Pituitary Apoplexy After Leuprolide Therapy in a Breast Cancer Patient: A Case Report

Pituitary apoplexy (PA) is a clinical syndrome resulting from sudden hemorrhage and/or infarction of the pituitary gland. Recent reports documented the development of PA secondary to treatment with gonadotropin-releasing hormone (GnRH) agonists for prostate cancer. A 52-year-old woman visited our emergency room with a severe headache, occurred 1 day prior. She underwent breast-conserving surgery for breast cancer 1 month prior. She was currently undergoing radiation and hormone therapy, consisting of leuprorelin. Brain contrast-enhanced MRI revealed a pituitary adenoma with internal hemorrhage in the sellar and suprasellar areas. Pachymeningeal enhancement was observed along the retroclival and bilateral frontal areas. The patient was diagnosed with PA and aseptic meningitis. The patient underwent total excision via transsphenoidal surgery 8 days after admission. The patient was pathologically diagnosed with a pituitary adenoma with necrosis. On immunochemical staining, the tumor was positive for follicle-stimulating hormone. The follow-up MRI revealed no evidence of residual tumor or an improved pachymeningeal enhancement. She is currently undergoing follow-up at the neurosurgery and endocrinology outpatient departments with no noted complications. In breast cancer patients receiving GnRH agonist therapy, PA may be rare complication.

Brainstem Venous Infarction from Thrombosis on the Jugular Bulb as a Serious Complication of Blunt Head Trauma Injury: A Case Report / 대한신경손상학회지

The incidence of dural venous sinus thrombosis (DVST) and the cerebral venous infarct have not exactly known, but DVST is closely related to the skull fracture around the venous sinus.A 56-year-old man experienced massive watery discharge after hitting on his face by a falling machine. He was alert and no cerebrospinal fluid discharge on admission. Air-density was shown on the jugular fossa in the brain computed tomography. On the 3th day of trauma, he suddenly had dyspnea and loss of consciousness and became comatose. Acute edema on medulla, pons and right cerebellar hemisphere and focal infarct on right medulla were visualized on the brain magnetic resonance imaging. And the sigmoid sinus and the jugular vein were occluded and venous circulation on the right posterior fossa was diminished on the cerebral angiography. Air-density on the sinus may be an indicator into developing venous thrombosis and brainstem venous infarct could be followed by the DVST round the jugular bulb.

Fully Automatic Segmentation of Acute Ischemic Lesions on Diffusion-Weighted Imaging Using Convolutional Neural Networks: Comparison with Conventional Algorithms

OBJECTIVE: To develop algorithms using convolutional neural networks (CNNs) for automatic segmentation of acute ischemic lesions on diffusion-weighted imaging (DWI) and compare them with conventional algorithms, including a thresholding-based segmentation. MATERIALS AND METHODS: Between September 2005 and August 2015, 429 patients presenting with acute cerebral ischemia (training:validation:test set = 246:89:94) were retrospectively enrolled in this study, which was performed under Institutional Review Board approval. Ground truth segmentations for acute ischemic lesions on DWI were manually drawn under the consensus of two expert radiologists. CNN algorithms were developed using two-dimensional U-Net with squeeze-and-excitation blocks (U-Net) and a DenseNet with squeeze-and-excitation blocks (DenseNet) with squeeze-and-excitation operations for automatic segmentation of acute ischemic lesions on DWI. The CNN algorithms were compared with conventional algorithms based on DWI and the apparent diffusion coefficient (ADC) signal intensity. The performances of the algorithms were assessed using the Dice index with 5-fold cross-validation. The Dice indices were analyzed according to infarct volumes ( 100), time intervals to DWI, and DWI protocols. RESULTS: The CNN algorithms were significantly superior to conventional algorithms (p < 0.001). Dice indices for the CNN algorithms were 0.85 for U-Net and DenseNet and 0.86 for an ensemble of U-Net and DenseNet, while the indices were 0.58 for ADC-b1000 and b1000-ADC and 0.52 for the commercial ADC algorithm. The Dice indices for small and large lesions, respectively, were 0.81 and 0.88 with U-Net, 0.80 and 0.88 with DenseNet, and 0.82 and 0.89 with the ensemble of U-Net and DenseNet. The CNN algorithms showed significant differences in Dice indices according to infarct volumes (p < 0.001). CONCLUSION: The CNN algorithm for automatic segmentation of acute ischemic lesions on DWI achieved Dice indices greater than or equal to 0.85 and showed superior performance to conventional algorithms.