Sergio García-García

Background: Intracranial meningiomas(IM) are often associated with peritumoral brain edema(PTBE), visible as hyperintensities on T2/FLAIR MRI. Postoperative persisting PTBE-like changes likely represent gliosis that, in turn, contributes to surgical morbidity. Since the human eye is unable to distinguish between PTBE and gliosis on MR images, we hypothesized that radiomic analysis of preoperative peritumoral T2/FLAIR hyperintensities could distinguish preoperatively established gliosis from reversible edema. Methods: MRI of patients with gross totally resected IM were retrospectively analyzed. Preoperative and 1-year postoperative PTBE were segmented on MRI. One-year MRI were classified into two categories based on whether PTBE resolution exceeded 80% of the initial volume. RF were extracted from meningioma and PTBE regions on T1-contrast-enhanced, T2, and FLAIR MRI sequences. The dataset was split into training, validation, and test cohorts(70–10-20%). Feature reduction used correlation-based exclusion and recursive feature elimination with cross-validation. Nine ML algorithms were trained and evaluated, and best model’s interpretability assessed using Shapley Additive Explanations(SHAP). Results: 644 RF were extracted per individual from the pre and postoperative MRI of 123 operated patients. The Random Forest model utilizing 10 RF achieved the best performance (accuracy:0.91;precision:0.92;F1-score:0.92;ROC-AUC:0.94), demonstrating radiomics’ utility in predicting PTBE resolution at 1-year post-surgery. SHAP analysis provided interpretability, highlighting key RF, differences between patient groups, and potential sources of algorithmic error. Conclusions: These results underscore the potential of radiomics and ML to accurately predict postoperative PTBE resolution, differentiating transient PTBE from persistent PTBE-like changes (gliosis). This study provides initial insights into the potential of advanced imaging and computational techniques for non-invasive preoperative assessment, which may contribute to more personalized surgical strategies.

Background: Brain Dural Arteriovenous Fistulae (DAVF) are acquired abnormal connections between dural arteries and cerebral veins or venous sinuses. Disconnection of the pathological shunt is recommended for high-grade fistulae and cases with intolerable symptoms or previous bleedings. Surgical disconnection remains the preferred method for anterior fossa DAVF. Method: Microsurgical disconnection of anterior fossa DAVF is performed with the assistance of a robotic exoscope. Intraoperative aniography is implemented to confirm the exclusion of DAVF. Conclusion: The exoscope provides excellent lighting and magnification in challenging surgical fields improving surgeon's ergonomics and enabling tailored, minimally invasive approaches without compromising procedural safety or effectiveness.

Background Spinal dural arteriovenous fistulae(sDAVF) represent the most common spinal vascular malformation. Therapeutic strategies include both surgical and endovascular approaches. Surgical interruption of the fistula is crucial to prevent the onset and progression of neurological symptoms. Despite a range of surgical techniques, little research has evaluated their relative efficacy. This review aims to summarize the available surgical techniques and identify open questions for future research. Methods A systematic review was conducted on PubMed, Ovid, and Web of Science databases to identify surgically treated cohorts of sDAVF. Five commonly reported surgical steps were analyzed: type of approach, temporary clipping, shunt disruption method, fistulous vein management, and epidural vessel management. In addition, open questions lacking consensus or evidence were identified for pre, intra and postoperative stages. Results 63 of 115 analyzed cohorts described surgical techniques. The most commonly used approach was single-level laminectomy (52.4%). Temporary clipping was used in 11.1% of cases, while 15.9% of studies reported excision of intradural arterialized veins. There was wide variation in the methods used to close the fistula, with the most frequent being coagulation alone (27.5%). The management of epidural vessels was reported in 11.1% of studies. Sixteen major open questions were identified with a wide variety of technical nuances. Discussion Surgical treatment of sDAVF is not a standardized procedure but rather encompasses significantly different techniques. The lack of controlled trials leaves many questions unanswered, including optimal surgical strategies and the role of adjunct imaging and monitoring techniques. Further research is required to address these gaps and refine treatment protocols.

