Comparing neuroendocrine recovery between surgical and conservative management in pituitary apoplexy patients: a propensity score-matched analysis.

Pituitary apoplexy is a rare and potentially life-threatening clinical syndrome. Patients may present with severeneuro-ophthalmologic or endocrine symptoms. Current evidence is unclear whether conservative or surgicalmanagement leads to the best neuroendocrine outcomes. This study aimed to compare neuroendocrine outcomesbetween surgical and conservative treatments in a single center. Cases of patients with pituitary apoplexy whoreceived transsphenoidal surgery or conservative management in Songklanagarind Hospital between January 1,2005 and December 31, 2022 were retrospectively reviewed. A propensity score matching method was used toadjust bias from treatment selection (surgery or conservative treatment). Differences in visual field, visual acuity,cranial nerve, and endocrine outcomes between the surgical and conservative treatment groups were analyzedusing logistic regression analysis. This study included 127 patients, with 98 and 29 patients in the surgical and theconservative treatment group, respectively. The optimal matching method was used for propensity score matching.Compared to the conservative group, the surgically treated patients had a significantly higher rate of visual fieldrecovery (odds ratio (OR): 12.89, P = 0.007). However, there were no statistical differences in the recovery rate ofpreoperative visual acuity, cranial nerve, and endocrine deficits between the groups. Transsphenoidal surgery wasassociated with a higher rate of visual field recovery when compared to the conservative treatment for pituitaryapoplexy patients. Careful selection of appropriate treatment based on the patient's presentation andneuroendocrine status will result in the best outcomes while avoiding unnecessary surgical intervention.

Economic impact of a machine learning-based strategy for preparation of blood products in brain tumor surgery.

BACKGROUND: Globally, blood donation has been disturbed due to the pandemic. Consequently, the optimization of preoperative blood preparation should be a point of concern. Machine learning (ML) is one of the modern approaches that have been applied by physicians to help decision-making. The main objective of this study was to identify the cost differences of the ML-based strategy compared with other strategies in preoperative blood products preparation. A secondary objective was to compare the effectiveness indexes of blood products preparation among strategies. METHODS: The study utilized a retrospective cohort design conducted on brain tumor patients who had undergone surgery between January 2014 and December 2021. Overall data were divided into two cohorts. The first cohort was used for the development and deployment of the ML-based web application, while validation, comparison of the effectiveness indexes, and economic evaluation were performed using the second cohort. Therefore, the effectiveness indexes of blood preparation and cost difference were compared among the ML-based strategy, clinical trial-based strategy, and routine-based strategy. RESULTS: Over a 2-year period, the crossmatch to transfusion (C/T) ratio, transfusion probability (Tp), and transfusion index (Ti) of the ML-based strategy were 1.10, 57.0%, and 1.62, respectively, while the routine-based strategy had a C/T ratio of 4.67%, Tp of 27.9%%, and Ti of 0.79. The overall costs of blood products preparation among the ML-based strategy, clinical trial-based strategy, and routine-based strategy were 30, 061.56$, 57,313.92$, and 136,292.94$, respectively. From the cost difference between the ML-based strategy and routine-based strategy, we observed cost savings of 92,519.97$ (67.88%) for the 2-year period. CONCLUSION: The ML-based strategy is one of the most effective strategies to balance the unnecessary workloads at blood banks and reduce the cost of unnecessary blood products preparation from low C/T ratio as well as high Tp and Ti. Further studies should be performed to confirm the generalizability and applicability of the ML-based strategy.

Development of a nomogram to predict the outcome of moderate or severe pediatric traumatic brain injury.

