Quantitative Perfusion and Permeability Biomarkers in Brain Cancer from Tomographic CT and MR Images

Review of tracer kinetic models in evaluation of gliomas using dynamic contrast-enhanced imaging

Glioma is the most common type of primary malignant tumor of the central nervous system (CNS), and is characterized by high malignancy, high recurrence rate and poor survival. Conventional imaging techniques only provide information regarding the anatomical location, morphological characteristics, and enhancement patterns. In contrast, advanced imaging techniques such as dynamic contrast-enhanced (DCE) MRI or DCE CT can reflect tissue microcirculation, including tumor vascular hyperplasia and vessel permeability. Although several studies have used DCE imaging to evaluate gliomas, the results of data analysis using conventional tracer kinetic models (TKMs) such as Tofts or extended-Tofts model (ETM) have been ambiguous. More advanced models such as Brix's conventional two-compartment model (Brix), tissue homogeneity model (TH) and distributed parameter (DP) model have been developed, but their application in clinical trials has been limited. This review attempts to appraise issues on glioma studies using conventional TKMs, such as Tofts or ETM model, highlight advancement of DCE imaging techniques and provides insights on the clinical value of glioma management using more advanced TKMs.

Targeting Glioblastoma via Selective Alteration of Mitochondrial Redox State

Simple Summary

Glioblastoma is characterized by a pronounced redox imbalance due to elevated glycolytic and mitochondrial oxidative metabolism. New therapeutic strategies have been developed to modulate glioblastoma redox signaling to effectively suppress growth and prolong survival. However, drug selectivity and therapeutic relapse prove to be the major challenges. We describe a pharmacological strategy for the selective targeting and treatment of glioblastoma using the redox active combination drug menadione/ascorbate, which is characterized by tolerance to normal cells and tissues. Menadione/ascorbate treatment of glioblastoma mice suppressed tumor growth and significantly increased survival without adverse side effects. This is accompanied by increased oxidative stress, decreased reducing capacity and decreased cellular density in the tumor alone, as well as increased brain perfusion and decreased regulation of several oncoproteins and oncometabolites, which implies modulation of the immune response and reduced drug resistance. We believe that this therapeutic strategy is feasible and promising and deserves the attention of clinicians.

Abstract

Glioblastoma is one of the most aggressive brain tumors, characterized by a pronounced redox imbalance, expressed in a high oxidative capacity of cancer cells due to their elevated glycolytic and mitochondrial oxidative metabolism. The assessment and modulation of the redox state of glioblastoma are crucial factors that can provide highly specific targeting and treatment. Our study describes a pharmacological strategy for targeting glioblastoma using a redox-active combination drug. The experiments were conducted in vivo on glioblastoma mice (intracranial model) and in vitro on cell lines (cancer and normal) treated with the redox cycling pair menadione/ascorbate (M/A). The following parameters were analyzed in vivo using MRI or ex vivo on tissue and blood specimens: tumor growth, survival, cerebral perfusion, cellular density, tissue redox state, expression of tumor-associated NADH oxidase (tNOX) and transforming growth factor-beta 1 (TGF-β1). Dose-dependent effects of M/A on cell viability, mitochondrial functionality, and redox homeostasis were evaluated in vitro. M/A treatment suppressed tumor growth and significantly increased survival without adverse side effects. This was accompanied by increased oxidative stress, decreased reducing capacity, and decreased cellular density in the tumor only, as well as increased cerebral perfusion and down-regulation of tNOX and TGF-β1. M/A induced selective cytotoxicity and overproduction of mitochondrial superoxide in isolated glioblastoma cells, but not in normal microglial cells. This was accompanied by a significant decrease in the over-reduced state of cancer cells and impairment of their “pro-oncogenic” functionality, assessed by dose-dependent decreases in: NADH, NAD⁺, succinate, glutathione, cellular reducing capacity, mitochondrial potential, steady-state ATP, and tNOX expression. The safety of M/A on normal cells was compromised by treatment with cerivastatin, a non-specific prenyltransferase inhibitor. In conclusion, M/A differentiates glioblastoma cells and tissues from normal cells and tissues by redox targeting, causing severe oxidative stress only in the tumor. The mechanism is complex and most likely involves prenylation of menadione in normal cells, but not in cancer cells, modulation of the immune response, a decrease in drug resistance, and a potential role in sensitizing glioblastoma to conventional chemotherapy.

Extraction of a vascular function for a fully automated dynamic contrast-enhanced magnetic resonance brain image processing pipeline

PURPOSE

To develop a deep-learning model that leverages the spatial and temporal information from dynamic contrast-enhanced magnetic resonance (DCE MR) brain imaging in order to automatically estimate a vascular function (VF) for quantitative pharmacokinetic (PK) modeling.

METHODS

Patients with glioblastoma multiforme were scanned post-resection approximately every 2 months using a high spatial and temporal resolution DCE MR imaging sequence ( ≈ 5 s and ≈ 2 cm³ ). A region over the transverse sinus was manually drawn in the dynamic T1-weighted images to provide a ground truth VF. The manual regions and their resulting VF curves were used to train a deep-learning model based on a 3D U-net architecture. The model concurrently utilized the spatial and temporal information in DCE MR images to predict the VF. In order to analyze the contribution of the spatial and temporal terms, different weighted combinations were examined. The manual and deep-learning predicted regions and VF curves were compared.

RESULTS

Forty-three patients were enrolled in this study and 155 DCE MR scans were processed. The 3D U-net was trained using a loss function that combined the spatial and temporal information with different weightings. The best VF curves were obtained when both spatial and temporal information were considered. The predicted VF curve was similar to the manual ground truth VF curves.

CONCLUSION

The use of spatial and temporal information improved VF curve prediction relative to when only the spatial information is used. The method generalized well for unseen data and can be used to automatically estimate a VF curve suitable for quantitative PK modeling. This method allows for a more efficient clinical pipeline and may improve automation of permeability mapping.