Synthesizing a Contrast-Enhancement Map in Patients with High-Grade Gliomas Based on a Postcontrast MR Imaging Quantification Only

Brain tumor enhancement prediction from pre-contrast conventional weighted images using synthetic multiparametric mapping and generative artificial intelligence

Background

Gadolinium-based contrast agents (GBCAs) are usually employed for glioma diagnosis. However, GBCAs raise safety concerns, lead to patient discomfort and increase costs. Parametric maps offer a potential solution by enabling quantification of subtle tissue changes without GBCAs, but they are not commonly used in clinical practice due to the need for specifically targeted sequences. This work proposes to predict post-contrast T1-weighted enhancement without GBCAs from pre-contrast conventional weighted images through synthetic parametric maps computed with generative artificial intelligence (deep learning).

Methods

In this retrospective study, three datasets have been employed: (I) a proprietary dataset with 15 glioma patients (hereafter, GLIOMA dataset); (II) relaxometry maps from 5 healthy volunteers; and (III) UPenn-GBM, a public dataset with 493 glioblastoma patients. A deep learning method for synthesizing parametric maps from only two conventional weighted images is proposed. Particularly, we synthesize longitudinal relaxation time (T1), transversal relaxation time (T2), and proton density (PD) maps. The deep learning method is trained in a supervised manner with the GLIOMA dataset, which comprises weighted images and parametric maps obtained with magnetic resonance image compilation (MAGiC). Thus, MAGiC maps were used as references for the training. For testing, a leave-one-out scheme is followed. Finally, the synthesized maps are employed to predict T1-weighted enhancement without GBCAs. Our results are compared with those obtained by MAGiC; specifically, both the maps obtained with MAGiC and the synthesized maps are used to distinguish between healthy and abnormal tissue (ABN) and, particularly, tissues with and without T1-weighted enhancement. The generalization capability of the method was also tested on two additional datasets (healthy volunteers and the UPenn-GBM).

Results

Parametric maps synthesized with deep learning obtained similar performance compared to MAGiC for discriminating normal from ABN (sensitivities: 88.37% vs. 89.35%) and tissue with and without T1-weighted enhancement (sensitivities: 93.26% vs. 87.29%) on the GLIOMA dataset. These values were comparable to those obtained on UPenn-GBM (sensitivities of 91.23% and 81.04% for each classification).

Conclusions

Our results suggest the feasibility to predict T1-weighted-enhanced tissues from pre-contrast conventional weighted images using deep learning for the synthesis of parametric maps.

Magnetic resonance relaxometry in quantitative imaging of brain gliomas: A literature review

Magnetic resonance (MR) relaxometry is a quantitative imaging method that measures tissue relaxation properties. This review discusses the state of the art of clinical proton MR relaxometry for glial brain tumors. Current MR relaxometry technology also includes MR fingerprinting and synthetic MRI, which solve the inefficiencies and challenges of earlier techniques. Despite mixed results regarding its capability for brain tumor differential diagnosis, there is growing evidence that MR relaxometry can differentiate between gliomas and metastases and between glioma grades. Studies of the peritumoral zones have demonstrated their heterogeneity and possible directions of tumor infiltration. In addition, relaxometry offers T2* mapping that can define areas of tissue hypoxia not discriminated by perfusion assessment. Studies of tumor therapy response have demonstrated an association between survival and progression terms and dynamics of native and contrast-enhanced tumor relaxometric profiles. In conclusion, MR relaxometry is a promising technique for glial tumor diagnosis, particularly in association with neuropathological studies and other imaging techniques.

Toward deep learning replacement of gadolinium in neuro-oncology: A review of contrast-enhanced synthetic MRI

Gadolinium-based contrast agents (GBCAs) have become a crucial part of MRI acquisitions in neuro-oncology for the detection, characterization and monitoring of brain tumors. However, contrast-enhanced (CE) acquisitions not only raise safety concerns, but also lead to patient discomfort, the need of more skilled manpower and cost increase. Recently, several proposed deep learning works intend to reduce, or even eliminate, the need of GBCAs. This study reviews the published works related to the synthesis of CE images from low-dose and/or their native -non CE- counterparts. The data, type of neural network, and number of input modalities for each method are summarized as well as the evaluation methods. Based on this analysis, we discuss the main issues that these methods need to overcome in order to become suitable for their clinical usage. We also hypothesize some future trends that research on this topic may follow.

