Role of microsurgical tumor burden reduction in patients with breast cancer brain metastases considering molecular subtypes: a two-center volumetric survival analysis.

BACKGROUND: Advancements in metastatic breast cancer (BC) treatment have enhanced overall survival (OS), leading to increased rates of brain metastases (BM). This study analyzes the association between microsurgical tumor reduction and OS in patients with BCBM, considering tumor molecular subtypes and perioperative treatment approaches. METHODS: Retrospective analysis of surgically treated patients with BCBM from two tertiary brain tumor Swiss centers. The association of extent of resection (EOR), gross-total resection (GTR) achievement, and postoperative residual tumor volume (RV) with OS and intracranial progression-free survival (IC-PFS) was evaluated using Cox proportional hazard model. RESULTS: 101 patients were included in the final analysis, most patients (38%) exhibited HER2-/HR + BC molecular subtype, followed by HER2 + /HR + (25%), HER2-/HR- (21%), and HER2 + /HR- subtypes (13%). The majority received postoperative systemic treatment (75%) and radiotherapy (84%). Median OS and intracranial PFS were 22 and 8 months, respectively. The mean pre-surgery intracranial tumor volume was 26 cm3, reduced to 3 cm3 post-surgery. EOR, GTR achievement and RV were not significantly associated with OS or IC-PFS, but higher EOR and lower RV correlated with extended OS in patients without extracranial metastases. HER2-positive tumor status was associated with longer OS, extracranial metastases at BM diagnosis and symptomatic lesions with shorter OS and IC-PFS. CONCLUSIONS: Our study found that BC molecular subtypes, extracranial disease status, and BM-related symptoms were associated with OS in surgically treated patients with BCBM. Additionally, while extensive resection to minimize residual tumor volume did not significantly affect OS across the entire cohort, it appeared beneficial for patients without extracranial metastases.

From molecular signatures to radiomics: tailoring neurooncological strategies through forecasting of glioma growth.

OBJECTIVE: Contemporary oncological paradigms for adjuvant treatment of low- and intermediate-grade gliomas are often guided by a limited array of parameters, overlooking the dynamic nature of the disease. The authors' aim was to develop a comprehensive multivariate glioma growth model based on multicentric data, to facilitate more individualized therapeutic strategies. METHODS: Random slope models with subject-specific random intercepts were fitted to a retrospective cohort of grade II and III gliomas from the database at Kepler University Hospital (n = 191) to predict future mean tumor diameters. Deep learning-based radiomics was used together with a comprehensive clinical dataset and evaluated on an external prospectively collected validation cohort from University Hospital Zurich (n = 9). Prediction quality was assessed via mean squared prediction error. RESULTS: A mean squared prediction error of 0.58 cm for the external validation cohort was achieved, indicating very good prognostic value. The mean ± SD time to adjuvant therapy was 28.7 ± 43.3 months and 16.1 ± 14.6 months for the training and validation cohort, respectively, with a mean of 6.2 ± 5 and 3.6 ± 0.7, respectively, for number of observations. The observed mean tumor diameter per year was 0.38 cm (95% CI 0.25-0.51) for the training cohort, and 1.02 cm (95% CI 0.78-2.82) for the validation cohort. Glioma of the superior frontal gyrus showed a higher rate of tumor growth than insular glioma. Oligodendroglioma showed less pronounced growth, anaplastic astrocytoma-unlike anaplastic oligodendroglioma-was associated with faster tumor growth. Unlike the impact of extent of resection, isocitrate dehydrogenase (IDH) had negligible influence on tumor growth. Inclusion of radiomics variables significantly enhanced the prediction performance of the random slope model used. CONCLUSIONS: The authors developed an advanced statistical model to predict tumor volumes both pre- and postoperatively, using comprehensive data prior to the initiation of adjuvant therapy. Using radiomics enhanced the precision of the prediction models. Whereas tumor extent of resection and topology emerged as influential factors in tumor growth, the IDH status did not. This study emphasizes the imperative of advanced computational methods in refining personalized low-grade glioma treatment, advocating a move beyond traditional paradigms.

A critical moment in machine learning in medicine: on reproducible and interpretable learning.

