bluuebunny/crossref_metadata_2025_split huggingface dataset:
Long-term results of Gamma Knife surgery for uveal melanomas
Authors: Kang Dong Wan , Lee Sung Chul , Park Yong Gou , Chang Jong Hee 
Published: November 2018

Object
Gamma Knife surgery (GKS) is currently believed to be a safe and minimally invasive modality in the treatment of uveal melanomas. It could be used as an alternative treatment to enucleation, preserving the eyeball as well as visual function. The authors report their experiences with GKS for uveal melanomas for the period from February 1998 to December 2006.

Methods
Twenty-two patients with uveal melanoma were enrolled in this study. The population consisted of 12 men and 10 women with a mean age of 53.4 years (range 24–79 years). The mean tumor volume was 877 mm3, and the mean margin dose was 45.6 Gy. The median follow-up period was 67 months (range 3–126 months). All of the patients had received a diagnosis and referral from an ophthalmology clinic; the patients underwent a preoperative orbital examination that included MRI.

Results
Tumor regression was achieved in 20 patients (90.9%), whereas tumor progression was observed in 2 patients (9.1%) 3 years after GKS. The cumulative 1-year and 2-year mean rates of tumor thickness reduction were 18.8% and 42.8%, respectively. The mean rate of tumor volume reduction was 63.7%. The rate of eye retention 5 years after radiosurgery was 77.3% (17 of 22 patients). Overall visual acuity was reduced after GKS in all patients; 14 patients (63.6%) displayed preserved visual function better than hand-movement perception. The most frequent side effect was cataract, which was detected in 9 patients (40.9%); this was followed in frequency by radiation-induced retinopathy in 5 patients (22.7%).

Conclusions
Gamma Knife surgery provides excellent local control of uveal melanomas with a decrease in volume over time. This procedure not only preserves the eyeball and its potential visual function, but also decreases the potential for hematological dissemination and achieves sufficient local tumor control with a gradual reduction in volume.
Gamma Knife surgery for intracranial chordoma and chondrosarcoma: radiosurgical perspectives and treatment outcomes
Authors: Kim Ji Hee , Jung Hyun Ho , Chang Jong Hee , Chang Jin Woo , Park Yong Gou , Chang Won Seok 
Published: November 2018

ObjectIntracranial chordomas and chondrosarcomas are histologically low-grade, locally invasive tumors that are reported to be similar in terms of anatomical location, clinical presentation, and radiological findings but different in terms of behavior and outcomes. The purpose of this study was to investigate and compare clinical outcomes after Gamma Knife surgery (GKS) for the treatment of intracranial chordoma and chondrosarcoma.MethodsThe authors conducted a retrospective review of the results of radiosurgical treatment of intracranial chordomas and chondrosarcomas. They enrolled patients who had undergone GKS for intracranial chordoma or chondrosarcoma at the Yonsei Gamma Knife Center, Yonsei University College of Medicine, from October 2000 through June 2007. Analyses included only patients for whom the disease was pathologically diagnosed before GKS and for whom more than 5 years of follow-up data after GKS were available. Rates of progression-free survival and overall survival were analyzed and compared according to tumor pathology. Moreover, the association between tumor control and the margin radiation dose to the tumor was analyzed, and the rate of tumor volume change after GKS was quantified.ResultsA total of 10 patients were enrolled in this study. Of these, 5 patients underwent a total of 8 sessions of GKS for chordoma, and the other 5 patients underwent a total of 7 sessions of GKS for chondrosarcoma. The 2- and 5-year progression-free survival rates for patients in the chordoma group were 70% and 35%, respectively, and rates for patients in the chondrosarcoma group were 100% and 80%, respectively (log-rank test, p = 0.04). The 2- and 5-year overall survival rates after GKS for patients in the chordoma group were 87.5% and 72.9%, respectively, and rates for patients in the chondrosarcoma group were 100% and 100%, respectively (log-rank test, p = 0.03). The mean rates of tumor volume change 2 years after radiosurgery were 79.64% and 39.91% for chordoma and chondrosarcoma, respectively (p = 0.05). No tumor progression was observed when margin doses greater than 16 Gy for chordoma and 14 Gy for chondrosarcoma were prescribed.ConclusionsOutcomes after GKS were more favorable for patients with chondrosarcoma than for those with chordoma. The data also indicated that at 2 years after GKS, the rate of volume change is significantly higher for chordomas than for chondrosarcomas. The authors conclude that radiosurgery with a margin dose of more than 16 Gy for chordomas and more than 14 Gy for chondrosarcomas seems to enhance local tumor control with relatively few complications. Further studies are needed to determine the optimal dose of GKS for patients with intracranial chordoma or chondrosarcoma.
Differences in fixation strength among constructs of atlantoaxial fixation
Authors: Chang Chih-Chang , Huang Wen-Cheng , Tu Tsung-Hsi , Chang Peng-Yuan , Fay Li-Yu , Wu Jau-Ching , Cheng Henrich 
Published: November 2018