OBJECTIVES: Traumatic brain injury (TBI) in children has become the major cause of mortality and morbidity in Thailand that has had an impact with economic consequences. This study aimed to develop and internally validate a nomogram for a 6-month follow-up outcome prediction in moderate or severe pediatric TBI. METHODS: This retrospective cohort study involved 104 children with moderate or severe TBI. Various clinical variables were reviewed. The functional outcome was assessed at the hospital discharge and at a 6-month follow-up based on the King's Outcome Scale for Childhood Head Injury classification. Predictors associated with the 6-month follow-up outcome were developed from the predictive model using multivariable binary logistic regression to estimate the performance and internal validation. A nomogram was developed and presented as a predictive model. RESULTS: The mean age of the samples was 99.75 months (standard deviation 59.65). Road traffic accidents were the highest injury mechanism at 84.6%. The predictive model comprised Glasgow Coma Scale of 3-8 (odds ratio [OR]: 16.07; 95% confidence interval [CI]: 1.27-202.42), pupillary response in one eye (OR 7.74; 95% CI 1.26-47.29), pupillary nonresponse in both eyes (OR: 57.74; 95% CI: 2.28-145.81), hypotension (OR: 19.54; 95%: CI 3.23-117.96), and subarachnoid hemorrhage (OR: 9.01, 95% CI: 1.33-60.80). The concordance statistic index (C-index) of the model's discrimination was 0.931, while the C-index following the bootstrapping and 5-cross validation were 0.920 and 0.924, respectively. CONCLUSIONS: The performance of a clinical nomogram for predicting 6-month follow-up outcomes in pediatric TBI patients was assessed at an excellent level. However, further external validation would be required for the confirmation of the tool's performance.

Factors Predicting Neuroendocrine Recovery Following Transsphenoidal Surgery in Pituitary Apoplexy Patients.

BACKGROUND: Pituitary apoplexy is a rare clinical syndrome. Only a few studies have examined factors associated with recovery of neuroendocrine functions following transsphenoidal surgery. This study aimed to identify factors associated with neuroendocrine recovery following surgery for pituitary apoplexy. METHODS: The records of pituitary apoplexy patients who underwent transsphenoidal surgery at Songklanagarind Hospital between January 2005 and December 2020 were retrospectively reviewed. The primary outcomes were the recoveries of preoperative visual acuity (VA), visual field, cranial nerve function, and pituitary hormone deficits. Using logistic regression analysis, various factors were analyzed for their associations with recovery of neuroendocrine functions. RESULTS: The study included 98 patients. Multivariate analysis showed that older age and greater suprasellar extension were associated with no recovery of VA (P = 0.042 and P = 0.018, respectively). Only 33% of patients aged >55 years and suprasellar extension of lesion ≥25 mm gained recovery of VA, while 100% of patients with neither of these factors had recovery. Underlying hypertension was associated with no recovery of preoperative visual field defect (P = 0.027). Wilson-Hardy classification-invasion grades 3-4 and lower preoperative serum prolactin level were associated with no recovery from preoperative hypoadrenalism and hypothyroidism (P = 0.016 and P = 0.007, respectively). CONCLUSIONS: Older age, higher suprasellar extension, and hypertension were poor prognostic factors for visual recovery. Wilson-Hardy inferior invasion grade 3-4 and lower preoperative serum prolactin level were poor prognostic factors for recovery from hypopituitarism. Stratifying patients according to these prognostic factors may assist in selecting patients for surgery.

Quality of life, out-of-pocket expenditures, and indirect costs among patients with the central nervous system tumors in Thailand.

