Symptomatic mature teratoma of the lumbar spine: A case report

Background: Teratomas are a unique family of tumors derived from two or more of the three embryonic layers: endoderm, mesoderm, and ectoderm. Mature teratomas are comprised the most well-differentiated tissue types and may contain skin, hair, teeth, smooth muscle, respiratory tissues, etc. Infrequently, mature teratomas may be found within the central nervous system and, in exceedingly rare cases, may be occur within the spinal cord itself (i.e., intramedullary/intradural). Case Description: A 78-year-old female presented with a subacute progressive lower extremity paraparesis. The MR revealed a cystic 81 × 30 × 25 mm intradural/intramedullary spinal mass involving the distal conus with exophytic extension into the L1-L4 spinal canal. Following surgical intervention consisting of a L1-L4 laminectomy, the lesion was largely removed. Pathology of the mass confirmed a large mature teratoma containing a multilobulated cyst that intraoperatively compressed the conus and cauda equina. Immediately postoperatively, the patient significantly improved neurologically. However, on postoperative day 2, she acutely developed a change in mental status with the left gaze preference and hemiparesis. CT brain in the acute setting showed no evidence of causative pathology and subsequent MR brain was unremarkable. The patient’s neurologic deficits progressively improved leading to eventual discharge. Conclusion: Intrathecal intramedullary/extramedullary mature teratomas of the conus that results in subacute cauda equina syndromes are rare. The differential diagnosis for such lesions exophytic to the conus must include mature teratomas which, though rare, may be readily resected resulting in generally favorable outcomes.

Immune-mediated ophthalmoparesis with anti-GD1a antibodies

A young woman presented to neurology with a 1 month history of progressive diplopia on lateral gaze and a 1 week history of headaches. On examination she was found to have complex ophthalmoparesis with binocular horizontal diplopia, failure of abduction bilaterally and limited upgaze with convergence-retraction nystagmus. The rest of the neurological examination was normal. She was admitted for investigations: blood, CT brain, MR brain and lumbar puncture results were normal. Anti-GD1a antibodies were strongly positive; anti-GM1, anti-GM2 and anti-GD1b were also positive. On follow-up 3 weeks later, the complex ophthalmoplegia persisted. It was decided to treat with intravenous immunoglobulins (IVIgs) with good response but recurrence at 2 weeks post infusion. She was treated with 4 weekly IVIg courses and remains responsive and controlled over 1 year since presentation but becomes symptomatic in the week running up to each dose; thus, disease modifying treatment is currently being considered.

Classification of Low-Grade and High-Grade Glioma from MR Brain Images Using Multiple-Instance Learning with Combined Feature Set

Fully automatic brain image classification of MR brain images is of great importance for research and clinical studies, since the precise detection may lead to a better treatment. In this work, an efficient method based on Multiple-Instance Learning (MIL) is proposed for the automatic classification of low-grade and high-grade MR brain tumor images. The main advantage of MIL-based approach over other classification methods is that MIL considers an image as a group of instances rather than a single instance, thus facilitating an effective learning process. The mi-Graph-based MIL approach is proposed for this classification. Two different implementations of MIL-based classification, viz. Patch-based MIL (PBMIL) and Superpixel-based MIL (SPBMIL), are made in this study. The combined feature set of LBP, SIFT and FD is used for the classification. The accuracies of low-grade–high-grade tumor image classification algorithm using SPBMIL method performed on [Formula: see text], [Formula: see text] and FLAIR images read 99.2765%, 99.4195% and 99.2265%, respectively. The error rate of the proposed classification system was noted to be insignificant and hence this automated classification system could be used for the classification of images with different pathological conditions, types and disease statuses.

