Jeroen de Bresser

Arterial spin labeling (ASL) and dynamic susceptibility contrast (DSC) magnetic resonance imaging (MRI) have shown potential for differentiating tumor progression from pseudoprogression. For pseudocontinuous ASL with a single postlabeling delay, the presence of delayed arterial transit times (ATTs) could affect the evaluation of ASL‐MRI perfusion data. In this study, the influence of ATT artifacts on the perfusion assessment and differentiation between tumor progression and pseudoprogression were studied. This study comprised 66 adult patients (mean age 60 ± 13 years; 40 males) with a histologically confirmed glioblastoma who received postoperative radio (chemo)therapy. ASL‐MRI and DSC‐MRI scans were acquired at 3 months postradiotherapy as part of the standard clinical routine. These scans were visually scored regarding (i) the severity of ATT artifacts (%) on the ASL‐MRI scans only, scored by two neuroradiologists; (ii) perfusion of the enhancing tumor lesion; and (iii) radiological evaluation of tumor progression versus pseudoprogression by one neuroradiologist. The final outcome was based on combined clinical and radiological follow‐up until 9 months postradiotherapy. ATT artifacts were identified in all patients based on the mean scores of two raters. A significant difference between the radiological evaluation of ASL‐MRI and DSC‐MRI was observed only for ASL images with moderate ATT severity (30%–65%). The perfusion assessment showed ASL‐MRI tending more towards hyperperfusion than DSC‐MRI in the case of moderate ATT artifacts. In addition, there was a significant difference between the prediction of tumor progression with ASL‐MRI and the final outcome in the case of severe ATT artifacts (McNemar test, p = 0.041). Despite using ASL imaging parameters close to the recommended settings, ATT artifacts frequently occur in patients with treated brain tumors. Those artifacts could hinder the radiological evaluation of ASL‐MRI data and the detection of true disease progression, potentially affecting treatment decisions for patients with glioblastoma.

Background Distinguishing true tumor progression (TP) from treatment-induced abnormalities (eg, pseudo-progression (PP) after radiotherapy) on conventional MRI scans remains challenging in patients with a glioblastoma. We aimed to establish brain MRI phenotypes of glioblastomas early after treatment by combined analysis of structural and perfusion tumor characteristics and assessed the relation with recurrence rate and overall survival time. Methods Structural and perfusion MR images of 67 patients at 3 months post-radiotherapy were visually scored by a neuroradiologist. In total 23 parameters were predefined and used for hierarchical clustering analysis. Progression status was assessed based on the clinical course of each patient 9 months after radiotherapy (or latest available). Multivariable Cox regression models were used to determine the association between the phenotypes, recurrence rate, and overall survival. Results We established 4 subgroups with significantly different tumor MRI characteristics, representing distinct MRI phenotypes of glioblastomas: TP and PP rates did not differ significantly between subgroups. Regression analysis showed that patients in subgroup 1 (characterized by having mostly small and ellipsoid nodular enhancing lesions with some hyper-perfusion) had a significant association with increased mortality at 9 months (HR: 2.6 (CI: 1.1–6.3); P = .03) with a median survival time of 13 months (compared to 22 months of subgroup 2). Conclusions Our study suggests that distinct MRI phenotypes of glioblastomas at 3 months post-radiotherapy can be indicative of overall survival, but does not aid in differentiating TP from PP. The early prognostic information our method provides might in the future be informative for prognostication of glioblastoma patients.

Background Fatigue is a commonly reported and severe symptom in primary brain tumor patients, but the exact occurrence in meningioma patients is unknown. This study aimed to determine the frequency and severity of fatigue in meningioma patients as well as associations between the level of fatigue and patient-, tumor-, and treatment-related factors. Methods In this multicenter cross-sectional study, meningioma patients completed questionnaires on fatigue (MFI-20), sleep (PSQI), anxiety and depression (HADS), tumor-related symptoms (MDASI-BT), and cognitive functioning (MOS-CFS). Multivariable regression models were used to evaluate the independent association between fatigue and each patient-, tumor-, and treatment-related factor separately, corrected for relevant confounders. Results Based on predetermined in- and exclusion criteria, 275 patients, on average 5.3 (SD = 2.0) year since diagnosis, were recruited. Most patients had undergone resection (92%). Meningioma patients reported higher scores on all fatigue subscales compared to normative data and 26% were classified as fatigued. Having experienced a complication due to resection (OR 3.6, 95% CI: 1.8–7.0), having received radiotherapy (OR 2.4, 95% CI: 1.2–4.8), a higher number of comorbidities (OR 1.6, 95% CI: 1.3–1.9) and lower educational level (low level as reference; high level OR 0.3, 95% CI: 0.2–0.7) were independently associated with more fatigue. Conclusions Fatigue is a frequent problem in meningioma patients even many years after treatment. Both patient- and treatment-related factors were determinants of fatigue, with the treatment-related factors being the most likely target for intervention in this patient population.