Objectives: The aim of this study was to investigate out-of-pocket (OOP) expenditures, indirect costs, and health-related quality of life (HRQoL) associated with the central nervous system (CNS) tumors in Thailand. Materials and Methods: A prospective study of CNS tumor patients who underwent first tumor resection at a tertiary care institution in Thailand was conducted. Patients were interviewed during hospitalization for undergoing first surgery. Within 6 months, they were interviewed once more if the disease continued to progress. Costs collected from a patient perspective and converted to 2019 US dollars. For dealing with these skewed data, a generalized linear model was used to investigate the effects of disease severity (malignancy, progressive disease, Karnofsky performance status score, and histology) and other factors on costs (OOP, informal care, productivity loss, and total costs). P < 0.05 was considered statistical significant for all analysis. Results: Among a total of 123 intracranial CNS tumor patients, there were 83 and 40 patients classified into benign and malignant, respectively. In the first brain surgery, there was no statistical difference in HRQoL between patients with benign and malignant tumors (P = 0.072). However, patients with progressive disease had lower HRQoL mean scores at pre-operative and progressive disease periods were 0.711 (95% confidence interval [CI]: 0.662-0.760) and 0.261 (95% CI: 0.144-0.378), respectively. Indirect expenditures were the primary cost driver, accounting for 73.81% of annual total costs. The total annual costs accounted for 59.81% of the reported patient's income in malignant tumor patients. The progressive disease was the only factor that was significantly increases in all sorts of costs, including the OOP (P = 0.001), the indirect costs (P = 0.013), and the total annual costs (P = 0.001). Conclusion: Although there was no statistical difference in HRQoL and costs between patients with benign and malignant tumor, the total costs accounted for more than half of the reported income in malignant tumor patients. The primary cause of significant increases in all costs categories was disease progression.

Prognostic factors and clinical nomogram predicting survival in high-grade glioma.

BACKGROUND: Genomic-based tools have been used to predict poor prognosis high-grade glioma (HGG). As genetic technologies are not generally available in countries with limited resources, clinical parameters may be still necessary to use in predicting the prognosis of the disease. This study aimed to identify prognostic factors associated with survival of patients with HGG. We also proposed a validated nomogram using clinical parameters to predict the survival of patients with HGG. METHODS: A multicenter retrospective study was conducted in patients who were diagnosed with anaplastic astrocytoma (WHO III) or glioblastoma (WHO IV). Collected data included clinical characteristics, neuroimaging findings, treatment, and outcomes. Prognostic factor analysis was conducted using Cox proportional hazard regression analysis. Then, we used the significant prognostic factors to develop a nomogram. A split validation of nomogram was performed. Twenty percent of the dataset was used to test the performance of the developed nomogram. RESULTS: Data from 171 patients with HGG were analyzed. Overall median survival was 12 months (interquartile range: 5). Significant independent predictors included frontal HGG (hazard ratio [HR]: 0.62; 95% confidence interval [CI]: 0.40-0.60), cerebellar HGG (HR: 4.67; 95% CI: 0.93-23.5), (HR: 1.55; 95% CI: 1.03-2.32; reference = total resection), and postoperative radiotherapy (HR: 0.18; 95% CI: 0.10-0.32). The proposed nomogram was validated using nomogram's predicted 1-year mortality rate. Sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve of our nomogram were 1.0, 0.50, 0.45, 1.0, 0.64, and 0.75, respectively. CONCLUSION: We developed a nomogram for individually predicting the prognosis of HGG. This nomogram had acceptable performances with high sensitivity for predicting 1-year mortality.

Multidisciplinary management of a penetrating cerebellar injury by a fishing speargun: A case study and literature review.

BACKGROUND: Fishing spearguns are a rare cause of nonmissile penetrating brain injuries (PBIs). Discussion of their injury patterns and treatments has been published only sporadically. Here, we report a case of a self-inflicted PBI caused by this type of weapon and present an extensive review of previous case reports to help ascertain the appropriate surgical approach. CASE DESCRIPTION: A 26-year-old man with a preexisting psychiatric illness was transferred to our hospital after a self-inflicted shot with a fishing speargun through his mouth. The ensuing injuries included the impalement of a spear intracranially through the soft palate and posterior oropharyngeal wall. The spear was surgically accessed by the otolaryngology team by splitting the soft palate and was removed by the neurosurgery team in the retrograde direction. Cerebral angiographies were done pre- and postoperatively, and these did not detect any vertebrobasilar arterial system injuries. The patient's postoperative care was uneventful, and he was followed up by a psychiatrist for his long-term care. CONCLUSION: This example of a complicated case of nonmissile PBI caused by an uncommon type of weapon shows how this type of medical emergency can be managed successfully with effective teamwork using a multidisciplinary approach.

Application of machine learning to predict the outcome of pediatric traumatic brain injury.