OBJECTIVETo avoid jeopardizing an aberrant vertebral artery, there are three common options in placing a C2 screw, including pedicle, pars, and translaminar screws. Although biomechanical studies have demonstrated similar strength among these C2 screws in vitro, there are limited clinical data to address their differences in vivo. When different screws were placed in each side, few reports have compared the outcomes. The present study aimed to evaluate these multiple combinations of C2 screws.METHODSConsecutive adult patients who underwent posterior atlantoaxial (AA) fixation were retrospectively reviewed. Every patient uniformly had bilateral C1 lateral mass screws in conjunction with 2 C2 screws (1 C2 screw on each side chosen among the three options: pedicle, pars, or translaminar screws, based on individualized anatomical consideration). These patients were then grouped according to the different combinations of C2 screws for comparison of the outcomes.RESULTSA total of 63 patients were analyzed, with a mean follow-up of 34.3 months. There were five kinds of construct combinations of the C2 screws: 2 pedicle screws (the Ped-Ped group, n = 24), 2 translaminar screws (the La-La group, n = 7), 2 pars screws (the Pars-Pars group, n = 6), 1 pedicle and 1 pars screw (the Ped-Pars group, n = 7), and 1 pedicle and 1 translaminar screw (the Ped-La group, n = 19). The rate of successful fixation in each of the groups was 100%, 57.1%, 100%, 100%, and 78.9% (Ped-Ped, La-La, Par-Par, Ped-Par, and Ped-La, respectively). The patients who had no translaminar screw had a higher rate of success than those who had 1 or 2 translaminar screws (100% vs 73.1%, p = 0.0009). Among the 5 kinds of construct combinations, 2 C2 pedicle screws (the Ped-Ped group) had higher rates of success than 1 C2 pedicle and 1 C2 translaminar screw (the Ped-La group, p = 0.018). Overall, the rate of successful fixation was 87.3% (55/63). There were 7 patients (4 in the Ped-La group and 3 in the La-La group) who lost fixation/reduction, and they all had at least 1 translaminar screw.CONCLUSIONSIn AA fixation, C2 pedicle or pars screws or a combination of both provided very high success rates. Involvement of 1 or 2 C2 translaminar screws in the construct significantly lowered success rates. Therefore, a C2 pars screw is recommended over a translaminar screw.
Updating superficial siderosis of the central nervous system: bleeding of a dorsal osteophyte into the subarachnoid space from a perforating artery
Authors: Brembilla Carlo , Lanterna Luigi Andrea , Bonito Virginio , Gardinetti Margherita , Dorelli Gianluigi , Rampini Angela Dele , Gritti Paolo , Bernucci Claudio 
Published: November 2018