Metabolic and transcriptomic profiles of glioblastoma invasion revealed by comparisons between patients and corresponding orthotopic xenografts in mice

The invasive behavior of glioblastoma, the most aggressive primary brain tumor, is considered highly relevant for tumor recurrence. However, the invasion zone is difficult to visualize by Magnetic Resonance Imaging (MRI) and is protected by the blood brain barrier, posing a particular challenge for treatment. We report biological features of invasive growth accompanying tumor progression and invasion based on associated metabolic and transcriptomic changes observed in patient derived orthotopic xenografts (PDOX) in the mouse and the corresponding patients’ tumors. The evolution of metabolic changes, followed in vivo longitudinally by ¹H Magnetic Resonance Spectroscopy (¹H MRS) at ultra-high field, reflected growth and the invasive properties of the human glioblastoma transplanted into the brains of mice (PDOX). Comparison of MRS derived metabolite signatures, reflecting temporal changes of tumor development and invasion in PDOX, revealed high similarity to spatial metabolite signatures of combined multi-voxel MRS analyses sampled across different areas of the patients’ tumors. Pathway analyses of the transcriptome associated with the metabolite profiles of the PDOX, identified molecular signatures of invasion, comprising extracellular matrix degradation and reorganization, growth factor binding, and vascular remodeling. Specific analysis of expression signatures from the invaded mouse brain, revealed extent of invasion dependent induction of immune response, recapitulating respective signatures observed in glioblastoma. Integrating metabolic profiles and gene expression of highly invasive PDOX provided insights into progression and invasion associated mechanisms of extracellular matrix remodeling that is essential for cell–cell communication and regulation of cellular processes. Structural changes and biochemical properties of the extracellular matrix are of importance for the biological behavior of tumors and may be druggable. Ultra-high field MRS reveals to be suitable for in vivo monitoring of progression in the non-enhancing infiltration zone of glioblastoma.

Age-Related Changes in Relaxation Times, Proton Density, Myelin, and Tissue Volumes in Adult Brain Analyzed by 2-Dimensional Quantitative Synthetic Magnetic Resonance Imaging

OBJECTIVES

Quantitative synthetic magnetic resonance imaging (MRI) enables the determination of fundamental tissue properties, namely, T1 and T2 relaxation times and proton density (PD), in a single scan. Myelin estimation and brain segmentation based on these quantitative values can also be performed automatically. This study aimed to reveal the changes in tissue characteristics and volumes of the brain according to age and provide age-specific reference values obtained by quantitative synthetic MRI.

MATERIALS AND METHODS

This was a prospective study of healthy subjects with no history of brain diseases scanned with a multidynamic multiecho sequence for simultaneous measurement of relaxometry of T1, T2, and PD. We performed myelin estimation and brain volumetry based on these values. We performed volume-of-interest analysis on both gray matter (GM) and white matter (WM) regions for T1, T2, PD, and myelin volume fraction maps. Tissue volumes were calculated in the whole brain, producing brain parenchymal volume, GM volume, WM volume, and myelin volume. These volumes were normalized by intracranial volume to a brain parenchymal fraction, GM fraction, WM fraction, and myelin fraction (MyF). We examined the changes in the mean regional quantitative values and segmented tissue volumes according to age.

RESULTS

We analyzed data of 114 adults (53 men and 61 women; median age, 66.5 years; range, 21-86 years). T1, T2, and PD values showed quadratic changes according to age and stayed stable or decreased until around 60 years of age and increased thereafter. Myelin volume fraction showed a reversed trend. Brain parenchymal fraction and GM fraction decreased throughout all ages. The approximation curves showed that WM fraction and MyF gradually increased until around the 40s to 50s and decreased thereafter. A significant decline in MyF was first noted in the 60s age group (Tukey test, P < 0.001).

CONCLUSIONS

Our study showed changes according to age in tissue characteristic values and brain volumes using quantitative synthetic MRI. The reference values for age demonstrated in this study may be useful to discriminate brain disorders from healthy brains.

Quantitative MRI using relaxometry in malignant gliomas detects contrast enhancement in peritumoral oedema

Malignant gliomas are primary brain tumours with an infiltrative growth pattern, often with contrast enhancement on magnetic resonance imaging (MRI). However, it is well known that tumour infiltration extends beyond the visible contrast enhancement. The aim of this study was to investigate if there is contrast enhancement not detected visually in the peritumoral oedema of malignant gliomas by using relaxometry with synthetic MRI. 25 patients who had brain tumours with a radiological appearance of malignant glioma were prospectively included. A quantitative MR-sequence measuring longitudinal relaxation (R₁), transverse relaxation (R₂) and proton density (PD), was added to the standard MRI protocol before surgery. Five patients were excluded, and in 20 patients, synthetic MR images were created from the quantitative scans. Manual regions of interest (ROIs) outlined the visibly contrast-enhancing border of the tumours and the peritumoral area. Contrast enhancement was quantified by subtraction of native images from post GD-images, creating an R₁-difference-map. The quantitative R₁-difference-maps showed significant contrast enhancement in the peritumoral area (0.047) compared to normal appearing white matter (0.032), p = 0.048. Relaxometry detects contrast enhancement in the peritumoral area of malignant gliomas. This could represent infiltrative tumour growth.