Identifying clusters of objective functional impairment in patients with degenerative lumbar spinal disease using unsupervised learning

OBJECTIVES The five-repetition sit-to-stand (5R-STS) test was designed to capture objective functional impairment (OFI), and thus provides an adjunctive dimension in patient assessment. It is conceivable that there are different subsets of patients with OFI and degenerative lumbar disease. We aim to identify clusters of objectively functionally impaired individuals based on 5R-STS and unsupervised machine learning (ML). METHODS Data from two prospective cohort studies on patients with surgery for degenerative lumbar disease and 5R-STS times of ≥ 10.5 s-indicating presence of OFI. K-means clustering-an unsupervised ML algorithm-was applied to identify clusters of OFI. Cluster hallmarks were then identified using descriptive and inferential statistical analyses. RESULTS We included 173 patients (mean age [standard deviation]: 46.7 [12.7] years, 45% male) and identified three types of OFI. OFI Type 1 (57 pts., 32.9%), Type 2 (81 pts., 46.8%), and Type 3 (35 pts., 20.2%) exhibited mean 5R-STS test times of 14.0 (3.2), 14.5 (3.3), and 27.1 (4.4) seconds, respectively. The grades of OFI according to the validated baseline severity stratification of the 5R-STS increased significantly with each OFI type, as did extreme anxiety and depression symptoms, issues with mobility and daily activities. Types 1 and 2 are characterized by mild to moderate OFI-with female gender, lower body mass index, and less smokers as Type I hallmarks. CONCLUSIONS Unsupervised learning techniques identified three distinct clusters of patients with OFI that may represent a more holistic clinical classification of patients with OFI than test-time stratifications alone, by accounting for individual patient characteristics

Sex-related differences in postoperative complications following elective craniotomy for intracranial lesions: An observational study

Introduction: The integration of sex-related differences in neurosurgery is crucial for new, possible sex-specific, therapeutic approaches. In neurosurgical emergencies, such as traumatic brain injury and aneurysmal subarachnoid hemorrhage, these differences have been investigated. So far, little is known concerning the impact of sex on frequency of postoperative complications after elective craniotomy. This study investigates whether sex-related differences exist in frequency of postoperative complications in patients who underwent elective craniotomy for intracranial lesion. Material and methods: All consecutive patients who underwent an elective intracranial procedure over a 2-year period at our center were eligible for inclusion in this retrospective study. Demographic data, comorbidities, frequency of postoperative complications at 24 hours following surgery and at discharge, and hospital length of stay were compared among females and males. Results: Overall, 664 patients were considered for the analysis. Of those, 339 (50.2%) were females. Demographic data were comparable among females and males. More females than males suffered from allergic, muscular, and rheumatic disorders. No differences in frequency of postoperative complications at 24 hours after surgery and at discharge were observed among females and males. Similarly, the hospital length of stay was comparable. Conclusions: In the present study, no sex-related differences in frequency of early postoperative complications and at discharge following elective craniotomy for intracranial lesions were observed

FUSE-ML: development and external validation of a clinical prediction model for mid-term outcomes after lumbar spinal fusion for degenerative disease

Background: Indications and outcomes in lumbar spinal fusion for degenerative disease are notoriously heterogenous. Selected subsets of patients show remarkable benefit. However, their objective identification is often difficult. Decision-making may be improved with reliable prediction of long-term outcomes for each individual patient, improving patient selection and avoiding ineffective procedures. Methods: Clinical prediction models for long-term functional impairment [Oswestry Disability Index (ODI) or Core Outcome Measures Index (COMI)], back pain, and leg pain after lumbar fusion for degenerative disease were developed. Achievement of the minimum clinically important difference at 12 months postoperatively was defined as a reduction from baseline of at least 15 points for ODI, 2.2 points for COMI, or 2 points for pain severity. Results: Models were developed and integrated into a web-app ( https://neurosurgery.shinyapps.io/fuseml/ ) based on a multinational cohort [N = 817; 42.7% male; mean (SD) age: 61.19 (12.36) years]. At external validation [N = 298; 35.6% male; mean (SD) age: 59.73 (12.64) years], areas under the curves for functional impairment [0.67, 95% confidence interval (CI): 0.59-0.74], back pain (0.72, 95%CI: 0.64-0.79), and leg pain (0.64, 95%CI: 0.54-0.73) demonstrated moderate ability to identify patients who are likely to benefit from surgery. Models demonstrated fair calibration of the predicted probabilities. Conclusions: Outcomes after lumbar spinal fusion for degenerative disease remain difficult to predict. Although assistive clinical prediction models can help in quantifying potential benefits of surgery and the externally validated FUSE-ML tool may aid in individualized risk-benefit estimation, truly impacting clinical practice in the era of "personalized medicine" necessitates more robust tools in this patient population. Keywords: Clinical prediction model; Machine learning; Neurosurgery; Outcome prediction; Predictive analytics; Spinal fusion