Over the past two decades, advances in computational power and data availability combined with increased accessibility to pre-trained models have led to an exponential rise in machine learning (ML) publications. While ML may have the potential to transform healthcare, this sharp increase in ML research output without focus on methodological rigor and standard reporting guidelines has fueled a reproducibility crisis. In addition, the rapidly growing complexity of these models compromises their interpretability, which currently impedes their successful and widespread clinical adoption. In medicine, where failure of such models may have severe implications for patients' health, the high requirements for accuracy, robustness, and interpretability confront ML researchers with a unique set of challenges. In this review, we discuss the semantics of reproducibility and interpretability, as well as related issues and challenges, and outline possible solutions to counteracting the "black box". To foster reproducibility, standard reporting guidelines need to be further developed and data or code sharing encouraged. Editors and reviewers may equally play a critical role by establishing high methodological standards and thus preventing the dissemination of low-quality ML publications. To foster interpretable learning, the use of simpler models more suitable for medical data can inform the clinician how results are generated based on input data. Model-agnostic explanation tools, sensitivity analysis, and hidden layer representations constitute further promising approaches to increase interpretability. Balancing model performance and interpretability are important to ensure clinical applicability. We have now reached a critical moment for ML in medicine, where addressing these issues and implementing appropriate solutions will be vital for the future evolution of the field.

Intraoperative ultrasonography in microsurgical resection of vestibular schwannomas via retrosigmoid approach: surgical technique and proof-of-concept illustrative case series.

PURPOSE: Intraoperative ultrasonography (ioUS) is an established tool for the real-time intraoperative orientation and resection control in intra-axial oncological neurosurgery. Conversely, reports about its implementation in the resection of vestibular schwannomas (VS) are scarce. The aim of this study is to describe the role of ioUS in microsurgical resection of VS. METHODS: ioUS (Craniotomy Transducer N13C5, BK5000, B Freq 8 MHz, BK Medical, Burlington, MA, USA) is integrated into the surgical workflow according to a 4-step protocol (transdural preresection, intradural debulking control, intradural resection control, transdural postclosure). Illustrative cases of patients undergoing VS resection through a retrosigmoid approach with the use of ioUS are showed to illustrate advantages and pitfalls of the technique. RESULTS: ioUS allows clear transdural identification of the VS and its relationships with surgically relevant structures of the posterior fossa and of the cerebellopontine cistern prior to dural opening. Intradural ioUS reliably estimates the extent of tumor debulking, thereby helping in the choice of the right moment to start peripheral preparation and in the optimization of the extent of resection in those cases where subtotal resection is the ultimate goal of surgery. Transdural postclosure ioUS accurately depicts surgical situs. CONCLUSION: ioUS is a cost-effective, safe, and easy-to-use intraoperative adjunctive tool that can provide a significant assistance during VS surgery. It can potentially improve patient safety and reduce complication rates. Its efficacy on clinical outcomes, operative time, and complication rate should be validated in further studies.

Safety of microneurosurgical interventions for superficial and deep-seated brain metastases: single-center cohort study of 637 consecutive cases.

PURPOSE: Microneurosurgical techniques have greatly improved over the past years due to the introduction of new technology and surgical concepts. To reevaluate the role of micro-neurosurgery in brain metastases (BM) resection in the era of new systemic and local treatment options, its safety profile needs to be reassessed. The aim of this study was to analyze the rate of adverse events (AEs) according to a systematic, comprehensive and reliably reproducible grading system after microneurosurgical BM resection in a large and modern microneurosurgical series with special emphasis on anatomical location. METHODS: Prospectively collected cases of BM resection between 2013 and 2022 were retrospectively analyzed. Number of AEs, defined as any deviations from the expected postoperative course according to Clavien-Dindo-Grade (CDG) were evaluated. Patient, surgical, and lesion characteristics, including exact anatomic tumor locations, were analyzed using uni- and multivariate logistic regression and survival analysis to identify predictive factors for AEs. RESULTS: We identified 664 eligible patients with lung cancer being the most common primary tumor (44%), followed by melanoma (25%) and breast cancer (11%). 29 patients (4%) underwent biopsy only whereas BM were resected in 637 (96%) of cases. The overall rate of AEs was 8% at discharge. However, severe AEs (≥ CDG 3a; requiring surgical intervention under local/general anesthesia or ICU treatment) occurred in only 1.9% (n = 12) of cases with a perioperative mortality of 0.6% (n = 4). Infratentorial tumor location (OR 5.46, 95% 2.31-13.8, p = .001), reoperation (OR 2.31, 95% 1.07-4.81, p = .033) and central region tumor location (OR 3.03, 95% 1.03-8.60) showed to be significant predictors in a multivariate analysis for major AEs (CDG ≥ 2 or new neurological deficits). Neither deep supratentorial nor central region tumors were associated with more major AEs compared to convexity lesions. CONCLUSIONS: Modern microneurosurgical resection can be considered an excellent option in the management of BM in terms of safety, as the overall rate of major AEs are very rare even in eloquent and deep-seated lesions.

