Chronic lymphocytic leukemia and the central nervous system: a clinical and pathological study.

Chronic lymphocytic leukemia is the most common human leukemia but infrequently causes neurologic symptoms. We have reviewed all previously reported cases of chronic lymphocytic leukemia in the CNS along with three new cases; one patient was diagnosed antemortem and treated with immediate improvement and 4-year survival. In addition, we reviewed all autopsy cases since 1972 and available lumbar puncture data on patients with chronic lymphocytic leukemia admitted to the Massachusetts General Hospital. Invasion of the CNS by chronic lymphocytic leukemia often leads to confusional state, meningitis with cranial nerve abnormalities, optic neuropathy, or cerebellar dysfunction. Lumbar puncture shows a lymphocytosis consisting of monoclonal B cells, but CSF cytology studies are of limited value in establishing the diagnosis. Long-term survival may be related to the stage of chronic lymphocytic leukemia at the time of CNS disease and may be associated with intrathecal chemotherapy. A mild, asymptomatic infiltration of the brain, frequently noted in late-stage chronic lymphocytic leukemia in autopsy series, may explain the CSF lymphocytosis in some patients with late-stage chronic lymphocytic leukemia

Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

Deep Learning versus Classical Regression for Brain Tumor Patient Survival Prediction

Deep learning for regression tasks on medical imaging data has shown promising results. However, compared to other approaches, their power is strongly linked to the dataset size. In this study, we evaluate 3D-convolutional neural networks (CNNs) and classical regression methods with hand-crafted features for survival time regression of patients with high grade brain tumors. The tested CNNs for regression showed promising but unstable results. The best performing deep learning approach reached an accuracy of 51.5% on held-out samples of the training set. All tested deep learning experiments were outperformed by a Support Vector Classifier (SVC) using 30 radiomic features. The investigated features included intensity, shape, location and deep features. The submitted method to the BraTS 2018 survival prediction challenge is an ensemble of SVCs, which reached a cross-validated accuracy of 72.2% on the BraTS 2018 training set, 57.1% on the validation set, and 42.9% on the testing set. The results suggest that more training data is necessary for a stable performance of a CNN model for direct regression from magnetic resonance images, and that non-imaging clinical patient information is crucial along with imaging information.Comment: Contribution to The International Multimodal Brain Tumor Segmentation (BraTS) Challenge 2018, survival prediction tas

Natural History of Meningioma Development in Mice Reveals: A Synergy of Nf2 and p16Ink4a Mutations

Meningiomas account for approximately 30% of all primary central nervous system tumors and are found in half of neurofibromatosis type 2 patients often causing significant morbidity. Although most meningiomas are benign, 10% are classified as atypical or anaplastic, displaying aggressive clinical behavior. Biallelic inactivation of the neurofibromatosis 2 (NF2) tumor suppressor is associated with meningioma formation in all NF2 patients and 60% of sporadic meningiomas. Deletion of the p16INK4a/p14ARF locus is found in both benign and malignant meningiomas, while mutation of the p53 tumor suppressor gene is uncommon. Previously, we inactivated Nf2 in homozygous conditional knockout mice by adenoviral Cre delivery and showed that Nf2 loss in arachnoid cells is rate-limiting for meningioma formation. Here, we report that additional nullizygosity for p16Ink4a increases the frequency of meningioma and meningothelial proliferation in these mice without modifying the tumor grade. In addition, by using magnetic resonance imaging (MRI) to screen a large cohort of mutant mice, we were able to detect meningothelial proliferation and meningioma development opening the way to future studies in which therapeutic interventions can be tested as preclinical assessment of their potential clinical application

The critical role of ERK in death resistance and invasiveness of hypoxia-selected glioblastoma cells

