Vascular Signature Mapping of Brain Tumor Genotypes

A glioma is a primary brain tumor in adults that is characterized by a highly variable, but overall poor survival. The optimal timing of treatment is in part determined by the expected biological behavior of the tumor. At present the expected biological behavior, determined by the tumor genotype, can only be determined by tissue analysis, which requires brain surgery. Non-invasive and improved diagnostic methods are sought to obtain insight into the molecular profile of the tumor and the expected biological behavior to avoid surgery performed solely for diagnostic purposes. Vascularization is an important aspect of the biological behavior of a primary brain tumor. Tumor vascularization characteristics can be assessed by Magnetic Resonance Imaging (MRI), but with the currently available technology this can only be achieved with unacceptably long scan times. In this proposal, the investigators will develop and optimize a novel MRI protocol to gather a large set of quantitative vascularization parameters within an acceptable scan time. The hypothesis is that from such a 'vascular signature' the tumor genotype can be inferred by means of machine learning.

Objective of the study: The primary objective is to develop and clinically validate a fast multi-parametric MRI acquisition technique, for non-invasive and comprehensive characterization of the tumor's vascularization, 'vascular signature mapping', at 3 Tesla (3T) and 7 Tesla (7T) MRI. The secondary objective is to limit difficult and time-consuming visual interpretation of the acquired vascular information by developing a computer-aided diagnostic algorithm that automatically and accurately predicts the brain tumor genotype from the vascular signature maps. Study design: The study 'Vascular Signature Mapping for Brain Tumor Genotypes' is a multi-center observational diagnostic study, which consists of two parts. The first part of this study aims to develop and optimize a new MRI protocol that will exploit the effect of contrast agent on the MRI signal to infer information on the vascular properties of a tumor. It combines scans during the pre-contrast injection phase, the dynamic phase during and right after contrast agent injection, as well as the quasi static post-contrast phase. This research will focus on studying the optimal way of encoding the vascular architecture into the MRI signal and the decoding approach, In addition, the image processing methodology will be optimized. The second part of this study is a proof-of-concept clinical study. This part aims to link the vascular parameters with molecular profiles of tumors by using the collected data for the development of machine learning algorithms for predicting the tumor's genotype based on its vascular signature. Study population: The study population consists of 3 cohorts, all aged over 18 years, able to provide written informed consent and without contraindications for contrast-enhanced MRI. The first cohort, for development and optimization of the protocol, consists of 60 patients scheduled for brain MRI as part of their standard clinical diagnostic procedure and in whom contrast agent (CA) administration is part of their standard radiological assessment. These patients do not necessarily have a brain tumor as the purpose here is to develop and evaluate the vascular signature mapping sequence in general. The second cohort consists of 20 glioma patients in whom the vascular signature mapping sequence is tested and for a direct comparison between 3T and 7T MRI, where the 3T scan is an extension of the diagnostic care and the additional 7T scan is optional. The third cohort, for the clinical proof-of-concept study, consists of 100 adult patients referred for biopsy or surgery of suspected glioma. Primary study parameters/outcome of the study: The endpoint of the first part of the study is a novel MRI protocol for characterization of the vessel architecture, assessed with respect to the signal-to-noise ratio (SNR) and the ability to obtain vascular Information. The main parameters that will be used for characterization of the vasculature are physiological parameters including the vessel architecture, cerebral blood volume (CBV), cerebral blood flow (CBF), mean transit time (MTT), and oxygenation level. The main study end point of the second part of the study is the accuracy of automatic classification of the tumor's genotype. The accuracy of the new method will be compared to the current state-of-the-art reference method based on conventional MRI. Secondary study parameters/outcome of the study: Baseline characteristics of subjects (including age, sex, Karnofsky performance status, tumor histology, molecular parameters (1p/19q, Isocitrate Dehydrogenase (IDH1/2) and 06-Methylguanine-DNA Methyltransferase (MGMT) status), tumor location, supportive and antitumor treatment). In addition, the outcome (e.g. mortality, tumor progression, radiation necrosis, functioning of patients) will be used as study parameter. The outcome will be determined from the follow-up scans after 3 and 6 months, where the criterion for progression or pseudo-progression is determined by the outcome of the scan. Nature and extent of the burden and risks associated with participation, benefit and group relatedness: For the first cohort, the additional burden will not be substantial for the participant. The additional scan time will not exceed 10 minutes and therefore the impact on the patient will be limited. For the second and third cohort, the additional burden includes a prolonged MRI examination at a clinical MRI scanner (3T) and an optional additional examination at 7T MRI including additional CA injection. The ultrahigh field 7T MRI system is commonly used for research and no serious adverse events have been reported. Patients participating in this study will have no personal benefit; their participation aids in the development of a novel MRI method for the non-invasive determination of the tumor's molecular profile. Moreover, there is a small chance that the additional 7T MRI scan would provide more Information on the status of the disease in the participant.