Superficial siderosis of the central nervous system (SSCNS) is an uncommon and often unrecognized disorder that results from recurrent and persistent bleeding into the subarachnoid space. Currently, there is no effective treatment for SSCNS. The identification and surgical resolution of the cause of bleeding remains the most reliable method of treatment, but the cause of bleeding is often not apparent. The identified sources of recurrent bleeding have typically included neoplasms, vascular malformations, brachial plexus or nerve root injury or avulsion, and previous head and spinal surgery. An association between recurrent bleeding in the CNS and dural abnormalities in the spine has recently been suggested. Dural tears have been identified in relation to a protruding disc or osteophyte. Also in these patients, the exact mechanism of bleeding remains unknown because of a lack of objective surgical data, even in patients who undergo neurosurgical procedures.The present case concerns a 48-year-old man who presented with longstanding symptoms of mild hearing loss and mild gait ataxia. A diagnosis of SSCNS was made in light of the patient’s history and the findings on physical examination, imaging, and laboratory testing. MRI and CT detected a small calcific osteophyte in the anterior epidural space of T8–9. The patient underwent surgical removal of the bone spur and dural tear repair. During the surgery, the authors detected a perforating artery, which was on the osteophyte, that was bleeding into the subarachnoid space. This case shows a possible mechanism of chronic bleeding from an osteophyte into the subarachnoid space. In the literature currently available, a perforating artery on an osteophyte bleeding into the subarachnoid space has never been described in SSCNS.
Impact of local steroid application in a minimally invasive transforaminal lumbar interbody fusion: results of a prospective, randomized, single-blind trial
Authors: Haws Brittany E. , Khechen Benjamin , Patel Dil V. , Bawa Mundeep S. , Ahn Junyoung , Bohl Daniel D. , Mayo Benjamin C. , Massel Dustin H. , Guntin Jordan A. , Cardinal Kaitlyn L. , Singh Kern 
Published: November 2018

OBJECTIVELocal epidural steroid application may be associated with decreased pain and narcotic use in the immediate postoperative period following lumbar discectomy. However, local steroid delivery following lumbar fusion procedures has not been well characterized. This study aims to characterize the effect of local intraoperative depomedrol application on perioperative and postoperative outcomes following a single-level minimally invasive transforaminal lumbar interbody fusion (MIS TLIF).METHODSA prospective, randomized, single-blinded study was performed. A priori power analysis determined that 86 patients were needed to detect a difference of 1 point in the visual analog scale (VAS) pain score between groups. Ninety-three patients were randomized into depomedrol (DEPO) and no depomedrol (NODEPO) cohorts. Prior to surgical closure, DEPO patients received 1 ml depomedrol (80 mg) applied directly to the surgical site by using a Gelfoam carrier. NODEPO patients received 1 ml saline on the same Gelfoam carrier. Perioperative outcomes including acute postoperative pain and narcotic use were assessed for the duration of inpatient stay. Patient-reported outcomes (PROs) questionnaires including VAS back and leg pain scores, and Oswestry Disability Index (ODI) were administered preoperatively and at 6-week, 12-week, and 6-month follow-up. Outcomes for DEPO and NODEPO cohorts were compared using linear regression controlled for sex.RESULTSOf the 93 patients, 45 (48.4%) were randomized to DEPO and 48 (51.6%) to NODEPO. A greater percentage of DEPO patients were female (53.3% vs 27.1%, p = 0.010). There were no other significant differences in patient baseline characteristics. Similarly, operating time, estimated blood loss, and length of inpatient stay did not differ between cohorts. Patients in the DEPO cohort consumed fewer hourly narcotics on postoperative day 0 (5.3 vs 6.3 oral morphine equivalents/hour, p = 0.034). However, no differences in acute postoperative pain or total narcotics consumption were observed between groups. Preoperative VAS leg scores were statistically different between cohorts (p = 0.027). However, preoperative ODI and VAS back scores did not differ between groups. Additionally, DEPO and NODEPO groups experienced similar improvements in PROs at all postoperative time points.CONCLUSIONSLocal depomedrol use did not lead to decreases in acute postoperative pain or narcotics consumption after MIS TLIF. Additionally, local depomedrol was not associated with postoperative improvements in PROs. The findings of this randomized trial suggest that surgical and clinical outcomes following MIS TLIF may not be impacted by intraoperative application of depomedrol.Clinical trial registration no.: NCT03308084 (clinicaltrials.gov)
Machine-learning analysis outperforms conventional statistical models and CT classification systems in predicting 6-month outcomes in pediatric patients sustaining traumatic brain injury
Authors: Hale Andrew T. , Stonko David P. , Brown Amber , Lim Jaims , Voce David J. , Gannon Stephen R. , Le Truc M. , Shannon Chevis N. 
Published: November 2018

