Physiological MRI Biomarkers in the Differentiation Between Glioblastomas and Solitary Brain Metastases

Abstract

Purpose Glioblastomas (GB) and solitary brain metastases (BM) are the most common brain tumors in adults. GB and BM may appear similar in conventional magnetic resonance imaging (cMRI). Their management strategies, however, are quite different with significant consequences on clinical outcome. The aim of this study was to evaluate the usefulness of a previously presented physiological MRI approach scoping to obtain quantitative information about microvascular architecture and perfusion, neovascularization activity, and oxygen metabolism to differentiate GB from BM. Procedures Thirty-three consecutive patients with newly diagnosed, untreated, and histopathologically confirmed GB or BM were preoperatively examined with our physiological MRI approach as part of the cMRI protocol. Results Physiological MRI biomarker maps revealed several significant differences in the pathophysiology of GB and BM: Central necrosis was more hypoxic in GB than in BM (30 %; P = 0.036), which was associated with higher neovascularization activity (65 %; P = 0.043) and metabolic rate of oxygen (48 %; P = 0.004) in the adjacent contrast-enhancing viable tumor parts of GB. In peritumoral edema, GB infiltration caused neovascularization activity (93 %; P = 0.018) and higher microvascular perfusion (30 %; P = 0.022) associated with higher tissue oxygen tension (33 %; P = 0.020) and lower oxygen extraction from vasculature (32 %; P = 0.040). Conclusion Our physiological MRI approach, which requires only 7 min of extra data acquisition time, might be helpful to noninvasively distinguish GB and BM based on pathophysiological differences. However, further studies including more patients are required.

Hemodynamic Imaging in Cerebral Diffuse Glioma—Part A: Concept, Differential Diagnosis and Tumor Grading

“…The standard imaging protocol for suspected diffuse adult glioma evaluation includes different sequences of magnetic resonance imaging (MRI), such as pre-and postgadolinium contrast-enhanced (CE-) T1-weighted imaging, T2-weighted sequences including Fluid Attenuated Inversion Recovery (FLAIR) imaging, which are often complemented by diffusion-weighted imaging, susceptibility-weighted sequences and perfusion-weighted imaging (PWI) for a more refined diagnostic imaging workup [2,6]. The drawbacks of morphological MRI sequences are well-recognized and result in a broader differential diagnosis because other lesions, such as cerebral lymphoma, metastasis and abscesses, can have a similar radiological presentation during standard neuroimaging [7]. Additional limitations include imprecise characterization of glioma grading and subtype allocation for treatment decisions [8], as well as suboptimal determination of the extent of tumor infiltration for accurate resection planning [9].…”

Predicting Survival in Patients with Brain Tumors: Current State-of-the-Art of AI Methods Applied to MRI

“…A variety of methods have been proposed over the past decade with progressive developments leading to current state-of-the-art methods. Older methods were based on clinical [ 48 , 49 ], pathological [ 50 , 51 ] and imaging [ 30 , 52 ] biomarkers, those were gradually refined with the deployment of ML and DL techniques. In this review, ML and DL algorithms are chronologically presented.…”

Conventional and Advanced Magnetic Resonance Imaging Assessment of Non-Enhancing Peritumoral Area in Brain Tumor

“…Analyses of MR perfusion patterns in the NEPA have shown more coherent results than those of the perfusion patterns for the enhancing tumors in the differential diagnosis of HGGs and BMs. DSC perfusion studies demonstrated that rCBV in the NEPA is higher for glioblastomas than for BMs [18,[46][47][48][49]51,52], and that its performance in discriminating HGGs and BMs is better than that of the rCBV in the solid part of the tumor, with different diagnostic accuracies reported in the literature. Askaner et al found areas under the curve (AUCs) of 0.74 and 0.68, respectively, with normalized rCBV threshold values of 1.56 for NEPA and 3.75 for the enhancing region [48], while Neska-Matuszewska et al reported a higher accuracy of 0.94 for a max rCBV cutoff value of 0.98 in the peritumoral zone [47] and no significant differences for the max rCBV within the enhancing tumor.…”

“…Future large prospective studies aiming at the standardization of MRI acquisition protocols, as recommended by imaging societies [135,136], are warranted to define the actual role of NEPAs in brain tumors. More importantly, rigorous validation between centers and confirmation with clinical and histological data-using, for example, a neuro-navigation system for serial biopsy sampling-should be undertaken [52]. At present, NEPA assessment using advanced MRI techniques is unquestionably helpful in the imaging study of brain tumors and represents a promising tool for making NEPAs a robust quantitative imaging biomarker for differential diagnosis in brain tumors.…”