Anatomical phenotyping and staging of brain tumours.

Unlike other tumours, the anatomical extent of brain tumours is not objectified and quantified through staging. Staging systems are based on understanding the anatomical sequence of tumour progression and its relationship to histopathological dedifferentiation and survival. The aim of this study was to describe the spatiotemporal phenotype of the most frequent brain tumour entities, to assess the association of anatomical tumour features with survival probability and to develop a staging system for WHO grade 2 and 3 gliomas and glioblastoma. Anatomical phenotyping was performed on a consecutive cohort of 1000 patients with first diagnosis of a primary or secondary brain tumour. Tumour probability in different topographic, phylogenetic and ontogenetic parcellation units was assessed on preoperative MRI through normalization of the relative tumour prevalence to the relative volume of the respective structure. We analysed the spatiotemporal tumour dynamics by cross-referencing preoperative against preceding and subsequent MRIs of the respective patient. The association between anatomical phenotype and outcome defined prognostically critical anatomical tumour features at diagnosis. Based on a hypothesized sequence of anatomical tumour progression, we developed a three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma. This staging system was validated internally in the original cohort and externally in an independent cohort of 300 consecutive patients. While primary CNS lymphoma showed highest probability along white matter tracts, metastases enriched along terminal arterial flow areas. Neuroepithelial tumours mapped along all sectors of the ventriculocortical axis, while adjacent units were spared, consistent with a transpallial behaviour within phylo-ontogenetic radial units. Their topographic pattern correlated with morphogenetic processes of convergence and divergence of radial units during phylo- and ontogenesis. While a ventriculofugal growth dominated in neuroepithelial tumours, a gradual deviation from this neuroepithelial spatiotemporal behaviour was found with progressive histopathological dedifferentiation. The proposed three-level staging system for WHO grade 2 and 3 gliomas and glioblastoma correlated with the degree of histological dedifferentiation and proved accurate in terms of survival upon both internal and external validation. In conclusion, this study identified specific spatiotemporal phenotypes in brain tumours through topographic probability and growth pattern assessment. The association of anatomical tumour features with survival defined critical steps in the anatomical sequence of neuroepithelial tumour progression, based on which a staging system for WHO grade 2 and 3 gliomas and glioblastoma was developed and validated.

Early versus delayed mobilization after aneurysmal subarachnoid hemorrhage: a systematic review and meta-analysis of efficacy and safety.