OBJECTIVEModern surgical planning and prognostication requires the most accurate outcomes data to practice evidence-based medicine. For clinicians treating children following traumatic brain injury (TBI) these data are severely lacking. The first aim of this study was to assess published CT classification systems in the authors’ pediatric cohort. A pediatric-specific machine-learning algorithm called an artificial neural network (ANN) was then created that robustly outperformed traditional CT classification systems in predicting TBI outcomes in children.METHODSThe clinical records of children under the age of 18 who suffered a TBI and underwent head CT within 24 hours after TBI (n = 565) were retrospectively reviewed.RESULTS“Favorable” outcome (alive with Glasgow Outcome Scale [GOS] score ≥ 4 at 6 months postinjury, n = 533) and “unfavorable” outcome (death at 6 months or GOS score ≤ 3 at 6 months postinjury, n = 32) were used as the primary outcomes. The area under the receiver operating characteristic (ROC) curve (AUC) was used to delineate the strength of each CT grading system in predicting survival (Helsinki, 0.814; Rotterdam, 0.838; and Marshall, 0.781). The AUC for CT score in predicting GOS score ≤ 3, a measure of overall functionality, was similarly predictive (Helsinki, 0.717; Rotterdam, 0.748; and Marshall, 0.663). An ANN was then constructed that was able to predict 6-month outcomes with profound accuracy (AUC = 0.9462 ± 0.0422).CONCLUSIONSThis study showed that machine-learning can be leveraged to more accurately predict TBI outcomes in children.
Machine learning analyses can differentiate meningioma grade by features on magnetic resonance imaging
Authors: Hale Andrew T. , Stonko David P. , Wang Li , Strother Megan K. , Chambless Lola B. 
Published: November 2018

OBJECTIVEPrognostication and surgical planning for WHO grade I versus grade II meningioma requires thoughtful decision-making based on radiographic evidence, among other factors. Although conventional statistical models such as logistic regression are useful, machine learning (ML) algorithms are often more predictive, have higher discriminative ability, and can learn from new data. The authors used conventional statistical models and an array of ML algorithms to predict atypical meningioma based on radiologist-interpreted preoperative MRI findings. The goal of this study was to compare the performance of ML algorithms to standard statistical methods when predicting meningioma grade.METHODSThe cohort included patients aged 18–65 years with WHO grade I (n = 94) and II (n = 34) meningioma in whom preoperative MRI was obtained between 1998 and 2010. A board-certified neuroradiologist, blinded to histological grade, interpreted all MR images for tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, presence of a draining vein, and patient sex. The authors trained and validated several binary classifiers: k-nearest neighbors models, support vector machines, naïve Bayes classifiers, and artificial neural networks as well as logistic regression models to predict tumor grade. The area under the curve–receiver operating characteristic curve was used for comparison across and within model classes. All analyses were performed in MATLAB using a MacBook Pro.RESULTSThe authors included 6 preoperative imaging and demographic variables: tumor volume, degree of peritumoral edema, presence of necrosis, tumor location, patient sex, and presence of a draining vein to construct the models. The artificial neural networks outperformed all other ML models across the true-positive versus false-positive (receiver operating characteristic) space (area under curve = 0.8895).CONCLUSIONSML algorithms are powerful computational tools that can predict meningioma grade with great accuracy.
Utility of deep neural networks in predicting gross-total resection after transsphenoidal surgery for pituitary adenoma: a pilot study
Authors: Staartjes Victor E. , Serra Carlo , Muscas Giovanni , Maldaner Nicolai , Akeret Kevin , van Niftrik Christiaan H. B. , Fierstra Jorn , Holzmann David , Regli Luca 
Published: November 2018

