Myeloid Sarcoma in the Central Nervous System: Case Report and Review of the Literature

Mijeloidni sarkomi su rijetke pojavnosti uglavnom mijeloblastične leukemije. Njihova pojava u središnjem živčanom sustavu je iznimna, pa je dotična literatura danas ograničena na studije pojedinih slučajeva. Mi opisujemo jo. jedan slučaj, dok je pregled literature poslužio kako bismo ispitali značajke i mogućnosti liječenja mijeloidnog sarkoma središnjega živčanog sustva. U žene stare 61 godinu s akutnom mijeloblastičnom leukemijom (FAB M5) i progresivnom lijevostranom hemiparezom utvrđena je desnostrano parieto-okcipitalno epiduralno oštećenje koje je sličilo meningiomu. Učinjena je djelomična resekcija koja je otkrila mijeloidni sarkom. Pregledom literature utvrdili smo 44 slučaja s dostatnim opisom dijagnoze, liječenja i praćenja do jedne godine. Kod tih bolesnika primijenjeni su različiti načini liječenja. Međutim, bolesnici su imali najbolji postotak jednogodišnjeg preživljenja kad je protokol liječenja uključivao sustavnu kemoterapiju ili zračenje.Myeloid sarcomas are rare manifestations of mainly myeloblastic leukemia. Their occurrence in the central nervous system is exceptional and current literature is limited to case studies. A case is added herewith and a review was performed to investigate clinical characteristics and treatment options of central nervous system myeloid sarcoma. A 61-year-old female with acute myeloblastic leukemia (FAB M5) and progressive left sided hemiparesis showed a right parieto-occipital epidural lesion mimicking meningioma. Partial resection was performed to reveal a myeloid sarcoma. Reviewing the literature we identified 44 cases with sufficient description of the diagnosis, treatment and follow up to one year. In these patients different treatment regimens were applied. However, when systemic chemotherapy or irradiation was included in the treatment regimen, patients showed the best 1-year survival proportion

Magnetic resonance imaging biomarkers for clinical routine assessment of microvascular architecture in glioma

Knowledge about the topological and structural heterogeneity of the microvasculature is important for diagnosis and monitoring of glioma. A vessel caliber and type-dependent temporal shift in the magnetic resonance imaging signal forms the basis for vascular architecture mapping. This study introduced a clinically feasible approach for assessment of vascular pathologies in gliomas using vascular architecture mapping. Sixty consecutive patients with known or suspected gliomas were examined using vascular architecture mapping as part of the routine magnetic resonance imaging protocol. Maps of microvessel radius and density, which adapted to the vasculature-dependent temporal shift phenomenon, were calculated using a costume-made software tool. Microvessel radius and density were moderately to severely elevated in a heterogeneous, inversely correlated pattern within high-grade gliomas. Additionally, three new imaging biomarkers were introduced: Microvessel type indicator allowing differentiation between supplying arterial and draining venous microvasculature in high-grade gliomas. Vascular-induced bolus peak time shift may presumably be sensitive for early neovascularization in the infiltration zone. Surprisingly, curvature showed significant changes in peritumoral vasogenic edema which correlated with neovascularization in the tumor core of high-grade gliomas. These new magnetic resonance imaging biomarkers give insights into complexity and heterogeneity of vascular changes in glioma; however, histological validations in more well-defined patient populations are required

