performed after surgery and before chemo-radiation treatment. Comprehensive qMT, CEST, T₂ data were acquired. A two-pool MT model was fit to qMT data in transient state, to calculate MT model parameters [Formula: see text]. CEST signal was isolated by removing the contributions from the MT and direct water saturation, and CEST signal was calculated for Amide (CEST_Amide), Amine (CEST_Amine) and nuclear overhauser effect, NOE (CEST_NOE).

RESULTS

There was no difference between GBM patients and normal controls in the qMT properties of the macromolecular pool [Formula: see text]. However, their free water pool spectrum was different (1/R_aT_2a,patient = 28.1 ± 3.9, 1/R_aT_2a,control = 25.0 ± 1.1, p = 0.03). This difference could be attributed to the difference in their T₂ time ([Formula: see text] = 83 ± 4, [Formula: see text] = 88 ± 1, p = 0.004). CEST signals were statistically significantly different with the CEST_Amide having the largest difference between the two cohorts (CEST_{Amide,patient} = 2.8 ± 0.4, CEST_{Amide,control} = 3.4 ± 0.5, p = 0.009).

CONCLUSIONS

CEST in cNAWM of GBM patients was lower than healthy controls which could be caused by modified brain metabolism due to tumor cell infiltration. There was no difference in MT properties of the patients and controls, however, the differences in free water pool properties were mainly due to reduced T₂ in cNAWM of the patients (resulting from structural changes and increased cellularity).

Value of diffusion kurtosis imaging in assessing low-grade gliomas

BACKGROUND

Diffusion kurtosis imaging (DKI) measures have been shown to provide increased sensitivity relative to diffusion tensor imaging (DTI) in detecting pathologies.

PURPOSE

To compare the sensitivity of DKI-derived kurtosis and diffusion maps for assessment of low-grade gliomas (LGG).

STUDY TYPE

Prospective study.

POPULATION

In all, 19 LGG patients and 26 healthy control subjects were recruited.

FIELD STRENGTH/SEQUENCE

Echo-planar-imaging diffusion-weighted MR images (b-values = 0, 1000, and 2000 with 30 diffusion gradient directions) were acquired on a 3T scanner.

ASSESSMENT

Maps for mean, axial, and radial diffusivity (MD, AD, and RD) and kurtosis (MK, AK, and RK), and fractional anisotropy (FA) were evaluated in the tumor, perilesional white matter, and contralateral normal-appearing white matter regions.

STATISTICAL TESTING

General linear models (GLM), Cohen's d for effect size estimates, false discovery rate (FDR) for multiple corrections, Cochran Q-test.

RESULTS

Pairwise differences were observed for all diffusion and kurtosis measures between the studied regions (FDR P < 0.001), except an FA map that failed to show significant differences between the lesion and perilesional white matter (FDR P = 0.373). Effect size analysis showed that kurtosis metrics were found to be 18.8% (RK, P = 0.144) to 29.1% (AK, P < 0.05) more sensitive in discriminating perilesional regions from the lesion than corresponding diffusion metrics, whereas AK provided a 25.0% (P < 0.05) increase in sensitivity in discriminating perilesional and contralateral white matter. RK was found to be the most sensitive to contralateral white matter differences between low-grade gliomas and controls, with MK and RK providing a significantly greater sensitivity of 587.2% (P < 0.001) and 320.7% (P < 0.001) than MD and RD, respectively.

DATA CONCLUSION

Kurtosis maps showed increased sensitivity, as compared to counterpart diffusion maps, for evaluation of microstructural changes in gliomas with a 3-6-fold increment in assessing changes in contralateral white matter.

LEVEL OF EVIDENCE

2 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2018;48:1551-1558.