OBJECTIVE: A central tenet of Enhanced Recovery After Surgery (ERAS) is evidence-based medicine. Survivors of aneurysmal subarachnoid hemorrhage (aSAH) constitute a fragile patient population prone to prolonged hospitalization within neurointensive care units (NICUs), prolonged immobilization, and a range of nosocomial adverse events. Potentially, well-monitored early mobilization (EM) could constitute a beneficial element of ERAS protocols in this population. Therefore, the objective was to summarize the available evidence on EM strategies in patients with aSAH. METHODS: The authors retrieved prospective and retrospective studies that reported efficacy or safety data on EM (defined as EM in the NICU starting ≤ 7 days after ictus) versus delayed mobilization (DM) (any strategy that comparatively delayed mobilization) after aSAH and were published after January 1, 2000, in PubMed/MEDLINE, Embase, and the Cochrane Library. Random-effects meta-analysis was performed. RESULTS: Ten studies analyzing 1292 patients were included for quantitative synthesis, including 1 randomized, 1 prospective nonrandomized, and 8 retrospective studies. Modified Rankin Scale scores at discharge were not different between the EM and DM groups (mean difference [MD] [95% CI] -0.86 [-2.93 to 1.20] points, p = 0.41). Hospital length of stay in days was markedly reduced in the EM group (MD [95% CI] -6.56 [-10.64 to -2.47] days, p = 0.002). Although there was a statistically significant reduction in radiological vasospasms (OR [95% CI] 0.65 [0.44-0.97], p = 0.03), the reduction in clinically relevant vasospasms was nonsignificant (OR [95% CI] 0.63 [0.31-1.26], p = 0.19). The odds of shunting were significantly lower in the EM group (OR [95% CI] 0.61 [0.39-0.95], p = 0.03). The rates of mortality, pneumonia, and thrombosis were similar among groups (p > 0.05). CONCLUSIONS: Due to a lack of high-quality studies, vastly varying protocols, and resulting statistical clinical and statistical heterogeneity, the level of evidence for recommendations regarding EM in patients with aSAH remains low. The currently available data indicated that mobilization within the first 5 days after aneurysm repair was feasible and safe without significant excessive adverse events, that neurological outcome with EM was almost certainly not worse than with prolonged immobilization, and that there was likely at least some reduction in length of hospital stay. Radiological and clinical vasospasms were not more frequent-with signals even trending toward a decrease-in patients who mobilized early. Higher-quality studies and implementation of full ERAS protocols are necessary to evaluate efficacy and safety with a higher level of evidence and to guide practical implementation through increased standardization. Clinical trial registration no.: CRD42023432828 (www.crd.york.ac.uk/prospero).

Influence of gender and sexual hormones on outcomes after pituitary surgery: a systematic review and meta-analysis.

BACKGROUND: Although there is an increasing body of evidence showing gender differences in various medical domains as well as presentation and biology of pituitary adenoma (PA), gender differences regarding outcome of patients who underwent transsphenoidal resection of PA are poorly understood. The aim of this study was to identify gender differences in PA surgery. METHODS: The PubMed/MEDLINE database was searched up to April 2023 to identify eligible articles. Quality appraisal and extraction were performed in duplicate. RESULTS: A total of 40 studies including 4989 patients were included in this systematic review and meta-analysis. Our analysis showed odds ratio of postoperative biochemical remission in males vs. females of 0.83 (95% CI 0.59-1.15, P = 0.26), odds ratio of gross total resection in male vs. female patients of 0.68 (95% CI 0.34-1.39, P = 0.30), odds ratio of postoperative diabetes insipidus in male vs. female patients of 0.40 (95% CI 0.26-0.64, P < 0.0001), and a mean difference of preoperative level of prolactin in male vs. female patients of 11.62 (95% CI - 119.04-142.27, P = 0.86). CONCLUSIONS: There was a significantly higher rate of postoperative DI in female patients after endoscopic or microscopic transsphenoidal PA surgery, and although there was some data in isolated studies suggesting influence of gender on postoperative biochemical remission, rate of GTR, and preoperative prolactin levels, these findings could not be confirmed in this meta-analysis and demonstrated no statistically significant effect. Further research is needed and future studies concerning PA surgery should report their data by gender or sexual hormones and ideally further assess their impact on PA surgery.

Ex vivo and in vivo evaluation of transsphenoidal Liqoseal application to prevent cerebrospinal fluid leakage.

BACKGROUND: Despite improvements in closure techniques by using a vital nasoseptal flap, the use of sealing materials, and improved neurosurgical techniques, cerebrospinal fluid (CSF) leak after transsphenoidal surgery still is a clinically relevant problem. Liqoseal® (Polyganics bv, Groningen, The Netherlands) is a CE-approved bioresorbable sealant patch for use as an adjunct to standard methods of cranial dural closure to prevent CSF leakage. This study aims to evaluate the application of Liqoseal in transsphenoidal surgery ex vivo and in vivo. METHODS: 1. We created an ex vivo setup simulating the sphenoidal anatomy, using a fluid pump and porcine dura positioned on a conus with the anatomical dimensions of the sella to evaluate whether the burst pressure of Liqoseal applied to a bulging surface was above physiological intracranial pressure. Burst pressure was measured with a probe connected to dedicated computer software. Because of the challenging transsphenoidal environment, we tested in 4 groups with varying compression weight and time for the application of Liqoseal. 2. We subsequently describe the application of Liqoseal® in 3 patients during transsphenoidal procedures with intraoperative CSF leakage to prevent postoperative CSF leakage. RESULTS: 1. Ex vivo: The overall mean burst pressure in the transsphenoidal setup was 231 (± 103) mmHg. There was no significant difference in mean burst pressure between groups based on application weight and time (p = 0.227). 2. In Vivo: None of the patients had a postoperative CSF leak. No nose passage problems were observed. One patient had a postoperative meningitis and ventriculitis, most likely related to preoperative extensive CSF leakage. Postoperative imaging did not show any local infection, swelling, or other device-related adverse effects. CONCLUSIONS: We assess the use of Liqoseal® to seal a dural defect during an endoscopic transsphenoidal procedure as to be likely safe and potentially effective.