PURPOSE: Traumatic brain injury (TBI) generally causes mortality and disability, particularly in children. Machine learning (ML) is a computer algorithm, applied as a clinical prediction tool. The present study aims to assess the predictability of ML for the functional outcomes of pediatric TBI. METHODS: A retrospective cohort study was performed targeting children with TBI who were admitted to the trauma center of southern Thailand between January 2009 and July 2020. The patient was excluded if he/she (1) did not undergo a CT scan of the brain, (2) died within the first 24 h, (3) had unavailable complete medical records during admission, or (4) was unable to provide updated outcomes. Clinical and radiologic characteristics were collected such as vital signs, Glasgow coma scale score, and characteristics of intracranial injuries. The functional outcome was assessed using the King's Outcome Scale for Childhood Head Injury, which was thus dichotomized into favourable outcomes and unfavourable outcomes: good recovery and moderate disability were categorized as the former, whereas death, vegetative state, and severe disability were categorized as the latter. The prognostic factors were estimated using traditional binary logistic regression. By data splitting, 70% of data were used for training the ML models and the remaining 30% were used for testing the ML models. The supervised algorithms including support vector machines, neural networks, random forest, logistic regression, naive Bayes and k-nearest neighbor were performed for training of the ML models. Therefore, the ML models were tested for the predictive performances by the testing datasets. RESULTS: There were 828 patients in the cohort. The median age was 72 months (interquartile range 104.7 months, range 2-179 months). Road traffic accident was the most common mechanism of injury, accounting for 68.7%. At hospital discharge, favourable outcomes were achieved in 97.0% of patients, while the mortality rate was 2.2%. Glasgow coma scale score, hypotension, pupillary light reflex, and subarachnoid haemorrhage were associated with TBI outcomes following traditional binary logistic regression; hence, the 4 prognostic factors were used for building ML models and testing performance. The support vector machine model had the best performance for predicting pediatric TBI outcomes: sensitivity 0.95, specificity 0.60, positive predicted value 0.99, negative predictive value 1.0; accuracy 0.94, and area under the receiver operating characteristic curve 0.78. CONCLUSION: The ML algorithms of the present study have a high sensitivity; therefore they have the potential to be screening tools for predicting functional outcomes and counselling prognosis in general practice of pediatric TBIs.

Predicting the Health-related Quality of Life in Patients Following Traumatic Brain Injury.

Background Traumatic brain injury (TBI) commonly causes death and disability that can result in productivity loss and economic burden. The health-related quality of life (HRQoL) has been measured in patients suffering from TBI, both in clinical and socioeconomic perspectives. The study aimed to assess the HRQoL in patients following TBI using the European quality of life measure-5 domain-5 level (EQ-5D-5L) questionnaire and develop models for predicting the EQ-5D-5L index score in patients with TBI. Method A cross-sectional study was performed with 193 TBI patients who had completed the EQ-5D-5L questionnaire. The clinical characteristics, Glasgow coma scale (GCS) score, treatment, and Glasgow outcome scale (GOS) were collected. The total data was divided into training data (80%) and testing data (20%); hence, the factors affecting the EQ-5D-5L index scores were used to develop the predictive model with linear and nonlinear regression. The performances of the predictive models were estimated with the adjusted coefficient of determination (R 2 ) and the root mean square error (RMSE). Results A good recovery was found at 96.4%, while 2.1% displayed an unfavorable outcome. Moreover, the mean EQ-5D-5L index scores were 0.91558 (standard deviation [SD] 1.09639). GCS score, pupillary light reflex, surgery, and GOS score significantly correlated with the HRQoL scores. The multiple linear regression model had a high adjusted R 2 of 0.6971 and a low RMSE of 0.06701, while the polynomial regression developed a nonlinear model that had the highest adjusted R 2 of 0.6843 and the lowest RMSE of 0.06748. Conclusions A strong positive correlation between the physician-based outcome as GOS and HRQoL was observed. Furthermore, both the linear and nonlinear regression models were acceptable approaches to predict the HRQoL of patients after TBI. There would be limitations for estimating the HRQoL in unconscious or intubated patients. The HRQoL obtained from the predictive models would be an alternative method to resolve this problem.

