A rare case of thoracic lipomyelomeningocele in a young female: A case report

Thoracic lipomyelomeningocele is a rare type of congenital occult spinal dysraphism. It is characterized by lipomatous tissue connected to the dorsal spinal cord that protrudes through a spinal defect together with the meninges or spinal cord to form a posterior mass beneath the skin. Closed spinal dysraphism can present diagnostic challenges when resources are scarce and advanced imaging techniques like magnetic resonance imaging are not readily available. Here, we describe a case of thoracic lipomyelomeningocele, a type of closed spinal dysraphism in a young female presenting with gradually progressive weakness and tingling sensation in bilateral lower limbs over the last 6 months. On physical examination, she had a soft tissue swelling with dimpling over the dorsal spine and paraparesis. Magnetic resonance imaging of the spine revealed dorsal lipomyelomeningocele corresponding to D4-D7 vertebral levels with tethered spinal cord.

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome mimicking herpes simplex encephalitis: A case report

Mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome presents with the features of herpes simplex encephalitis (HSE), which is rare and has been described in only a few case reports. Our case describes a 17-year-old female with no significant previous medical history presenting with an acute onset of fever, headache, and epilepsy, similar to HSE. Computed tomography of the brain showed bilateral basal ganglia calcification. Magnetic resonance imaging demonstrated gyriform restricted diffusion with T2-weighted images prolongation. Further investigation showed elevated blood lactate concentration at rest. Hence, MELAS was suspected and the diagnosis was confirmed by the presence of a nucleotide 3243 A→G mutation in the mitochondrial DNA. The clinical presentation and imaging studies of MELAS are variable and may mimic those of HSE. Infection may have also precipitated MELAS manifestation in this patient. Laboratory features, such as elevated lactate, basal ganglia calcification, and gyriform restricted diffusion may be helpful in identifying patients with MELAS.

Transient Antidiuretic Hormone Insufficiency Caused by Severe Hyperosmolar Hyperglycemic Syndrome Based on Nephrogenic Diabetes Insipidus

BACKGROUND

The hyperosmolar hyperglycemic state (HHS), an acute complication of diabetes mellitus with plasma hyperosmolarity, promotes the secretion of anti-diuretic hormone (ADH) and reduces the storage of ADH. Magnetic resonance T1-weighted imaging reflects ADH storage in the posterior pituitary lobe, which disappears when the storage is depleted. Whether the HHS induces ADH depletion leading to clinical manifestations has been unclear.

CASE REPORT

A 55-year-old Japanese woman was admitted to our center because of mental disturbance and hypotension. She had received lithium carbonate for bipolar disorder and presented with polydipsia and polyuria from 15 years of age. On admission, she had mental disturbance (Glasgow Coma Scale, E4V1M1), hypotension (systolic blood pressure, 50 mmHg), and tachycardia (pulse rate, 123/min). Plasma glucose was 697 mg/dL osmolality was 476 mOsm/kg•H2O, and bicarbonate was 23.7 mmol/L. The diagnoses of HHS and hypovolemic shock were made. During treatment with fluid replacement and insulin therapy, the urine volume continued to be approximately 3 to 4 L/day, and an endocrine examination revealed ADH insufficiency and nephrogenic diabetes insipidus. Desmopressin 10 μg/day and trichlormethiazide 2 mg/day were necessary and administered, and the endogenous ADH secretion improved gradually. The signal intensity of the pituitary posterior lobe, initially decreased on magnetic resonance T1 images, was also improved.

CONCLUSION

This patient had ADH insufficiency associated with ADH depletion due to hyperosmolarity and nephrogenic diabetes insipidus. Clinicians should be aware of the risk of the development of critical HHS and relative ADH insufficiency in patients being treated with lithium carbonate.

Spinal arteriovenous malformations causing Foix-Alajouanine syndrome, a case report and review of the literature

Foix-Alajouanine syndrome is a rare progressive form of spinal AVM predominantly affecting the lower thoracic and/or lumbosacral regions. This study aims to describe the imaging findings of spinal AVM causing Foix-Alajouanine syndrome and to review the literature. We present a 48-year-old man with progressive back pain, leg weakness, and gait imbalance without urinary retention. We discuss the clinical and imaging findings and the significance of MRI in establishing the diagnosis. A definitive diagnosis of spinal AVM requires radiographic demonstration of the vascular anomaly. Despite the high sensitivity of angiography for the diagnosis of spinal AVM, the result of the study may be inconclusive and/or negative. The key MRI findings are the presence of abnormally dilated perimedullary vessels with signal voids from a high-velocity flow on T1 and T2 weighted images.

Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading

Gliomas are the most common intra-axial primary brain tumour; therefore, predicting glioma grade would influence therapeutic strategies. Although several methods based on single or multiple parameters from diagnostic images exist, a definitive method for pre-operatively determining glioma grade remains unknown. We aimed to develop an unsupervised method using multiple parameters from pre-operative diffusion tensor images for obtaining a clustered image that could enable visual grading of gliomas. Fourteen patients with low-grade gliomas and 19 with high-grade gliomas underwent diffusion tensor imaging and three-dimensional T1-weighted magnetic resonance imaging before tumour resection. Seven features including diffusion-weighted imaging, fractional anisotropy, first eigenvalue, second eigenvalue, third eigenvalue, mean diffusivity and raw T2 signal with no diffusion weighting, were extracted as multiple parameters from diffusion tensor imaging. We developed a two-level clustering approach for a self-organizing map followed by the K-means algorithm to enable unsupervised clustering of a large number of input vectors with the seven features for the whole brain. The vectors were grouped by the self-organizing map as protoclusters, which were classified into the smaller number of clusters by K-means to make a voxel-based diffusion tensor-based clustered image. Furthermore, we also determined if the diffusion tensor-based clustered image was really helpful for predicting pre-operative glioma grade in a supervised manner. The ratio of each class in the diffusion tensor-based clustered images was calculated from the regions of interest manually traced on the diffusion tensor imaging space, and the common logarithmic ratio scales were calculated. We then applied support vector machine as a classifier for distinguishing between low- and high-grade gliomas. Consequently, the sensitivity, specificity, accuracy and area under the curve of receiver operating characteristic curves from the 16-class diffusion tensor-based clustered images that showed the best performance for differentiating high- and low-grade gliomas were 0.848, 0.745, 0.804 and 0.912, respectively. Furthermore, the log-ratio value of each class of the 16-class diffusion tensor-based clustered images was compared between low- and high-grade gliomas, and the log-ratio values of classes 14, 15 and 16 in the high-grade gliomas were significantly higher than those in the low-grade gliomas (p < 0.005, p < 0.001 and p < 0.001, respectively). These classes comprised different patterns of the seven diffusion tensor imaging-based parameters. The results suggest that the multiple diffusion tensor imaging-based parameters from the voxel-based diffusion tensor-based clustered images can help differentiate between low- and high-grade gliomas.

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We have developed a novel unsupervised method for voxel-based clustered imaging.

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Each class ratio in clustered images differentiated high from low-grade gliomas.

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The 16-class clustered images showed the best performance for the differentiation.

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Each class comprised different patterns of the seven diffusion tensor-based features.

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Multiple parameters from diffusion tensor images are useful for glioma grading.