DeepEOR: automated perioperative volumetric assessment of variable grade gliomas using deep learning.

PURPOSE: Volumetric assessments, such as extent of resection (EOR) or residual tumor volume, are essential criterions in glioma resection surgery. Our goal is to develop and validate segmentation machine learning models for pre- and postoperative magnetic resonance imaging scans, allowing us to assess the percentagewise tumor reduction after intracranial surgery for gliomas. METHODS: For the development of the preoperative segmentation model (U-Net), MRI scans of 1053 patients from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2021 as well as from patients who underwent surgery at the University Hospital in Zurich were used. Subsequently, the model was evaluated on a holdout set containing 285 images from the same sources. The postoperative model was developed using 72 scans and validated on 45 scans obtained from the BraTS 2015 and Zurich dataset. Performance is evaluated using Dice Similarity score, Jaccard coefficient and Hausdorff 95%. RESULTS: We were able to achieve an overall mean Dice Similarity Score of 0.59 and 0.29 on the pre- and postoperative holdout sets, respectively. Our algorithm managed to determine correct EOR in 44.1%. CONCLUSION: Although our models are not suitable for clinical use at this point, the possible applications are vast, going from automated lesion detection to disease progression evaluation. Precise determination of EOR is a challenging task, but we managed to show that deep learning can provide fast and objective estimates.

Promotion of a neurosurgical academic journal on social media: a 1-year experience.

BACKGROUND: Social media (SoMe) use, in all of its forms, has seen massively increased throughout the past two decades, including academic publishing. Many journals have established a SoMe presence, yet the influence of promotion of scientific publications on their visibility and impact remains poorly studied. The European Journal of Neurosurgery «Acta Neurochirurgica¼ has established its SoMe presence in form of a Twitter account that regularly promotes its publications. We aim to analyze the impact of this initial SoMe campaign on various alternative metrics (altmetrics). METHODS: A retrospective analysis of all articles published in the journal Acta Neurochirurgica between May 1st, 2018, and April 30th, 2020, was performed. These articles were divided into a historical control group - containing the articles published between May 1st, 2018, and April 30th, 2019, when the SoMe campaign was not yet established - and into an intervention group. Several altmetrics were analyzed, along with website visits and PDF downloads per month. RESULTS: In total, 784 articles published during the study period, 128 (16.3%) were promoted via Twitter. During the promotion period, 29.7% of published articles were promoted. Overall, the published articles reached a mean of 31.3 ± 50.5 website visits and 17.5 ± 31.25 PDF downloads per month. Comparing the two study periods, no statistically significant differences in website visits (26.91 ± 32.87 vs. 34.90 ± 61.08, p = 0.189) and PDF downloads (17.52 ± 31.25 vs. 15.33 ± 16.07, p = 0.276) were detected. However, overall compared to non-promoted articles, promoted articles were visited (48.9 ± 95.0 vs. 29.0 ± 37.0, p = 0.005) and downloaded significantly more (25.7 ± 66.7 vs. 16.6 ± 18.0, p = 0.045) when compared to those who were not promoted during the promotion period. CONCLUSIONS: We report a 1-year initial experience with promotion of a general neurosurgical journal on Twitter. Our data suggest a clear benefit of promotion on article site visits and article downloads, although no single responsible element could be determined in terms of altmetrics. The impact of SoMe promotion on other metrics, including traditional bibliometrics such as citations and journal impact factor, remains to be determined.

Rhinologic outcome of endoscopic transnasal-transsphenoidal pituitary surgery: an institutional series, systematic review, and meta-analysis.