Prognostic Factors of Acromegalic Patients with Growth Hormone-Secreting Pituitary Adenoma After Transsphenoidal Surgery.

BACKGROUND: Acromegaly is a rare, chronic disorder that mostly results from growth hormone (GH)-secreting pituitary adenoma. Transsphenoidal surgery is the first-line treatment of this adenoma. This study aimed to identify factors associated with remission outcome in patients with GH-secreting pituitary adenomas following transsphenoidal surgery. METHODS: Patients with GH-secreting pituitary adenomas who underwent transsphenoidal surgery for tumor removal at Songklanagarind Hospital between January 2003 and December 2019 were retrospectively reviewed. The primary outcome was the remission of disease at the last follow-up using 2000 and 2010 consensus criteria. Using logistic regression analysis, various factors were analyzed for association with disease remission outcome. RESULTS: This study included 51 patients. The remission rate of GH-secreting pituitary microadenomas and macroadenomas following transsphenoidal surgery were 100% and 43.75%, respectively. Multivariate analysis showed that preoperative insulin-like growth factor 1 index ≥2.5 and Knosp classification grade 3-4 were significantly associated with nonremission outcome (P < 0.001 and P = 0.012, respectively). Patients with both of these factors had poor outcomes and never achieved remission after treatment, while patients with neither of these factors had high remission rates (87.5%) following surgery. Four of 6 (66.7%) patients who underwent repeat surgery gained remission. CONCLUSIONS: Preoperative insulin-like growth factor 1 index ≥2.5 and Knosp classification grade 3-4 were important prognostic factors that determined remission outcome after treatment. Patients who have both of these poor prognostic factors should be aggressively treated with surgery, medication, and probably radiation to optimally control the disease.

Morphometry of the C2 Pedicle and Lamina in Thai Patients.

BACKGROUND: Traumatic upper cervical spine leads to instability and neurological deficit. At present, C2 (axis) pedicle or lamina screws for fixation are popular because further external immobilization is not needed. However, these techniques demand experience inserting the screws and carry the risk of vertebral artery or spinal cord injury. In some patients, the C2 screws cannot be inserted because of limited C2 size. OBJECTIVE: To determine the width, length, height, and angle in the C2 pedicle and lamina in the Thai population. MATERIALS AND METHODS: Patient data were collected from the Picture Archiving and Communication System at the Faculty of Medicine, Prince of Songkla University from January 2016 to December 2017. The C2 parameters, i.e., width, length, height, and angle of the pedicle and lamina were recorded. RESULTS: The CT C-spine scans of 270 patients were enrolled. The mean Thai C2 pedicle dimensions were width 5.51 mm, length 23.78 mm, angle 39.04°, and height 8.64 mm. The mean C2 lamina dimensions were width 5.88 mm, length 32.17 mm, angle 49.46°, and height 12.27 mm. Twenty-four patients from the 270 patients (8.8%) had a pedicle width <3.5 mm but all patients had a lamina width ≥3.5 mm. CONCLUSION: In the Thai samples, 8.8% had a C2 pedicle width <3.5 mm which would not allow insertion of screws; however, they could be replaced with lamina screws since the lamina width was ≥3.5 mm. In this study, all of the patients who could not be inserted pedicle can be replaced with lamina screws inserted.

Prognostic Factors and Nomogram Predicting Survival in Diffuse Astrocytoma.

