Little is known about the growth dynamics of untreated glioblastoma and its possible influence on postoperative survival. Our aim was to analyze a possible association of preoperative growth dynamics with postoperative survival. We performed a retrospective analysis of all adult patients surgically treated for newly diagnosed glioblastoma at our center between 2010 and 2020. By volumetric analysis of data of patients with availability of ≥3 preoperative sequential MRI, a growth pattern was aimed to be identified. Main inclusion criterion for further analysis was the availability of two preoperative MRI scans with a slice thickness of 1 mm, at least 7 days apart. Individual growth rates were calculated. Association with overall survival (OS) was examined by multivariable. Out of 749 patients screened, 13 had ≥3 preoperative MRI, 70 had 2 MRI and met the inclusion criteria. A curve estimation regression model showed the best fit for exponential tumor growth. Median tumor volume doubling time (VDT) was 31 days, median specific growth rate (SGR) was 2.2% growth per day. SGR showed negative correlation with tumor size (rho = −0.59, P < .001). Growth rates were dichotomized according to the median SGR.OS was significantly longer in the group with slow growth (log-rank: P = .010). Slower preoperative growth was independently associated with longer overall survival in a multivariable Cox regression model for patients after tumor resection. Especially small lesions suggestive of glioblastoma showed exponential tumor growth with variable growth rates and a median VDT of 31 days. SGR was significantly associated with OS in patients with tumor resection in our sample.

A fully diagnostic MRI glioma protocol is key to monitoring therapy assessment but is time-consuming and especially challenging in critically ill and uncooperative patients. Artificial intelligence demonstrated promise in reducing scan time and improving image quality simultaneously. The purpose of this study was to investigate the diagnostic performance, the impact on acquisition acceleration, and the image quality of a deep learning optimized glioma protocol of the brain. Thirty-three patients with histologically confirmed glioblastoma underwent standardized brain tumor imaging according to the glioma consensus recommendations on a 3-Tesla MRI scanner. Conventional and deep learning-reconstructed (DLR) fluid-attenuated inversion recovery, and T2- and T1-weighted contrast-enhanced Turbo spin echo images with an improved in-plane resolution, i.e., super-resolution, were acquired. Two experienced neuroradiologists independently evaluated the image datasets for subjective image quality, diagnostic confidence, tumor conspicuity, noise levels, artifacts, and sharpness. In addition, the tumor volume was measured in the image datasets according to Response Assessment in Neuro-Oncology (RANO) 2.0, as well as compared between both imaging techniques, and various clinical–pathological parameters were determined. The average time saving of DLR sequences was 30% per MRI sequence. Simultaneously, DLR sequences showed superior overall image quality (all p < 0.001), improved tumor conspicuity and image sharpness (all p < 0.001, respectively), and less image noise (all p < 0.001), while maintaining diagnostic confidence (all p > 0.05), compared to conventional images. Regarding RANO 2.0, the volume of non-enhancing non-target lesions (p = 0.963), enhancing target lesions (p = 0.993), and enhancing non-target lesions (p = 0.951) did not differ between reconstruction types. The feasibility of the deep learning-optimized glioma protocol was demonstrated with a 30% reduction in acquisition time on average and an increased in-plane resolution. The evaluated DLR sequences improved subjective image quality and maintained diagnostic accuracy in tumor detection and tumor classification according to RANO 2.0.

Introduction: Glioblastoma (GBM) is a tumor with rapid growth and a possible relationship to elevated intracranial pressure (ICP). High ICP may not always be associated with clinical signs. A non-invasive technique for assessment of ICP is measuring the optic nerve sheath diameter (ONSD). Identifying patients who need immediate intervention is of importance in neuro-oncological care. The goal of this study is to assess the available magnetic resonance imaging (MRI) of patients with GBM with respect to pre- and postoperative ONSD.Methods and Materials: Retrospective data analysis was performed on all patients operated for GBM at a tertiary care center between 2010 and 2020. Two pre and one postoperative MRI had to be available. Clinical data and ONSD at multiple time points were analyzed and correlated, as well as preoperative volumetrics.Results: Sixty-seven patients met the inclusion criteria. Clinical signs of elevated ICP were seen in 25.4% (n = 17), while significant perifocal edema was present in 67.2% (n = 45) of patients. Clinical signs of preoperatively elevated ICP were associated with significantly elevated ONSD at diagnosis (p < 0.001) as well as preoperative tumor volume (p < 0.001). Significant perifocal edema at the time of diagnosis was associated with elevated ONSD (p = 0.029) and higher tumor volume (p = 0.003). In patients with significant edema, ONSD increased significantly between preoperative MRIs (p = 0.003/005). In patients with clinical signs of raised ICP, ONSD also increased, whereas it was stable in asymptomatic patients (yes: 5.01+/-4.17 to 5.83+/-0.55 mm, p = 0.010, no: 5.17+/-0.46 mm to 5.38+/-0.41 mm, p = 0.81). A significant increase of ONSD from diagnosis to preoperative MRI and a significant decrease until 3 months postoperatively were observed (p < 0.001).Conclusions: ONSD might help identify high ICP in patients with GBM. In this first-of-its kind study, we observed a significant increase of ONSD preoperatively, likely associated with edema. Postoperatively, ONSD decreased significantly until 3 months after surgery and increased again at 12 months. Further prospective data collection is warranted.