PURPOSE: We aimed to summarize the available data on the objective rhinologic outcome after endoscopic transnasal-transsphenoidal (ETT) surgery. METHODS: Retrospective study on a consecutive cohort of treatment-naïve patients undergoing ETT pituitary gland surgery. Additionally, a systematic review and meta-analysis with focus on the rhinologic outcome, including postoperative smell function was performed. RESULTS: The institutional series incorporated 168 patients. A concomitant endoscopic septoplasty was performed in 29/168 patients (17.3%). A nasoseptal flap was used for reconstruction of large skull-base defects or high-flow CSF leaks in 4/168 (2.4%) patients. Early postoperative rhinologic complications (< 4 weeks) included epistaxis (3%), acute rhinosinusitis (1.2%) and late postoperative complications (≥ 8 weeks) comprised prolonged crusting (15.6%), symptomatic synechiae (11.9%) and septal perforation (0.6%). Postoperative smell function was not impaired (Fisher's exact test, p = 1.0). The systematic review included 19 studies on 1533 patients with a median postoperative epistaxis rate of 1.4% (IQR 1.0-2.2), a postoperative acute rhinosinusitis rate of 2.3% (IQR 2.1-3.0), a postoperative synechiae rate of 7.5% (IQR 1.8-19.1) and a postoperative septal perforation rate of 2.2% (IQR 0.5-5.4). Seven studies including a total of 206 patients reported adequate outcome measures for smell function before and after ETT surgery. Only 2/7 studies reported an impairment of smell function postoperatively, especially in patients with nasoseptal flap harvesting. CONCLUSION: Early and late postoperative rhinologic complication rates after ETT surgery for pituitary lesions seem to be low. A thorough evaluation of smell function, in particular in patients at risk for nasoseptal flap harvesting, may be an important factor in optimal postoperative care.

Postoperative progression of brain metastasis is associated with seizures.

Seizures in patients with brain metastases have an impact on morbidity and quality of life. The influence of tumor growth on the risk of seizures in these patients is not well defined. In this cohort study, we evaluated adult patients from the University Hospital of Zurich following resection of brain metastases from solid tumors, with or without preoperative seizures, at 3, 6, 9, and 12 months postoperatively. Brain magnetic resonance imaging was assessed for tumor progression using the Response Assessment in Neuro-Oncology criteria. The quarterly risk of unprovoked seizures was modeled with mixed effects logistic regression. We analyzed 444 time frames in 220 patients. Progression of brain metastases was independently associated with seizures during the respective quarterly follow-up period (odds ratio = 3.9, 95% confidence interval = 1.3-11.3, p = .014). Complete resection of brain metastases was associated with a lower risk of seizures (odds ratio = .2, 95% confidence interval = .04-.7, p = .015). Postoperative progression of brain metastases quadrupled the risk of seizures; therefore, vigorous follow-up may be useful to identify tumor progression and gauge the risk of seizures. The identification of patients at high seizure risk may have implications for treatment decisions and influence aspects of daily life. Breakthrough seizures may indicate brain metastases progression.

The extreme anterior interhemispheric transcallosal approach for pure aqueduct tumors: surgical technique and case series.

Purely aqueductal tumors represent a rare but distinct entity of neoplasms with characteristic morphology and clinical presentation. This study aims to describe the extreme anterior interhemispheric transcallosal approach as a surgical option for purely aqueductal tumors in the upper part of the cerebral aqueduct and present the surgical results. Prospectively collected data of 4 patients undergoing the extreme anterior interhemispheric transcallosal approach for purely aqueductal tumors in the upper cerebral aqueduct was analyzed. The technique is a variation of the anterior interhemispheric transcallosal approach. The callosotomy is placed at the transition between the body and genu of the corpus callosum, allowing an approach steep enough to reach through the foramen of Monro to the upper cerebral aqueduct without opening the choroidal fissure. All patients had preoperative, and intraoperative or immediate postoperative 3-T magnetic resonance imaging, and underwent examination at admission, after surgery, at discharge, and 3 months postoperatively. Patient data are reported according to common descriptive statistics. All patients harbored low-grade gliomas causing hydrocephalus. Complete resection was achieved without mortality or morbidity. All patients recovered and presented neurologically intact at the 3-month postoperative follow-up. None had recurrence or needed adjuvant therapy. The extreme anterior interhemispheric transcallosal approach proved to be effective and safe. This approach does not require manipulation of the choroidal fissure or disrupt healthy brain parenchyma (except for a small callosotomy). We propose it as an option for removing a purely aqueductal tumor in the upper cerebral aqueduct with associated hydrocephalus.