Background Prognosis of low-grade glioma are currently determined by genetic markers that are limited in some countries. This study aimed to use clinical parameters to develop a nomogram to predict survival of patients with diffuse astrocytoma (DA) which is the most common type of low-grade glioma. Materials and Methods Retrospective data of adult patients with DA from three university hospitals in Thailand were analyzed. Collected data included clinical characteristics, neuroimaging findings, treatment, and outcomes. Cox's regression analyses were performed to determine associated factors. Significant associated factors from the Cox regression model were subsequently used to develop a nomogram for survival prediction. Performance of the nomogram was then tested for its accuracy. Results There were 64 patients with DA with a median age of 39.5 (interquartile range [IQR] = 20.2) years. Mean follow-up time of patients was 42 months (standard deviation [SD] = 34.3). After adjusted for three significant factors associated with survival were age ≥60 years (hazard ratio [HR] = 5.8; 95% confidence interval [CI]: 2.09-15.91), motor response score of Glasgow coma scale < 6 (HR = 75.5; 95% CI: 4.15-1,369.4), and biopsy (HR = 0.45; 95% CI: 0.21-0.92). To predict 1-year mortality, sensitivity, specificity, positive predictive value, negative predictive value, accuracy, and area under the curve our nomogram was 1.0, 0.50, 0.45, 1.0, 0.64, and 0.75, respectively. Conclusions This study provided a nomogram predicting prognosis of DA. The nomogram showed an acceptable performance for predicting 1-year mortality.

Traumatic cerebrovascular injury: Prevalence and risk factors.

BACKGROUND: Traumatic cerebrovascular injury (TCVI) is uncommon in traumatic brain injury (TBI). Although TCVI is a rare condition, this complication is serious. A missed or delayed diagnosis may lead to an unexpected life-threatening hemorrhagic event or persistent neurological deficit. The object of this study was to determine the prevalence and risk factors associated with TCVI. METHODS: The authors retrospectively reviewed medical records and neuroimaging studies of 5178 patients with TBI. The association of various factors was investigated using time-to-event statistical analysis. A TCVI which resulted in an occlusion, arteriovenous fistula, pseudoaneurysm or cerebral artery transection was defined as an event. RESULTS: Forty-two patients developed a TCVI after injuries with an overall prevalence of 0.8%. The risk factors for an intracranial arterial injury based on univariate analysis using the Cox proportional hazard regression were penetrating injury, severe head injury, orbitofacial injury, basilar skull fracture, subdural hematoma, and cerebral contusion. In multivariable analysis, the two variables that were independently associated with TCVI were basilar skull fracture (odds ratio [OR] 22.1, 95% confidence interval [CI] 11.5-42.2) followed by orbitofacial fracture (OR 13.6, 95% CI 6.8-27.3). CONCLUSIONS: Although TCVI is a rare complication of TBI, early investigation in high-risk patients may be necessary for early treatment before an unexpected fatal event occurs.

Traumatic cerebrovascular injury: clinical characteristics and illustrative cases.

OBJECTIVE: Traumatic cerebrovascular injury (TCVI) is a rare and serious complication of traumatic brain injury (TBI). Various forms of TCVIs have been reported, including occlusions, arteriovenous fistulas, pseudoaneurysms, and transections. They can present at a variety of intervals after TBI and may manifest as sudden episodes, progressive symptoms, and even delayed fatal events. The purpose of this study was to analyze cases of TCVI identified at a single institution and further explore types and characteristics of these complications of TBI in order to improve recognition and treatment of these injuries. METHODS: The authors performed a retrospective review of cases of TCVI identified at their institution between 2013 and 2016. A total of 5178 patients presented with TBI during this time period, and 42 of these patients qualified for a diagnosis of TCVI and had adequate medical and imaging records for analysis. Data from their cases were analyzed, and 3 illustrative cases are presented in detail. RESULTS: The most common type of TCVI was arteriovenous fistula (86.4%), followed by pseudoaneurysm (11.9%), occlusion (2.4%), and transection (2.4%). The mortality rate of patients with TCVI was 7.1%. CONCLUSIONS: The authors describe the clinical characteristics of patients with TCVI and provide data from a series of 42 cases. It is important to recognize TCVI in order to facilitate early diagnosis and treatment.

Machine learning applications for the prediction of surgical site infection in neurological operations.

