Spinal tuberculoma in a patient with spinal myxopapillary ependymoma

Intramedullary spinal tuberculosis is a clinical curiosity. A 19-year-old female was diagnosed and treated for lumbosacral myxopapllary ependy moma (MPE). Three years later, she presented with back pain and hypoesthesia of the left upper limb. Besides revealing local recurrence, the MRI demonstrated a fresh lesion in the cervicomedullary area. The latter was operated and the histopathology revealed a tuberculoma

Dopamine induces functional extracellular traps in microglia

Dopamine (DA) plays many roles in the brain, especially in movement, motivation, and reinforcement of behavior; however, its role in regulating innate immunity is not clear. Here, we show that DA can induce DNA-based extracellular traps in primary, adult, human microglia and BV2 microglia cell line. These DNA-based extracellular traps are formed independent of reactive oxygen species, actin polymerization, and cell death. These traps are functional and capture fluorescein (FITC)-tagged Escherichia coli even when reactive oxygen species production or actin polymerization is inhibited. We show that microglial extracellular traps are present in Glioblastoma multiforme. This is crucial because Glioblastoma multiforme cells are known to secrete DA. Our findings demonstrate that DA plays a significant role in sterile neuro-inflammation by inducing microglia extracellular traps

Machine-Learning-Based Radiomics for Classifying Glioma Grade from Magnetic Resonance Images of the Brain

Grading of gliomas is a piece of critical information related to prognosis and survival. Classifying glioma grade by semantic radiological features is subjective, requires multiple MRI sequences, is quite complex and clinically demanding, and can very often result in erroneous radiological diagnosis. We used a radiomics approach with machine learning classifiers to determine the grade of gliomas. Eighty-three patients with histopathologically proven gliomas underwent MRI of the brain. Whenever available, immunohistochemistry was additionally used to augment the histopathological diagnosis. Segmentation was performed manually on the T2W MR sequence using the TexRad texture analysis softwareTM, Version 3.10. Forty-two radiomics features, which included first-order features and shape features, were derived and compared between high-grade and low-grade gliomas. Features were selected by recursive feature elimination using a random forest algorithm method. The classification performance of the models was measured using accuracy, precision, recall, f1 score, and area under the curve (AUC) of the receiver operating characteristic curve. A 10-fold cross-validation was adopted to separate the training and the test data. The selected features were used to build five classifier models: support vector machine, random forest, gradient boost, naive Bayes, and AdaBoost classifiers. The random forest model performed the best, achieving an AUC of 0.81, an accuracy of 0.83, f1 score of 0.88, a recall of 0.93, and a precision of 0.85 for the test cohort. The results suggest that machine-learning-based radiomics features extracted from multiparametric MRI images can provide a non-invasive method for predicting glioma grades preoperatively. In the present study, we extracted the radiomics features from a single cross-sectional image of the T2W MRI sequence and utilized these features to build a fairly robust model to classify low-grade gliomas from high-grade gliomas (grade 4 gliomas)

Deep learning based clinico-radiological model for paediatric brain tumor detection and subtype prediction

Aim: Early diagnosis of paediatric brain tumors significantly improves the outcome. The aim is to study magnetic resonance imaging (MRI) features of paediatric brain tumors and to develop an automated segmentation (AS) tool which could segment and classify tumors using deep learning methods and compare with radiologist assessment. Methods: This study included 94 cases, of which 75 were diagnosed cases of ependymoma, medulloblastoma, brainstem glioma, and pilocytic astrocytoma and 19 were normal MRI brain cases. The data was randomized into training data, 64 cases; test data, 21 cases and validation data, 9 cases to devise a deep learning algorithm to segment the paediatric brain tumor. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy of the deep learning model were compared with radiologist’s findings. Performance evaluation of AS was done based on Dice score and Hausdorff95 distance. Results: Analysis of MRI semantic features was done with necrosis and haemorrhage as predicting features for ependymoma, diffusion restriction and cystic changes were predictors for medulloblastoma. The accuracy of detecting abnormalities was 90%, with a specificity of 100%. Further segmentation of the tumor into enhancing and non-enhancing components was done. The segmentation results for whole tumor (WT), enhancing tumor (ET), and non-enhancing tumor (NET) have been analyzed by Dice score and Hausdorff95 distance. The accuracy of prediction of all MRI features was compared with experienced radiologist’s findings. Substantial agreement observed between the classification by model and the radiologist’s given classification [K-0.695 (K is Cohen’s kappa score for interrater reliability)]. Conclusions: The deep learning model had very high accuracy and specificity for predicting the magnetic resonance (MR) characteristics and close to 80% accuracy in predicting tumor type. This model can serve as a potential tool to make a timely and accurate diagnosis for radiologists not trained in neuroradiology