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.

Machine Intelligence in Clinical Neuroscience: Taming the Unchained Prometheus.

The democratization of machine learning (ML) through availability of open-source learning libraries, the availability of datasets in the "big data" era, increasing computing power even on mobile devices, and online training resources have both led to an explosion in applications and publications of ML in the clinical neurosciences, but has also enabled a dangerous amount of flawed analyses and cardinal methodological errors committed by benevolent authors. While powerful ML methods are nowadays available to almost anyone and can be applied after just few minutes of familiarizing oneself with these methods, that does not imply that one has mastered these techniques. This textbook for clinicians aims to demystify ML by illustrating its methodological foundations, as well as some specific applications throughout clinical neuroscience, and its limitations. While our mind can recognize, abstract, and deal with the many uncertainties in clinical practice, algorithms cannot. Algorithms must remain tools of our own mind, tools that we should be able to master, control, and apply to our advantage in an adjunctive manner. Our hope is that this book inspires and instructs physician-scientists to continue to develop the seeds that have been planted for machine intelligence in clinical neuroscience, not forgetting their inherent limitations.

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.

Foundations of Feature Selection in Clinical Prediction Modeling.

Selecting a set of features to include in a clinical prediction model is not always a simple task. The goals of creating parsimonious models with low complexity while, at the same time, upholding predictive performance by explaining a large proportion of the variance within the dependent variable must be balanced. With this aim, one must consider the clinical setting and what data are readily available to clinicians at specific timepoints, as well as more obvious aspects such as the availability of computational power and size of the training dataset. This chapter elucidates the importance and pitfalls in feature selection, focusing on applications in clinical prediction modeling. We demonstrate simple methods such as correlation-, significance-, and variable importance-based filtering, as well as intrinsic feature selection methods such as Lasso and tree- or rule-based methods. Finally, we focus on two algorithmic wrapper methods for feature selection that are commonly used in machine learning: Recursive Feature Elimination (RFE), which can be applied regardless of data and model type, as well as Purposeful Variable Selection as described by Hosmer and Lemeshow, specifically for generalized linear models.

Machine Learning and Intracranial Aneurysms: From Detection to Outcome Prediction.

Machine learning (ML) is a rapidly rising research tool in biomedical sciences whose applications include segmentation, classification, disease detection, and outcome prediction. With respect to traditional statistical methods, ML algorithms have the potential to learn and improve their predictive performance when fed with large data sets without the need of being specifically programmed. In recent years, this technology has been increasingly applied for tackling clinical issues in intracranial aneurysm (IA) research. Several studies attempted to provide reliable models for enhanced aneurysm detection. Convolutional neural networks trained with variable degrees of human interaction on data from diverse imaging modalities showed high sensitivity in aneurysm detection tasks, also outperforming expert image analysis. Algorithms were also shown to differentiate ruptured from unruptured IAs, with however limited clinical relevance. For prediction of rupture and stability assessment, ML was preliminarily shown to achieve better performance compared to conventional statistical methods and existing risk scores. ML-based complication and functional outcome prediction in the event of SAH have been more extensively reported, in contrast with periprocedural outcome investigation in unruptured IA patients. ML has the potential to be a game changer in IA patient management. Currently clinical translation of experimental results is limited.

Radiomic Features Associated with Extent of Resection in Glioma Surgery.

Radiomics defines a set of techniques for extraction and quantification of digital medical data in an automated and reproducible way. Its goal is to detect features potentially related to a clinical task, like classification, diagnosis, prognosis, and response to treatment, going beyond the intrinsic limits of operator-dependency and qualitative description of conventional radiological evaluation on a mesoscopic scale. In the field of neuro-oncology, researchers have tried to create prognostic models for a better tumor diagnosis, histological and biomolecular classification, prediction of response to treatment, and identification of disease relapse. Concerning glioma surgery, the most significant aid that radiomics can give to surgery is to improve tumor extension detection and identify areas that are more prone to recurrence to increase the extent of tumor resection, thereby ameliorating the patients' prognosis. This chapter aims to review the fundamentals of radiomics models' creation, the latest advance of radiomics in neuro-oncology, and possible radiomic features associated with the extent of resection in the brain gliomas.