OBJECTIVE: Surgical site infection (SSI) following a neurosurgical operation is a complication that impacts morbidity, mortality, and economics. Currently, machine learning (ML) algorithms are used for outcome prediction in various neurosurgical aspects. The implementation of ML algorithms to learn from medical data may help in obtaining prognostic information on diseases, especially SSIs. The purpose of this study was to compare the performance of various ML models for predicting surgical infection after neurosurgical operations. METHODS: A retrospective cohort study was conducted on patients who had undergone neurosurgical operations at tertiary care hospitals between 2010 and 2017. Supervised ML algorithms, which included decision tree, naive Bayes with Laplace correction, k-nearest neighbors, and artificial neural networks, were trained and tested as binary classifiers (infection or no infection). To evaluate the ML models from the testing data set, their sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), as well as their accuracy, receiver operating characteristic curve, and area under the receiver operating characteristic curve (AUC) were analyzed. RESULTS: Data were available for 1471 patients in the study period. The SSI rate was 4.6%, and the type of SSI was superficial, deep, and organ/space in 1.2%, 0.8%, and 2.6% of cases, respectively. Using the backward stepwise method, the authors determined that the significant predictors of SSI in the multivariable Cox regression analysis were postoperative CSF leakage/subgaleal collection (HR 4.24, p < 0.001) and postoperative fever (HR 1.67, p = 0.04). Compared with other ML algorithms, the naive Bayes had the highest performance with sensitivity at 63%, specificity at 87%, PPV at 29%, NPV at 96%, and AUC at 76%. CONCLUSIONS: The naive Bayes algorithm is highlighted as an accurate ML method for predicting SSI after neurosurgical operations because of its reasonable accuracy. Thus, it can be used to effectively predict SSI in individual neurosurgical patients. Therefore, close monitoring and allocation of treatment strategies can be informed by ML predictions in general practice.

Diagnostic Yield and Complication of Frameless Stereotactic Brain Biopsy.

BACKGROUND: With the advancement of neuronavigation technologies, frameless stereotactic brain biopsy has been developed. Previous studies proved that frameless stereotactic brain biopsy was as effective and safe as frame-based stereotactic brain biopsy. The authors aimed to find the factors associated with diagnostic yield and complication rate of frameless intracranial biopsy. MATERIALS AND METHODS: Frameless stereotactic brain biopsy procedures, between March 2009 and April 2017, were retrospectively reviewed from medical records including imaging studies. Using logistic regression analysis, various factors were analyzed for association with diagnostic yield and postoperative complications. RESULTS: Eighty-nine frameless stereotactic brain biopsy procedures were performed on 85 patients. The most common pathology was primary central nervous system lymphoma (43.8%), followed by low-grade glioma (15.7%), and high-grade glioma (15%), respectively. The diagnostic yield was 87.6%. Postoperative intracerebral hematoma occurred in 19% of cases; however, it was symptomatic in only one case. The size of the lesion was associated with both diagnostic yield and postoperative intracerebral hematoma complication. Lesions, larger than 3 cm in diameter, were associated with a higher rate of positive biopsy result (P = 0.01). Lesion 3 cm or smaller than 3 cm in diameter, and intraoperative bleeding associated with a higher percentage of postoperative intracerebral hematoma complications (P = 0.01). CONCLUSIONS: For frameless stereotactic brain biopsy, the size of the lesion is the essential factor determining diagnostic yield and postoperative intracerebral hematoma complication.

Outcome of Ruptured Posterior Communicating Artery Aneurysm Treatment Comparing Between Clipping and Coiling Techniques.