Anaplastic lymphoma kinase-positive pulmonary inflammatory myofibroblastic tumor with sarcomatous morphology and distant metastases: An unusual histomorphology and behavior

Inflammatory myofibroblastic tumor (IMT), an intermediate-grade neoplasm of myofibroblastic/fibroblastic differentiation, occurs commonly in children and young adults. It is characterized by anaplastic lymphoma kinase (ALK) gene rearrangement and overexpression of ALK-protein. However, aggressive behavior is more commonly associated with ALK-negativity rather than ALK-positivity. Pulmonary involvement is most common visceral location and carries minimal potential for distant metastasis. We present a case of 49-year-old female with pulmonary IMT of spindle cell sarcomatous histomorphology. Frequent mitoses and necrosis with characteristic cytoplasmic immunoreactivity for ALK-1 protein and ALK-gene rearrangement on fluorescence in-situ hybridization were noted. This case is unusual for occurrence in higher age-group of fifth decade, sarcomatous histomorphology at presentation (rather than transformation) and metastases to distant sites despite ALK-protein overexpression and gene rearrangement

Posterior fossa involvement in a recurrent gliosarcoma

Gliosarcoma (GSM) is a WHO grade 4 tumor and a variant of glioblastoma multiforme with predilection for the temporal lobe. We record, perhaps the first case in literature, of a temporal lobe GSM with recurrence involving the posterior fossa. A 50-year-old man presented to us with headache, vomiting, and lethargy of relatively recent onset. Magnetic resonance imaging revealed a well-circumscribed lesion in the left temporal lobe for which left temporal craniotomy with radical excision of the tumor was performed. Histopathology was suggestive of GSM. He presented to us within a month of the first surgery with a large recurrence involving the temporal lobe. He underwent a second surgery with radical excision of the tumor. Histopathology was confirmatory of GSM. He was administered concomitant chemotherapy and radiotherapy. Within a fortnight of starting adjuvant therapy, the bone flap started bulging and a repeat computed tomography scan revealed a large recurrence extending into the posterior fossa. The patient′s relatives refused consent for third surgery and he finally succumbed on postoperative day 21. GSMs are aggressive tumors that have a temporal lobe predilection, but they may present anywhere in the brain. Detailed studies on larger cohort of cases are needed to understand the true nature of these biphasic tumors

Case Report of Diffuse Large B Cell Lymphoma of Uterine Cervix Treated at a Semiurban Cancer Centre in North India

Lymphoma of the uterine cervix is very rare. We report a case of diffuse large B cell lymphoma (DLBCL) involving the uterine cervix treated at a newly commissioned semiurban cancer centre in north India in 2015. Data for this study was obtained from the hospital electronic medical records and the patient’s case file. We also reviewed published case reports of uterine and cervical lymphoma involving forty-one patients. We treated a case of stage IV DLBCL cervix with six cycles of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisolone) and intrathecal methotrexate followed by consolidation with radiotherapy. The patient showed complete response to chemotherapy. We conclude that, in advanced stage lymphoma involving uterus and cervix, combination of chemotherapy and radiotherapy is effective in short term