Quantitative Assessment of Invasion of High-Grade Gliomas Using Diffusion Tensor Magnetic Resonance Imaging

Conventional and Advanced Imaging Techniques in Post-treatment Glioma Imaging

Despite decades of advancement in the diagnosis and therapy of gliomas, the most malignant primary brain tumors, the overall survival rate is still dismal, and their post-treatment imaging appearance remains very challenging to interpret. Since the limitations of conventional magnetic resonance imaging (MRI) in the distinction between recurrence and treatment effect have been recognized, a variety of advanced MR and functional imaging techniques including diffusion-weighted imaging (DWI), diffusion tensor imaging (DTI), perfusion-weighted imaging (PWI), MR spectroscopy (MRS), as well as a variety of radiotracers for single photon emission computed tomography (SPECT) and positron emission tomography (PET) have been investigated for this indication along with voxel-based and more quantitative analytical methods in recent years. Machine learning and radiomics approaches in recent years have shown promise in distinguishing between recurrence and treatment effect as well as improving prognostication in a malignancy with a very short life expectancy. This review provides a comprehensive overview of the conventional and advanced imaging techniques with the potential to differentiate recurrence from treatment effect and includes updates in the state-of-the-art in advanced imaging with a brief overview of emerging experimental techniques. A series of representative cases are provided to illustrate the synthesis of conventional and advanced imaging with the clinical context which informs the radiologic evaluation of gliomas in the post-treatment setting.

Diagnostic Implications of White Matter Tract Involvement by Intra-axial Brain Tumors

Introduction

Diffusion tensor imaging (DTI) is being increasingly used during brain tumor surgery. However, there is limited data available on its diagnostic and prognostic value. Our objective was to assess the pattern of involvement of white matter tracts (WMTs) by intra-axial brain tumors on DTI. Secondary objectives were to evaluate implications of involvement of WMT on surgical resection, and the post-operative functional outcome.

Methods

This was a retrospective study of consecutive patients, who underwent DTI-guided surgery for brain tumors. The involvement of WMTs by tumors on DTI was assessed by a radiologist (who was blind to the pathology) using the Witwer classification. The pathology was reported by histopathologists using the World Health Organization brain tumor classification. Karnofsky Performance Status Scale (KPS) was used for assessing patients’ neurological status at admission, and at follow-up.

Results

Forty-five (58.4%) out of 77 tumors reviewed caused infiltration of WMTs, whereas only 22 (28.6%) tumors caused displacement of WMTs (p= 0.040). Among 32 cases of astrocytoma, the involvement of WMTs was influenced by the grade of tumor (p= 0.012), as high-grade tumors caused infiltration (19; 59.4%), unlike low-grade tumors that commonly caused displacement (2; 50%). Oligodendrogliomas caused infiltration/disruption of WMTs in most cases, irrespective of the grade (19 out of 25 cases; 76%). At the last follow-up, 27 (35.1%) patients showed improvement in KPS and 14 (18.2%) reported deterioration, while there was no change observed in 36 (46.8%) patients. The infiltration of WMTs was associated with a poor functional outcome.

Conclusions

High-grade astrocytomas mostly cause infiltration of WMTs, unlike oligodendrogliomas, which often infiltrate WMTs, irrespective of the tumor grade. The infiltration of WMTs is associated with a poor functional outcome at follow-ups.

The value of diffusion tensor tractography delineating corticospinal tract in glioma in rat: validation via correlation histology

Background

An assessment of the degree of white matter tract injury is important in neurosurgical planning for patients with gliomas. The main objective of this study was to assess the injury grade of the corticospinal tract (CST) in rats with glioma using diffusion tensor imaging (DTI).

Methods

A total 17 rats underwent 7.0T MRI on day 10 after tumor implantation. The apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were acquired in the tumor, peritumoral and contralateral areas, and the ADC ratio (ipsilateral ADC/contralateral ADC) and rFA (relative FA = ipsilateral FA/contralateral FA) in the peritumoral areas were measured. The CST injury was divided into three grades and delineated by diffusion tensor tractography reconstruction imaging. The fiber density index (FDi) of the ipsilateral and contralateral CST and rFDi (relative FDi = ipsilateral FDi/contralateral FDi) in the peritumoral areas were measured. After the mice were sacrificed, the invasion of glioma cells and fraction of proliferating cells were observed by hematoxylin-eosin and Ki67 staining in the tumor and peritumoral areas. The correlations among the pathology results, CST injury grade and DTI parameter values were calculated using a Spearman correlation analysis. One-way analysis of variance was performed to compare the different CST injury grade by the rFA, rFDi and ADC ratio values.

Results

The tumor cells and proliferation index were positively correlated with the CST injury grade (r = 0.8857, 0.9233, P < 0.001). A negative correlation was demonstrated between the tumor cells and the rFA and rFDi values in the peritumoral areas (r = −0.8571, −0.5588), and the proliferation index was negatively correlated with the rFA and rFDi values (r = −0.8571, −0.5588), while the ADC ratio was not correlated with the tumor cells or proliferation index. The rFA values between the CST injury grades (1 and 3, 2 and 3) and the rFDi values in grades 1 and 3 significantly differed (P < 0.05).

Conclusions

Diffusion tensor imaging may be used to quantify the injury degrees of CST involving brain glioma in rats. Our data suggest that these quantitative parameters may be used to enhance the efficiency of delineating the relationship between fiber tracts and malignant tumor.

Magnetic Resonance Imaging Parameters and Their Impact on Survival of Patients with Glioblastoma: Tumor Perfusion Predicts Survival

BACKGROUND

Many prognostic factors influence overall survival (OS) of patients with glioblastoma. Despite gross total resection and Stupp protocol adherence, many patients have poor survival. Perfusion magnetic resonance imaging may assist in diagnosis, treatment monitoring, and prognostication.

METHODS

This retrospective study of 36 patients with glioblastoma assessed influence of preoperative magnetic resonance imaging parameters reflecting tumor cell density and vascularity and patient age on OS.

RESULTS

The area under curve based on optimal receiver operating characteristic curves for the perfusion parameters normalized relative tumor blood volume (n_rTBV) and normalized relative tumor blood flow (n_rTBF) were 0.92 and 0.89, respectively, and the highest among all imaging parameters and age. OS showed strongly negative correlations with corrected n_rTBV (R = -0.70; P < 0.001) and n_rTBF (R = -0.67; P < 0.001). The Cox model, which included age and imaging parameters, demonstrated that n_rTBV and n_rTBF were most predictive of OS, with hazard ratios of 5.97 (P = 0.0001) and 8.76 (P = 0.0001), respectively, compared with 1.63 (P = 0.19) for age. Eighteen patients with corrected n_rTBV ≤2.5 (best cutoff value) had a median OS of 15.1 months (95% confidence interval (CI), 11.34-21.25) compared with 2.8 months (95% CI, 1.48-4.03; P < 0.001) for 18 patients with corrected n_rTBV >2.5. Twenty-four patients with n_rTBF ≤2.79 had a median OS of 12 months (95% CI, 10.46-17.9) compared with 2.8 months for 12 patients with n_rTBF >2.79 (95% CI, 1.31-4.2; P < 0.001).

CONCLUSIONS

The dominant predictors of OS are normalized perfusion parameters n_rTBV and n_rTBF. Preoperative perfusion imaging may be used as a surrogate to predict glioblastoma aggressiveness and survival independent of treatment.