OBJECTIVEGross-total resection (GTR) is often the primary surgical goal in transsphenoidal surgery for pituitary adenoma. Existing classifications are effective at predicting GTR but are often hampered by limited discriminatory ability in moderate cases and by poor interrater agreement. Deep learning, a subset of machine learning, has recently established itself as highly effective in forecasting medical outcomes. In this pilot study, the authors aimed to evaluate the utility of using deep learning to predict GTR after transsphenoidal surgery for pituitary adenoma.METHODSData from a prospective registry were used. The authors trained a deep neural network to predict GTR from 16 preoperatively available radiological and procedural variables. Class imbalance adjustment, cross-validation, and random dropout were applied to prevent overfitting and ensure robustness of the predictive model. The authors subsequently compared the deep learning model to a conventional logistic regression model and to the Knosp classification as a gold standard.RESULTSOverall, 140 patients who underwent endoscopic transsphenoidal surgery were included. GTR was achieved in 95 patients (68%), with a mean extent of resection of 96.8% ± 10.6%. Intraoperative high-field MRI was used in 116 (83%) procedures. The deep learning model achieved excellent area under the curve (AUC; 0.96), accuracy (91%), sensitivity (94%), and specificity (89%). This represents an improvement in comparison with the Knosp classification (AUC: 0.87, accuracy: 81%, sensitivity: 92%, specificity: 70%) and a statistically significant improvement in comparison with logistic regression (AUC: 0.86, accuracy: 82%, sensitivity: 81%, specificity: 83%) (all p < 0.001).CONCLUSIONSIn this pilot study, the authors demonstrated the utility of applying deep learning to preoperatively predict the likelihood of GTR with excellent performance. Further training and validation in a prospective multicentric cohort will enable the development of an easy-to-use interface for use in clinical practice.
A machine learning approach to predict early outcomes after pituitary adenoma surgery
Authors: Hollon Todd C. , Parikh Adish , Pandian Balaji , Tarpeh Jamaal , Orringer Daniel A. , Barkan Ariel L. , McKean Erin L. , Sullivan Stephen E. 
Published: November 2018

OBJECTIVEPituitary adenomas occur in a heterogeneous patient population with diverse perioperative risk factors, endocrinopathies, and other tumor-related comorbidities. This heterogeneity makes predicting postoperative outcomes challenging when using traditional scoring systems. Modern machine learning algorithms can automatically identify the most predictive risk factors and learn complex risk-factor interactions using training data to build a robust predictive model that can generalize to new patient cohorts. The authors sought to build a predictive model using supervised machine learning to accurately predict early outcomes of pituitary adenoma surgery.METHODSA retrospective cohort of 400 consecutive pituitary adenoma patients was used. Patient variables/predictive features were limited to common patient characteristics to improve model implementation. Univariate and multivariate odds ratio analysis was performed to identify individual risk factors for common postoperative complications and to compare risk factors with model predictors. The study population was split into 300 training/validation patients and 100 testing patients to train and evaluate four machine learning models using binary classification accuracy for predicting early outcomes.RESULTSThe study included a total of 400 patients. The mean ± SD patient age was 53.9 ± 16.3 years, 59.8% of patients had nonfunctioning adenomas and 84.7% had macroadenomas, and the mean body mass index (BMI) was 32.6 ± 7.8 (58.0% obesity rate). Multivariate odds ratio analysis demonstrated that age < 40 years was associated with a 2.86 greater odds of postoperative diabetes insipidus and that nonobese patients (BMI < 30) were 2.2 times more likely to develop postoperative hyponatremia. Using broad criteria for a poor early postoperative outcome—major medical and early surgical complications, extended length of stay, emergency department admission, inpatient readmission, and death—31.0% of patients met criteria for a poor early outcome. After model training, a logistic regression model with elastic net (LR-EN) regularization best predicted early postoperative outcomes of pituitary adenoma surgery on the 100-patient testing set—sensitivity 68.0%, specificity 93.3%, overall accuracy 87.0%. The receiver operating characteristic and precision-recall curves for the LR-EN model had areas under the curve of 82.7 and 69.5, respectively. The most important predictive variables were lowest perioperative sodium, age, BMI, highest perioperative sodium, and Cushing’s disease.CONCLUSIONSEarly postoperative outcomes of pituitary adenoma surgery can be predicted with 87% accuracy using a machine learning approach. These results provide insight into how predictive modeling using machine learning can be used to improve the perioperative management of pituitary adenoma patients.