Häufigkeit und Risikofaktoren von Komplikationen in der Frühphase zerebraler Insulte

Schlaganfälle sind häufige und gefährliche Erkrankungen. In einer prospektiven Studie untersuchten wir Risikofaktoren für Komplikationen in der Frühphase zerebraler Ischämien und intrazerebraler Blutungen. Innerhalb der Studienphase konnten wir sowohl soziodemografische als auch kardiovaskuläre Faktoren und die Infarktgröße als Risikofaktoren für das Auftreten verschiedener Komplikationen (Tod, Pneumonie, andere Infektionen, Reinsult, Herzinfarkt, Intubation) identifizieren. Im Hinblick auf andere Autoren konnten wir so teils deren Ergebnisse unterstützen, konnten aber auch neue Aspekte aufzeigen, die zu einer effizienten Prophylaxe und Therapie von Komplikationen bei betroffenen Patienten hilfreich sein können

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.

To investigate the applicability and performance of automated machine learning (AutoML) for potential applications in diagnostic neuroradiology. In the medical sector, there is a rapidly growing demand for machine learning methods, but only a limited number of corresponding experts. The comparatively simple handling of AutoML should enable even non-experts to develop adequate machine learning models with manageable effort. We aim to investigate the feasibility as well as the advantages and disadvantages of developing AutoML models compared to developing conventional machine learning models. We discuss the results in relation to a concrete example of a medical prediction application. In this retrospective IRB-approved study, a cohort of 107 patients who underwent gross total meningioma resection and a second cohort of 31 patients who underwent subtotal resection were included. Image segmentation of the contrast enhancing parts of the tumor was performed semi-automatically using the open-source software platform 3D Slicer. A total of 107 radiomic features were extracted by hand-delineated regions of interest from the pre-treatment MRI images of each patient. Within the AutoML approach, 20 different machine learning algorithms were trained and tested simultaneously. For comparison, a neural network and different conventional machine learning algorithms were trained and tested. With respect to the exemplary medical prediction application used in this study to evaluate the performance of Auto ML, namely the pre-treatment prediction of the achievable resection status of meningioma, AutoML achieved remarkable performance nearly equivalent to that of a feed-forward neural network with a single hidden layer. However, in the clinical case study considered here, logistic regression outperformed the AutoML algorithm. Using independent test data, we observed the following classification results (AutoML/neural network/logistic regression): mean area under the curve = 0.849/0.879/0.900, mean accuracy = 0.821/0.839/0.881, mean kappa = 0.465/0.491/0.644, mean sensitivity = 0.578/0.577/0.692 and mean specificity = 0.891/0.914/0.936. The results obtained with AutoML are therefore very promising. However, the AutoML models in our study did not yet show the corresponding performance of the best models obtained with conventional machine learning methods. While AutoML may facilitate and simplify the task of training and testing machine learning algorithms as applied in the field of neuroradiology and medical imaging, a considerable amount of expert knowledge may still be needed to develop models with the highest possible discriminatory power for diagnostic neuroradiology

The Simpson grading: defining the optimal threshold for gross total resection in meningioma surgery

Classification of the extent of resection into gross and subtotal resection (GTR and STR) after meningioma surgery is derived from the Simpson grading. Although utilized to indicate adjuvant treatment or study inclusion, conflicting definitions of STR in terms of designation of Simpson grade III resections exist. Correlations of Simpson grading and dichotomized scales (Simpson grades I-II vs ≥ III and grade I-III vs ≥ IV) with postoperative recurrence/progression were compared using Cox regression models. Predictive values were further compared by time-dependent receiver operating curve (tdROC) analyses. In 939 patients (28% males, 72% females) harboring WHO grade I (88%) and II/III (12%) meningiomas, Simpson grade I, II, III, IV, and V resections were achieved in 29%, 48%, 11%, 11%, and < .5%, respectively. Recurrence/progression was observed in 112 individuals (12%) and correlated with Simpson grading (p = .003). The risk of recurrence/progression was increased after STR in both dichotomized scales but higher when subsuming Simpson grade ≥ IV than grade ≥ III resections (HR: 2.49, 95%CI 1.50-4.12; p < .001 vs HR: 1.67, 95%CI 1.12-2.50; p = .012). tdROC analyses showed moderate predictive values for the Simpson grading and significantly (p < .05) lower values for both dichotomized scales. AUC values differed less between the Simpson grading and the dichotomization into grade I-III vs ≥ IV than grade I-II vs ≥ III resections. Dichotomization of the extent of resection is associated with a loss of the prognostic value. The value for the prediction of progression/recurrence is higher when dichotomizing into Simpson grade I-III vs ≥ IV than into grade I-II vs ≥ III resections

Predicting the risk of postoperative recurrence and high-grade histology in patients with intracranial meningiomas using routine preoperative MRI