Night-shift work increases cold pain perception

Background: Although night-shift work (NSW) is associated with a higher risk for several physical and mental disorders, the impact of NSW on pain perception is still unclear. This study investigates the impact of NSW on cold pain perception considering the impact of mood and sleepiness.& para;& para;Method: Quantitative sensory testing (QST) was performed in healthy night-shift workers. Cold pain threshold as well as tonic cold pain was assessed after one habitual night (T1), after a 12-hour NSW (T2) and after one recovery night (T3). Sleep quality was measured with the Pittsburgh Sleep Quality Index (PSQI) before T1, sleepiness with the Stanford Sleepiness Scale (SSS) and mood with a German short-version of the Profile of Mood States (ASTS) at Tl, T2 and T3. Depending on the distribution of the data, ANOVAs or Friedman tests as well as t- or Wilcoxon tests were performed.& para;& para;Results: Nineteen healthy shift-workers (13 females; 29.7 +/- 7.5 years old; 8.1 +/- 6.6 years in shift work, PSQI: 4.7 +/- 2.2) were included. Tonic cold pain showed a significant difference between T1 (48.2 +/- 27.5 mm), T2 (61.7 +/- 26.6 mm; effect size: Cohen's d=.49; percent change 28%), and T3 (52.1 +/- 28.7 mm) on a 0-100 mm Visual Analog Scale (p = 0.007). Cold pain threshold changed from 11.0 +/- 7.9 degrees C (T1) to 14.5 +/- 8.8 degrees C (T2) (p = 0.04), however, an ANOVA comparing T1, T2, and T3 was not significant (p = 0.095). Sleepiness (SSS) and mood (ASTS) changed significantly between T1, T2 and T3 (p-values < 0.01). The change of mood but not of sleepiness correlated with the difference in tonic cold pain from T1 to T2 (R: 0.53; R-2 : 0.29; p = 0.022).& para;& para;Discussion: NSW increases cold pain perception. The same tonic cold pain stimulus is rated 28% more painful after NSW and normalizes after a recovery night. Increases in cold pain perception due to NSW appear to be more strongly related to changes in mood as compared to changes in sleepiness. (C) 2018 Elsevier B.V. All rights reserved

Differentiation of Glioblastoma and Brain Metastases by MRI-Based Oxygen Metabolomic Radiomics and Deep Learning

Glioblastoma (GB) and brain metastasis (BM) are the most frequent types of brain tumors in adults. Their therapeutic management is quite different and a quick and reliable initial characterization has a significant impact on clinical outcomes. However, the differentiation of GB and BM remains a major challenge in today&rsquo;s clinical neurooncology due to their very similar appearance in conventional magnetic resonance imaging (MRI). Novel metabolic neuroimaging has proven useful for improving diagnostic performance but requires artificial intelligence for implementation in clinical routines. Here; we investigated whether the combination of radiomic features from MR-based oxygen metabolism (&ldquo;oxygen metabolic radiomics&rdquo;) and deep convolutional neural networks (CNNs) can support reliably pre-therapeutic differentiation of GB and BM in a clinical setting. A self-developed one-dimensional CNN combined with radiomic features from the cerebral metabolic rate of oxygen (CMRO2) was clearly superior to human reading in all parameters for classification performance. The radiomic features for tissue oxygen saturation (mitoPO2; i.e., tissue hypoxia) also showed better diagnostic performance compared to the radiologists. Interestingly, both the mean and median values for quantitative CMRO2 and mitoPO2 values did not differ significantly between GB and BM. This demonstrates that the combination of radiomic features and DL algorithms is more efficient for class differentiation than the comparison of mean or median values. Oxygen metabolic radiomics and deep neural networks provide insights into brain tumor phenotype that may have important diagnostic implications and helpful in clinical routine diagnosis

Radiophysiomics: Brain Tumors Classification by Machine Learning and Physiological MRI Data

The precise initial characterization of contrast-enhancing brain tumors has significant consequences for clinical outcomes. Various novel neuroimaging methods have been developed to increase the specificity of conventional magnetic resonance imaging (cMRI) but also the increased complexity of data analysis. Artificial intelligence offers new options to manage this challenge in clinical settings. Here, we investigated whether multiclass machine learning (ML) algorithms applied to a high-dimensional panel of radiomic features from advanced MRI (advMRI) and physiological MRI (phyMRI; thus, radiophysiomics) could reliably classify contrast-enhancing brain tumors. The recently developed phyMRI technique enables the quantitative assessment of microvascular architecture, neovascularization, oxygen metabolism, and tissue hypoxia. A training cohort of 167 patients suffering from one of the five most common brain tumor entities (glioblastoma, anaplastic glioma, meningioma, primary CNS lymphoma, or brain metastasis), combined with nine common ML algorithms, was used to develop overall 135 classifiers. Multiclass classification performance was investigated using tenfold cross-validation and an independent test cohort. Adaptive boosting and random forest in combination with advMRI and phyMRI data were superior to human reading in accuracy (0.875 vs. 0.850), precision (0.862 vs. 0.798), F-score (0.774 vs. 0.740), AUROC (0.886 vs. 0.813), and classification error (5 vs. 6). The radiologists, however, showed a higher sensitivity (0.767 vs. 0.750) and specificity (0.925 vs. 0.902). We demonstrated that ML-based radiophysiomics could be helpful in the clinical routine diagnosis of contrast-enhancing brain tumors; however, a high expenditure of time and work for data preprocessing requires the inclusion of deep neural networks