Assessing Perfusion in Steno-Occlusive Cerebrovascular Disease Using Transient Hypoxia-Induced Deoxyhemoglobin as a Dynamic Susceptibility Contrast Agent

BACKGROUND AND PURPOSE Resting brain tissue perfusion in cerebral steno-occlusive vascular disease can be assessed by MR imaging using gadolinium-based susceptibility contrast agents. Recently, transient hypoxia-induced deoxyhemoglobin has been investigated as a noninvasive MR imaging contrast agent. Here we present a comparison of resting perfusion metrics using transient hypoxia-induced deoxyhemoglobin and gadolinium-based contrast agents in patients with known cerebrovascular steno-occlusive disease. MATERIALS AND METHODS Twelve patients with steno-occlusive disease underwent DSC MR imaging using a standard bolus of gadolinium-based contrast agent compared with transient hypoxia-induced deoxyhemoglobin generated in the lungs using an automated gas blender. A conventional multi-slice 2D gradient echo sequence was used to acquire the perfusion data and analyzed using a standard tracer kinetic model. MTT, relative CBF, and relative CBV maps were generated and compared between contrast agents. RESULTS The spatial distributions of the perfusion metrics generated with both contrast agents were consistent. Perfusion metrics in GM and WM were not statistically different except for WM MTT. CONCLUSIONS Cerebral perfusion metrics generated with noninvasive transient hypoxia-induced changes in deoxyhemoglobin are very similar to those generated using a gadolinium-based contrast agent in patients with cerebrovascular steno-occlusive disease

Transient deoxyhemoglobin formation as a contrast for perfusion MRI studies in patients with brain tumors: a feasibility study

Background: Transient hypoxia-induced deoxyhemoglobin (dOHb) has recently been shown to represent a comparable contrast to gadolinium-based contrast agents for generating resting perfusion measures in healthy subjects. Here, we investigate the feasibility of translating this non-invasive approach to patients with brain tumors. Methods: A computer-controlled gas blender was used to induce transient precise isocapnic lung hypoxia and thereby transient arterial dOHb during echo-planar-imaging acquisition in a cohort of patients with different types of brain tumors (n = 9). We calculated relative cerebral blood volume (rCBV), cerebral blood flow (rCBF), and mean transit time (MTT) using a standard model-based analysis. The transient hypoxia induced-dOHb MRI perfusion maps were compared to available clinical DSC-MRI. Results: Transient hypoxia induced-dOHb based maps of resting perfusion displayed perfusion patterns consistent with underlying tumor histology and showed high spatial coherence to gadolinium-based DSC MR perfusion maps. Conclusion: Non-invasive transient hypoxia induced-dOHb was well-tolerated in patients with different types of brain tumors, and the generated rCBV, rCBF and MTT maps appear in good agreement with perfusion maps generated with gadolinium-based DSC MR perfusion

Global adoption of robotic technology into neurosurgical practice and research

Recent technological advancements have led to the development and implementation of robotic surgery in several specialties, including neurosurgery. Our aim was to carry out a worldwide survey among neurosurgeons to assess the adoption of and attitude toward robotic technology in the neurosurgical operating room and to identify factors associated with use of robotic technology. The online survey was made up of nine or ten compulsory questions and was distributed via the European Association of the Neurosurgical Societies (EANS) and the Congress of Neurological Surgeons (CNS) in February and March 2018. From a total of 7280 neurosurgeons who were sent the survey, we received 406 answers, corresponding to a response rate of 5.6%, mostly from Europe and North America. Overall, 197 neurosurgeons (48.5%) reported having used robotic technology in clinical practice. The highest rates of adoption of robotics were observed for Europe (54%) and North America (51%). Apart from geographical region, only age under 30, female gender, and absence of a non-academic setting were significantly associated with clinical use of robotics. The Mazor family (32%) and ROSA (26%) robots were most commonly reported among robot users. Our study provides a worldwide overview of neurosurgical adoption of robotic technology. Almost half of the surveyed neurosurgeons reported having clinical experience with at least one robotic system. Ongoing and future trials should aim to clarify superiority or non-inferiority of neurosurgical robotic applications and balance these potential benefits with considerations on acquisition and maintenance costs

Impact of intraoperative magnetic resonance imaging on gross total resection, extent of resection, and residual tumor volume in pituitary surgery: systematic review and meta-analysis