Purpose In an era characterized by rapid progression in neurosurgical technologies, traditional tools such as the non-navigated two-dimensional intraoperative ultrasound (nn-2D-IOUS) risk being overshadowed. Against this backdrop, this study endeavors to provide a comprehensive assessment of the clinical efficacy and surgical relevance of nn-2D-IOUS, specifically in the context of glioma resections. Methods This retrospective study undertaken at a single center evaluated 99 consecutive, non-selected patients diagnosed with both high-grade and low-grade gliomas. The primary objective was to assess the proficiency of nn-2D-IOUS in generating satisfactory image quality, identifying residual tumor tissue, and its influence on the extent of resection. To validate these results, early postoperative MRI data served as the reference standard. Results The nn-2D-IOUS exhibited a high level of effectiveness, successfully generating good quality images in 79% of the patients evaluated. With a sensitivity rate of 68% and a perfect specificity of 100%, nn-2D-IOUS unequivocally demonstrated its utility in intraoperative residual tumor detection. Notably, when total tumor removal was the surgical objective, a resection exceeding 95% of the initial tumor volume was achieved in 86% of patients. Additionally, patients in whom residual tumor was not detected by nn-2D-IOUS, the mean volume of undetected tumor tissue was remarkably minimal, averaging at 0.29 cm³. Conclusion Our study supports nn-2D-IOUS's invaluable role in glioma surgery. The results highlight the utility of traditional technologies for enhanced surgical outcomes, even when compared to advanced alternatives. This is particularly relevant for resource-constrained settings and emphasizes optimizing existing tools for efficient patient care. NCT05873946 – 24/05/2023 – Retrospectively registered.

Background: Subarachnoid hemorrhage (SAH) entails high morbidity and mortality rates. Convolutional neural networks (CNN) are capable of generating highly accurate predictions from imaging data. Our objective was to predict mortality in SAH patients by processing initial CT scans using a CNN-based algorithm. Methods: We conducted a retrospective multicentric study of a consecutive cohort of patients with SAH. Demographic, clinical and radiological variables were analyzed. Preprocessed baseline CT scan images were used as the input for training using the AUCMEDI framework. Our model’s architecture leveraged a DenseNet121 structure, employing transfer learning principles. The output variable was mortality in the first three months. Results: Images from 219 patients were processed; 175 for training and validation and 44 for the model’s evaluation. Of the patients, 52% (115/219) were female and the median age was 58 (SD = 13.06) years. In total, 18.5% (39/219) had idiopathic SAH. The mortality rate was 28.5% (63/219). The model showed good accuracy at predicting mortality in SAH patients when exclusively using the images of the initial CT scan (accuracy = 74%, F1 = 75% and AUC = 82%). Conclusion: Modern image processing techniques based on AI and CNN make it possible to predict mortality in SAH patients with high accuracy using CT scan images as the only input. These models might be optimized by including more data and patients, resulting in better training, development and performance on tasks that are beyond the skills of conventional clinical knowledge.

Background: In an era characterized by rapid progression in neurosurgical technologies, traditional tools such as the non-navigated two-dimensional intraoperative ultrasound (nn-2D-IOUS) risk being overshadowed. Against this backdrop, this study endeavors to provide a comprehensive and rigorous assessment of the clinical efficacy and surgical relevance of nn-2D-IOUS, specifically in the context of glioma resections. Methods: This retrospective study undertaken at a single center evaluated 99 consecutive, non-selected patients diagnosed with both high-grade and low-grade gliomas. The primary objective was to assess the proficiency of nn-2D-IOUS in generating satisfactory image quality, identifying residual tumor tissue, and its influence on the extent of resection. To validate these results, early postoperative MRI data served as the reference standard. Results: The nn-2D-IOUS exhibited a high level of effectiveness, successfully generating good quality images in 79% of the cases evaluated. With a sensitivity rate of 68% and a perfect specificity of 100%, nn-2D-IOUS unequivocally demonstrated its utility in intraoperative tumor detection. Notably, in cases where total tumor removal was the surgical objective, a resection exceeding 95% of the initial tumor volume was achieved in 86% of patients. Additionally, in cases where residual tumor was not detected by nn-2D-IOUS, the mean volume of undetected tumor tissue was remarkably minimal, averaging at 0.29 cm ³ . Conclusion: Our study provides compelling evidence supporting the invaluable role and efficacy of nn-2D-IOUS in glioma surgery. The results underscore the potential of harnessing traditional, cost-effective technologies such as nn-2D-IOUS to achieve enhanced surgical outcomes, even in the face of more advanced alternatives. These insights carry significant implications, particularly for resource-constrained settings, emphasizing the importance of optimizing the use of existing tools to improve patient care in a practical and efficient manner.

Editorial: Assessment of intraoperative image technologies to optimize clinical outcomes in neurosurgical oncology