BACKGROUND: Very few data stratify the functional outcome of ruptured posterior communicating artery (PCoA) aneurysm treatment based on each treatment strategy. Therefore, the authors aimed to evaluate the outcome of PCoA aneurysm treatment to compare the clipping and coiling techniques and find the factors that determined the treatment outcomes. METHODS: Patients with ruptured PCoA aneurysm at Songklanagarind Hospital between November 2002 and March 2018 were retrospectively reviewed and classified into the clipping group or the coiling group. The primary outcome was the modified Rankin Scale (mRS) at 6 months after treatment, and the patients were stratified into "good outcome" (mRS 0-2) and "poor outcome" (mRS 3-6). Using logistic regression analysis, various factors were analyzed for association with the treatment outcome. RESULTS: This study included 189 patients: 104 and 85 patients in the clipping and coiling groups, respectively. Patients in the coiling group were significantly older, with more underlying diseases. However, the treatment outcomes at 6 months were not significantly different between the 2 groups. The percentages of poor outcomes in the clipping and coiling groups were 28% and 31%, respectively (P = 0.734). Older age, seizure, higher Hunt and Hess grade, deterioration before aneurysm obliteration, an aneurysm on the left side, and a medially projected PCoA aneurysm were statistically significantly correlated with poor outcomes. CONCLUSIONS: Neurosurgical clipping and endovascular coiling are effective treatments for aneurysm obliteration in ruptured PCoA aneurysm. An aneurysm on the left side and a medially projected PCoA aneurysm were the factors correlated with poor outcome.

Multiple, Primary Brain Tumors with Diverse Origins and Different Localizations: Case Series and Review of the Literature.

BACKGROUND: Multiple, primary brain tumors with different histological types occurring in the same patient are extremely rare. Several hypotheses have been proposed, and the pathophysiology of coexisting tumors has long been debated; however, due to low incidence, standard practices for this scenario are still inconclusive. CASE DESCRIPTION: The authors describe 6 cases of coexisting tumors. By conducting a literature research focused on the computed tomography (CT) era and patients without prior radiation or phakomatosis. Sixty-five such reported cases were identified. In addition, the authors summarize their experience in 6 patients including histopathological features, chronological presentations, outcomes, mortality, and management from their series as well as from previous cases from the reported literature. CONCLUSION: The coexistence of multiple, primary brain tumors is an interesting condition. Surgical management remains the major treatment; malignant histology has a poor prognostic factor.

Butterfly Tumor of the Corpus Callosum: Clinical Characteristics, Diagnosis, and Survival Analysis.

BACKGROUND: The pathologies implicate the bilateral corpus callosum that builds the butterfly pattern on axial view. These tumors have seldom been investigated for both clinical manifestations and outcome. OBJECTIVE: The objective of this study was to describe the clinical characteristics and outcomes of the butterfly tumor and to identify the predictive factors associated with survival outcome. METHODS: A retrospective study of 50 butterfly tumor was conducted between 2003 and 2016. The clinical characteristics, imaging, and outcome were assessed for the purpose of descriptive analysis. Using the Kaplan-Meier method, the median overall survival of the butterfly tumor was determined. Furthermore, the Cox proportional hazard regression was the estimated hazard ratio for death. RESULTS: Diffuse large B-cell lymphoma was common of butterfly lesions. The mortality rate was 78% and overall median survival time was 16.03 months (95% confidence interval: 14.0-19.8). Using Cox proportional hazards regression, the independent prognostic factors were Karnofsky Performance Status score ≤70, splenium involvement, and butterfly glioblastoma. CONCLUSIONS: The butterfly tumor is a poor prognostic disease compared with each histology subgroup. Further molecular investigation is preferable to explore genetic variations associated with these tumors.

Spinal capillary hemangiomas: Two cases reports and review of the literature.

Hemangiomas have rarely been found in the spinal cord. A few cases of spinal capillary hemangioma have been reported since 1987. The authors reported the two cases of capillary hemangioma including the tumor at conus medullaris and the another mimicked von Hippel-Lindau disease. A 15-year-old man was presented with coccydynia and left leg pain. A magnetic resonance imaging (MRI) revealed an intradural extramedullary enhancing mass at conus medullaris. Another case, a 31-year-old man was presented with a history of familial history of brain tumor, retinal hemangioma both eyes, multiple pancreatic cyst and syringobulbia with syringohydromyelia. On MRI, a well-circumscribed intramedullary nodule was detected at C5-6 level and multiple subpial nodule along cervicothoracic spinal cord. All patients underwent surgery, and the histological diagnosis confirmed capillary hemangioma. Although rare and indistinguishable from other tumors, capillary hemangioma should be in the differential diagnosis of the spinal cord tumor.