Background/Objectives: Postoperative MRI is crucial for detecting residual tumor, identifying complications, and planning subsequent therapy. This study evaluates accelerated deep learning reconstruction (DLR) versus standard clinical protocols for early postoperative MRI following tumor resection. Methods: This study uses a multidisciplinary approach involving a neuroradiologist, neurosurgeon, neuro-oncologist, and radiotherapist to evaluate qualitative aspects using a 5-point Likert scale, the preferred reconstruction variant and potential residual tumor of DLR and conventional reconstruction (CR) of FLAIR, T1-weighted non-contrast and contrast-enhanced (T1), and coronal T2-weighted (T2) sequences for 1.5 and 3 T MRI. Quantitative analysis included the image quality metrics Structural Similarity Index (SSIM), Multi-Scale SSIM (MS-SSIM), Feature Similarity Index (FSIM), Noise Quality Metric (NQM), signal-to-noise ratio (SNR), and Peak SNR (PSNR) with CR as a reference. Results: All raters strongly preferred DLR over CR. This was most pronounced for FLAIR images at 1.5 and 3 T (91% at 1.5 T and 97% at 3 T) and least pronounced for T1 at 1.5 T (79% for non-contrast-enhanced and 84% for contrast-enhanced sequences) and for T2 at 3 T (69%). DLR demonstrated superior qualitative image quality for all sequences and field strengths (p < 0.001), except for T2 at 3 T, which was observed across all raters (p = 0.670). Diagnostic confidence was similar at 3 T with better but non-significant differences for T2 (p = 0.134) and at 1.5 T with better but non-significant differences for non-contrast-enhanced T1 (p = 0.083) and only marginally significant results for FLAIR (p = 0.033). Both the SSIM and MS-SSIM indicated near-perfect similarity between CR and DLR. FSIM performs worse in terms of consistency between CR and DLR. The image quality metrics NQM, SNR, and PSNR showed better results for DLR. Visual assessment of residual tumor was similar at 3 T but differed at 1.5 T, with more residual tumor detected with DLR, especially by the neurosurgeon (n = 4). Conclusions: An accelerated DLR protocol demonstrates clinical feasibility, enabling high-quality reconstructions in challenging postoperative MRIs. DLR sequences received strong multidisciplinary preference, underscoring their potential to improve neuro-oncologic decision making and suitability for clinical implementation.

Objective : In moyamoya disease (MMD), the internal carotid and proximal cerebral arteries narrow, potentially leading to stroke or hemorrhage from fragile collaterals. Disease activity and progression may be detected by contrast-enhanced (CE) high-resolution (HR) vessel wall imaging (CE-VWI) on T1-weighted MRI. However, this imaging approach needs standardization for the evaluation of signal intensity and longitudinal reproducibility. Methods: MMD patients with at least two separate CE-VWI examinations on the same and on different scanners were included. Signal intensity of the vessel wall, pituitary stalk, and temporal lobe white matter were measured and normalized using manually selected regions of interest. Intraindividual longitudinal reproducibility of MRI was analyzed and the clinical course was correlated with vessel wall enhancement data. Results: Eighty-seven patients were analyzed. Primary analysis included 60 patients with two or more CE-VWI measurements (n = 129) with median 14.8 months between examinations (range: 2–36 months) on the same scanner. Intraindividual variation in pituitary stalk enhancement (positive control) and temporal lobe white matter enhancement (negative control) showed median signal variability of 20.5% and 17.5%, respectively. The pituitary-to-temporal lobe signal intensity ratio remained stable over time (p = 0.843) with 9.4% median variability. Correlation analysis revealed a significant positive association between pituitary and temporal lobe signal changes (ρ = 0.717, p < 0.001). A total of 75% of patients showed vessel wall contrast enhancement with fluctuating signal intensity over approximately 15.9 months, likely depicting disease activity. Conclusions: CE-VWI is important for screening disease activity in moyamoya patients. Our findings demonstrate longitudinal intraindividual reproducibility when normalized to pituitary stalk, enabling quantified evaluation of disease progression through longitudinal vessel wall contrast-enhancement changes.

Background/Objectives: Contralateral disease progression (CDP) in Moyamoya disease (MMD) is a clinically relevant phenomenon, yet difficult to predict. We investigated the association between vessel wall contrast enhancement (VW-CE) and CDP in a European cohort of adult MMD patients with initially unilateral hemodynamic impairment treated with unilateral revascularization. Methods: In this retrospective single-center study, patients with an indication for only unilateral revascularization (based on BOLD fMRI or H₂¹⁵O-PET) were stratified into three subgroups according to contralateral Suzuki grade (0, 1, or ≥2). CDP was defined as secondary contralateral disease progression on imaging with hemodynamic worsening requiring contralateral revascularization during follow-up. Results: Of 158 screened patients, 38 with adequate follow-up were included. CDP occurred in 31.6% (n = 12; 13.6%, 71.4%, and 44.4% in groups 1–3; mean follow-up 2.71 years). In time-to-event analysis, CDP occurred earlier in groups 2 and 3 than in group 1, without reaching significance (log-rank p = 0.078). Patients who later developed CDP more often showed preoperative VW-CE in either hemisphere (90.0% vs. 43.5%, p = 0.021), with consistent but non-significant trends for each hemisphere separately. De novo contralateral VW-CE occurred exclusively in CDP(+) patients (n = 4); contralateral VW-CE overall was significantly more common in CDP(+) than stable patients at the time of CDP (70% vs. 4.3%, p < 0.001). Conclusions: In this exploratory study, contralateral angiographic involvement and preoperative VW-CE were associated with CDP in unilaterally treated MMD patients. De novo contralateral VW-CE may represent a candidate imaging marker of active CDP. Larger multicenter studies are needed to confirm these hypothesis-generating findings.