BACKGROUND: Residual tumor tissue after pituitary adenoma surgery, is linked with additional morbidity and mortality. Intraoperative magnetic resonance imaging (ioMRI) could improve resection. We aim to assess the improvement in gross total resection (GTR), extent of resection (EOR), and residual tumor volume (RV) achieved using ioMRI. METHODS: A systematic review was carried out on PubMed/MEDLINE to identify any studies reporting intra- and postoperative (1) GTR, (2) EOR, or (3) RV in patients who underwent resection of pituitary adenomas with ioMRI. Random effects meta-analysis of the rate of improvement after ioMRI for these three surgical outcomes was intended. RESULTS: Among 34 included studies (2130 patients), the proportion of patients with conversion to GTR (∆GTR) after ioMRI was 0.19 (95% CI 0.15-0.23). Mean ∆EOR was + 9.07% after ioMRI. Mean ∆RV was 0.784 cm$^{3}$. For endoscopically treated patients, ∆GTR was 0.17 (95% CI 0.09-0.25), while microscopic ∆GTR was 0.19 (95% CI 0.15-0.23). Low-field ioMRI studies demonstrated a ∆GTR of 0.19 (95% CI 0.11-0.28), while high-field and ultra-high-field ioMRI demonstrated a ∆GTR of 0.19 (95% CI 0.15-0.24) and 0.20 (95% CI 0.13-0.28), respectively. CONCLUSIONS: Our meta-analysis demonstrates that around one fifth of patients undergoing pituitary adenoma resection convert from non-GTR to GTR after the use of ioMRI. EOR and RV can also be improved to a certain extent using ioMRI. Endoscopic versus microscopic technique or field strength does not appear to alter the impact of ioMRI. Statistical heterogeneity was high, indicating that the improvement in surgical results due to ioMRI varies considerably by center

Machine Learning in Pituitary Surgery

Machine learning applications in neurosurgery are increasingly reported for diverse tasks such as faster and more accurate preoperative diagnosis, enhanced lesion characterization, as well as surgical outcome, complications and healthcare cost prediction. Even though the pertinent literature in pituitary surgery is less extensive with respect to other neurosurgical diseases, past research attempted to answer clinically relevant questions to better assist surgeons and clinicians. In the present chapter we review reported ML applications in pituitary surgery including differential diagnosis, preoperative lesion characterization (immunohistochemistry, cavernous sinus invasion, tumor consistency), surgical outcome and complication predictions (gross total resection, tumor recurrence, and endocrinological remission, cerebrospinal fluid leak, postoperative hyponatremia). Moreover, we briefly discuss from a practical standpoint the current barriers to clinical translation of machine learning research. On the topic of pituitary surgery, published reports can be considered mostly preliminary, requiring larger training populations and strong external validation. Thoughtful selection of clinically relevant outcomes of interest and transversal application of model development pipeline-together with accurate methodological planning and multicenter collaborations-have the potential to overcome current limitations and ultimately provide additional tools for more informed patient management

Topographic brain tumor anatomy drives seizure risk and enables machine learning based prediction

OBJECTIVE The aim of this study was to identify relevant risk factors for epileptic seizures upon initial diagnosis of a brain tumor and to develop and validate a machine learning based prediction to allow for a tailored risk-based antiepileptic therapy. METHODS Clinical, electrophysiological and high-resolution imaging data was obtained from a consecutive cohort of 1051 patients with newly diagnosed brain tumors. Factor-associated seizure risk difference allowed to determine the relevance of specific topographic, demographic and histopathologic variables available at the time of diagnosis for seizure risk. The data was divided in a 70/30 ratio into a training and test set. Different machine learning based predictive models were evaluated before a generalized additive model (GAM) was selected considering its traceability while maintaining high performance. Based on a clinical stratification of the risk factors, three different GAM were trained and internally validated. RESULTS A total of 923 patients had full data and were included. Specific topographic anatomical patterns that drive seizure risk could be identified. The involvement of allopallial, mesopallial or primary motor/somatosensory neopallial structures by brain tumors results in a significant and clinically relevant increase in seizure risk. While topographic input was most relevant for the GAM, the best prediction was achieved by a combination of topographic, demographic and histopathologic information (Validation: AUC: 0.79, Accuracy: 0.72, Sensitivity: 0.81, Specificity: 0.66). CONCLUSIONS This study identifies specific phylogenetic anatomical patterns as epileptic drivers. A GAM allowed the prediction of seizure risk using topographic, demographic and histopathologic data achieving fair performance while maintaining transparency