Risk factors for prediction of prognosis in meningiomas derivable from routine preoperative magnetic resonance imaging (pMRI) remain elusive. Correlations of tumor and edema volume, disruption of the arachnoid layer, heterogeneity of contrast enhancement, enhancement of the capsule, T2-intensity, tumor shape, and calcifications on pMRI with tumor recurrence and high-grade (WHO grade II/III) histology were analyzed in 565 patients who underwent surgery for WHO grade I (N = 516, 91%) or II/III (high-grade histology, N = 49, 9%) meningioma between 1991 and 2018. Edema volume (OR, 1.00; p = 0.003), heterogeneous contrast enhancement (OR, 3.10; p < 0.001), and an irregular shape (OR, 2.16; p = 0.015) were associated with high-grade histology. Multivariate analyses confirmed edema volume (OR, 1.00; p = 0.037) and heterogeneous contrast enhancement (OR, 2.51; p = 0.014) as risk factors for high-grade histology. Tumor volume (HR, 1.01; p = 0.045), disruption of the arachnoid layer (HR, 2.50; p = 0.003), heterogeneous contrast enhancement (HR, 2.05; p = 0.007), and an irregular tumor shape (HR, 2.57; p = 0.001) were correlated with recurrence. Multivariate analyses confirmed tumor volume (HR, 1.01; p = 0.032) and disruption of the arachnoid layer (HR, 2.44; p = 0.013) as risk factors for recurrence, independent of histology. Subgroup analyses revealed disruption of the arachnoid layer (HR, 9.41; p < 0.001) as a stronger risk factor for recurrence than high-grade histology (HR, 5.15; p = 0.001). Routine pMRI contains relevant information about the risk of recurrence or high-grade histology of meningioma patients. Loss of integrity of the arachnoid layer on MRI had a higher prognostic value than the WHO grading, and underlying histological or molecular alterations remain to be determined

Molecular neuropathology of brain-invasive meningiomas

Invasion of brain tissue by meningiomas has been identified as one key factor for meningioma recurrence. The identification of meningioma tumor tissue surrounded by brain tissue in neurosurgical samples has been touted as a criterion for atypical meningioma (CNS WHO grade 2), but is only rarely seen in the absence of other high-grade features, with brain-invasive otherwise benign (BIOB) meningiomas remaining controversial. While post-surgery irradiation therapy might be initiated in brain-invasive meningiomas to prevent recurrences, specific treatment approaches targeting key molecules involved in the invasive process are not established. Here we have compiled the current knowledge about mechanisms supporting brain tissue invasion by meningiomas and summarize preclinical models studying targeted therapies with potential inhibitory effects

Transcriptional factors for epithelial-mesenchymal transition are associated with mesenchymal differentiation in gliosarcoma.

Gliosarcoma is a rare variant of glioblastoma characterized by a biphasic pattern of glial and mesenchymal differentiation. It is unclear whether mesenchymal differentiation in gliosarcomas is because of extensive genomic instability and/or to a mechanism similar to epithelial-mesenchymal transition (EMT). In the present study, we assessed 40 gliosarcomas for immunoreactivity of Slug, Twist, matrix metalloproteinase-2 (MMP-2) and matrix metalloproteinase-9 (MMP-9), which are involved in EMT in epithelial tumors. Nuclear Slug expression was observed in >50% of neoplastic cells in mesenchymal tumor areas of 33 (83%) gliosarcomas, but not in glial areas (P 50% of neoplastic cells in mesenchymal tumor areas of 35 (88%) gliosarcomas, but glial tumor areas were largely negative except in four cases (P 10% neoplastic cells. Thus, expression of Slug, Twist, MMP-2 and MMP-9 was characteristic of mesenchymal tumor areas of gliosarcomas, suggesting that mechanisms involved in the EMT in epithelial neoplasms may play roles in mesenchymal differentiation in gliosarcomas. © 2012 The Authors; Brain Pathology © 2012 International Society of Neuropathology

ADAMTS genes and the risk of cerebral aneurysm

OBJECTIVE Cerebral aneurysms (CAs) affect 2%-5% of the population, and familial predisposition plays a significant role in CA pathogenesis. Several lines of evidence suggest that genetic variations in matrix metalloproteinase genes (MMP) are involved in the etiopathology of CAs. The authors performed a case-control study to investigate the effect of 4 MMP variants from the ADAMTS family on the pathogenesis of CAs. METHODS To identify susceptible genetic variants, the authors investigated 8 single nucleotide polymorphisms (SNPs) in 4 genes from the ADAMTS family (ADAMTS2, -7, -12, and -13) known to be associated with vascular diseases. The study included 353 patients with CAs and 1055 healthy adults. RESULTS The authors found significant associations between CA susceptibility and genetic variations in 3 members of the ADAMTS family. The largest risk for CA (OR 1.32, p = 0.006) was observed in carriers of the ADAMTS2 variant rs11750568, which has been previously associated with pediatric stroke. Three SNPs under investigation are associated with a protective effect in CA pathogenesis (ADAMTS12 variant rs1364044: OR 0.65, p = 0.0001; and ADAMTS13 variants rs739469 and rs4962153: OR 0.77 and 0.63, p = 0.02 and 0.0006, respectively), while 2 other ADAMTS13 variants may confer a significant risk (rs2301612: OR 1.26, p = 0.011; rs2285489: OR 1.24, p = 0.02). CONCLUSIONS These results suggest that reduced integrity of the endothelial wall, as conferred by ADAMTS variants, together with inflammatory processes and defective vascular remodeling plays an important role in CA pathogenesis, although the mechanism of action remains unknown. The authors' findings may lead to specific screening of at-risk populations in the future