Classification of Alzheimer Condition using MR Brain Images and Inception-Residual Network Model

Alzheimer’s Disease (AD) is an irreversible progressive neurodegenerative disorder. Magnetic Resonance (MR) imaging based deep learning models with visualization capabilities are essential for the precise diagnosis of AD. In this study, an attempt has been made to categorize AD and Healthy Controls (HC) using structural MR images and an Inception-Residual Network (ResNet) model. For this, T1- weighted MR brain images are acquired from a public database. These images are pre-processed and are applied to a two-layer Inception-ResNet-A model. Additionally, Gradient weighted Class Activation Mapping (Grad-CAM) is employed to visualize the significant regions in MR images identified by the model for AD classification. The network performance is validated using standard evaluation metrics. Results demonstrate that the proposed Inception-ResNet model differentiates AD from HC using MR brain images. The model achieves an average recall and precision of 69%. The Grad- CAM visualization identified lateral ventricles in the mid-axial slice as the most discriminative brain regions for AD classification. Thus, the computer aided diagnosis study could be useful in the visualization and automated analysis of AD diagnosis with minimal medical expertise.

Brain tumor identification with a hybrid feature extraction method based on discrete wavelet transform and principle component analysis

The Identification of brain tumors is a critical step that relies on the expertise and abilities of the physician. In order to enable radiologists to spot brain tumors, an automated tumor arrangement is extremely important. This paper presents a technique for MR brain image segmentation and classification to identify images as normal and abnormal. The proposed technique is a hybrid feature extraction submitted to enhance the classification results and basically consists of three stages. The first stage is used a 3-level of discrete wavelet transform (DWT) to extract image characteristics. In the second stage, the principle component analysis (PCA) is applied to reduce the size of characteristics. Finally, a random forest classifier (RF) was used with a feature selection for identification. 181 MR brain images are collected (81 normal and 100 abnormal), in distinguishing normal and abnormal tissues, the experimental results obtained an accuracy of 98%, the sensitivity achieved is 99.2%, specificity achieved is 97.8%, and showed the effectiveness of the proposed technique compared with many kinds of literature. The results show that the 3L-DWT+PCA+RF still achieved the best classification results. The proposed model could apply to the brain MRI sphere classification, which will help doctors to diagnose a tumor if it is normal or abnormal in certain degrees.

Brain Tumor Detection and Classification on MR Images by a Deep Wavelet Auto-Encoder Model

The process of diagnosing brain tumors is very complicated for many reasons, including the brain’s synaptic structure, size, and shape. Machine learning techniques are employed to help doctors to detect brain tumor and support their decisions. In recent years, deep learning techniques have made a great achievement in medical image analysis. This paper proposed a deep wavelet autoencoder model named “DWAE model”, employed to divide input data slice as a tumor (abnormal) or no tumor (normal). This article used a high pass filter to show the heterogeneity of the MRI images and their integration with the input images. A high median filter was utilized to merge slices. We improved the output slices’ quality through highlight edges and smoothened input MR brain images. Then, we applied the seed growing method based on 4-connected since the thresholding cluster equal pixels with input MR data. The segmented MR image slices provide two two-layer using the proposed deep wavelet auto-encoder model. We then used 200 hidden units in the first layer and 400 hidden units in the second layer. The softmax layer testing and training are performed for the identification of the MR image normal and abnormal. The contribution of the deep wavelet auto-encoder model is in the analysis of pixel pattern of MR brain image and the ability to detect and classify the tumor with high accuracy, short time, and low loss validation. To train and test the overall performance of the proposed model, we utilized 2500 MR brain images from BRATS2012, BRATS2013, BRATS2014, BRATS2015, 2015 challenge, and ISLES, which consists of normal and abnormal images. The experiments results show that the proposed model achieved an accuracy of 99.3%, loss validation of 0.1, low FPR and FNR values. This result demonstrates that the proposed DWAE model can facilitate the automatic detection of brain tumors.

Cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy (CARASIL)

A 44-year-old Caucasian man presented with seizures and cognitive impairment. He had marked retinal drusen, and MR brain scan showed features of cerebral small vessel disease; he was diagnosed with a leukoencephalopathy of uncertain cause. He died at the age of 46 years and postmortem brain examination showed widespread small vessel changes described as a vasculopathy of unknown cause. Seven years postmortem, whole-genome sequencing identified a homozygous nonsense HTRA1 mutation (p.Arg302Ter), giving a retrospective diagnosis of cerebral autosomal recessive arteriopathy with subcortical infarcts and leukoencephalopathy.