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Transsphenoidal endoscopic surgery is the first-line treatment for growth hormone-secreting adenomas. To analyse the results of the transsphenoidal endoscopic approach for acromegaly and to determine the predictive factors of remission. A single-centre retrospective review was performed in patients who underwent endoscopic transsphenoidal surgery for acromegaly between January 2009 and January 2019. Demographic features, clinical presentation, histopathology records, complications and pre- and postoperative radiologic and endocrinological assessments were evaluated. The factors that influenced the remission rates were investigated. A total of 73 patients underwent surgery via the transsphenoidal endoscopic approach. Cavernous sinus invasion was detected in 32 patients (43.8%); and macroadenoma, in 57 (78%). The pathology specimens of the 27 patients (36.9%) showed dual-staining adenomas with prolactin. A total of 51 patients (69.8%) attained biochemical remission 1 year after surgery. A second operation was performed in 10 patients (13.6%) with residual tumours without biochemical remission in the first year. Six (60%) of the patients attained remission at the last follow-up. Transient diabetes insipidus was observed in 18 patients (24.6%); and rhinorrhoea, which was resolved with conservative treatment, in 4 (5.4%). None of the patients developed panhypopituitarism. The presence of cavernous sinus invasion and preoperative IGF-1, immediate postoperative GH and third-month IGF-1 levels were predictive of remission. Transsphenoidal endoscopic surgery is a safe and effective treatment for acromegaly. Reoperation should be considered in patients with residual tumours without remission. [ABSTRACT FROM AUTHOR]

Aim: To discuss seizure outcomes of patients with invasive electroencephalography (EEG) monitorization (IEM) following their epilepsy surgery at our centre., Material and Methods: Forty-seven patients suffering from refractory epilepsy and who were evaluated by invasive EEG were included in this retrospective study at Istanbul Faculty of Medicine from 2003 to 2017. We examined the Video EEG and invasive EEG monitorization, cranial MRI, SPECT, PET and neuropsychological tests of all patients. Postoperative seizure outcome results were evaluated according to Engel classification. The factors affecting seizure outcomes were discussed., Results: Twenty-six of the patients were female (55.3%), 21 were male (44.7). The average age was 32.0 (± 12.4). Forty-three patients had surgery and the average age of these patients was 26,6 (±11.15). 38.3% of the patients had hippocampal sclerosis (HS), 23.4% had focal cortical dysplasia (FCD), 8.5% had a tumor, 14.9% had sequela lesion and 14.9% had unknown etiology. Postoperative seizure status according to the Engel classification showed that 81.6% of the patients were class I, 10.5% were class II, 2.6% were class III and 5.3% were class IV., Conclusion: A significant relation was statistically determined between structural MRI lesion and favorable seizure outcome (p < 0.05). The most frequent etiology was HS in our patients. Of the patients with Engel I, the averages of their ages, ages at onset of epilepsy and ages at surgery were lower than other groups, but the difference was not statistically significant (p > 0.05). We argue that IEM is an essential examination for favorable outcomes for determining the epileptogenic zone and/or the proximity of the functional structures.

Aim: To present an early warning system (EWS) that employs a supervised machine learning algorithm for the rapid detection of extra-axial hematomas (EAHs) in an emergency trauma setting., Material and Methods: A total of 150 sets of cranial computed tomography (CT) scans were used in this study with a total of 11,025 images. Of the CTs, 75 were labeled as EAH, the remaining 75 were normal. A random forest algorithm was utilized for the detection of EAHs. The CTs were randomized into two groups: 100 samples for training of the algorithm (split evenly between EAH and normal cases), and 50 samples for testing. In the training phase, the algorithm scanned every CT slice separately for image features such as entropy, moment, and variance. If the algorithm determined an EAH on two or more images in a CT set, then the workflow produced an alert in the form of an email., Results: Data from 50 patients (25 EAH and 25 controls) were used for testing the EWS. For all CTs with an EAH, an alert was produced, with a 0% false-negative rate. For 16% of the cases, the practitioner received an email from the EWS that the patient might have an EAH despite having a normal CT scan. Positive and negative predictive values were 86% and 100%, respectively., Conclusion: An EWS based on a machine learning algorithm is an efficient and inexpensive way of facilitating the work of emergency practitioners such as emergency physicians, neuroradiologists, and neurosurgeons.