<p>Abstract</p> <p>Background</p> <p>The rapid growth of tumor parenchyma leads to chronic hypoxia that can result in the selection of cancer cells with a more aggressive behavior and death-resistant potential to survive and proliferate. Thus, identifying the key molecules and molecular mechanisms responsible for the phenotypic changes associated with chronic hypoxia has valuable implications for the development of a therapeutic modality. The aim of this study was to identify the molecular basis of the phenotypic changes triggered by chronic repeated hypoxia.</p> <p>Methods</p> <p>Hypoxia-resistant T98G (HRT98G) cells were selected by repeated exposure to hypoxia and reoxygenation. Cell death rate was determined by the trypan blue exclusion method and protein expression levels were examined by western blot analysis. The invasive phenotype of the tumor cells was determined by the Matrigel invasion assay. Immunohistochemistry was performed to analyze the expression of proteins in the brain tumor samples. The Student T-test and Pearson Chi-Square test was used for statistical analyses.</p> <p>Results</p> <p>We demonstrate that chronic repeated hypoxic exposures cause T98G cells to survive low oxygen tension. As compared with parent cells, hypoxia-selected T98G cells not only express higher levels of anti-apoptotic proteins such as Bcl-2, Bcl-X_L, and phosphorylated ERK, but they also have a more invasive potential in Matrigel invasion chambers. Activation or suppression of ERK pathways with a specific activator or inhibitor, respectively, indicates that ERK is a key molecule responsible for death resistance under hypoxic conditions and a more invasive phenotype. Finally, we show that the activation of ERK is more prominent in malignant glioblastomas exposed to hypoxia than in low grade astrocytic glial tumors.</p> <p>Conclusion</p> <p>Our study suggests that activation of ERK plays a pivotal role in death resistance under chronic hypoxia and phenotypic changes related to the invasive phenotype of HRT98G cells compared to parent cells.</p

MicroRNA expression profiles in pediatric dysembryoplastic neuroepithelial tumors.

© Springer Science+Business Media New York 2015Among noncoding RNAs, microRNAs (miRNAs) have been most extensively studied, and their biology has repeatedly been proven critical for central nervous system pathological conditions. The diagnostic value of several miRNAs was appraised in pediatric dysembryoplastic neuroepithelial tumors (DNETs) using miRNA microarrays and receiving operating characteristic curves analyses. Overall, five pediatric DNETs were studied. As controls, 17 samples were used: the FirstChoice Human Brain Reference RNA and 16 samples from deceased children who underwent autopsy and were not present with any brain malignancy. The miRNA extraction was carried out using the mirVANA miRNA Isolation Kit, while the experimental approach included miRNA microarrays covering 1211 miRNAs. Quantitative real-time polymerase chain reaction was performed to validate the expression profiles of miR-1909* and miR-3138 in all samples initially screened with miRNA microarrays. Our findings indicated that miR-3138 might act as a tumor suppressor gene when down-regulated and miR-1909* as a putative oncogenic molecule when up-regulated in pediatric DNETs compared to the control cohort. Subsequently, both miRNA signatures might serve as putative diagnostic biomarkers for pediatric DNETs.Peer reviewedFinal Accepted Versio

Texture analysis-and support vector machine-assisted diffusional kurtosis imaging may allow in vivo gliomas grading and IDH-mutation status prediction:a preliminary study

We sought to investigate, whether texture analysis of diffusional kurtosis imaging (DKI) enhanced by support vector machine (SVM) analysis may provide biomarkers for gliomas staging and detection of the IDH mutation. First-order statistics and texture feature extraction were performed in 37 patients on both conventional (FLAIR) and mean diffusional kurtosis (MDK) images and recursive feature elimination (RFE) methodology based on SVM was employed to select the most discriminative diagnostic biomarkers. The first-order statistics demonstrated significantly lower MDK values in the IDH-mutant tumors. This resulted in 81.1% accuracy (sensitivity = 0.96, specificity = 0.45, AUC 0.59) for IDH mutation diagnosis. There were non-significant differences in average MDK and skewness among the different tumour grades. When texture analysis and SVM were utilized, the grading accuracy achieved by DKI biomarkers was 78.1% (sensitivity 0.77, specificity 0.79, AUC 0.79); the prediction accuracy for IDH mutation reached 83.8% (sensitivity 0.96, specificity 0.55, AUC 0.87). For the IDH mutation task, DKI outperformed significantly the FLAIR imaging. When using selected biomarkers after RFE, the prediction accuracy achieved 83.8% (sensitivity 0.92, specificity 0.64, AUC 0.88). These findings demonstrate the superiority of DKI enhanced by texture analysis and SVM, compared to conventional imaging, for gliomas staging and prediction of IDH mutational status