Glioblastoma, a highly aggressive primary brain tumor, is associated with poor patient outcomes. Although magnetic resonance imaging (MRI) plays a critical role in diagnosing, characterizing, and forecasting glioblastoma progression, public MRI repositories present significant drawbacks, including insufficient postoperative and follow-up studies as well as expert tumor segmentations. To address these issues, we present the “Río Hortega University Hospital Glioblastoma Dataset (RHUH-GBM),” a collection of multiparametric MRI images, volumetric assessments, molecular data, and survival details for glioblastoma patients who underwent total or near-total enhancing tumor resection. The dataset features expert-corrected segmentations of tumor subregions, offering valuable ground truth data for developing algorithms for postoperative and follow-up MRI scans.

The globally accepted surgical strategy in glioblastomas is removing the enhancing tumor. However, the peritumoral region harbors infiltration areas responsible for future tumor recurrence. This study aimed to evaluate a predictive model that identifies areas of future recurrence using a voxel-based radiomics analysis of magnetic resonance imaging (MRI) data. This multi-institutional study included a retrospective analysis of patients diagnosed with glioblastoma who underwent surgery with complete resection of the enhancing tumor. Fifty-five patients met the selection criteria. The study sample was split into training (N = 40) and testing (N = 15) datasets. Follow-up MRI was used for ground truth definition, and postoperative structural multiparametric MRI was used to extract voxel-based radiomic features. Deformable coregistration was used to register the MRI sequences for each patient, followed by segmentation of the peritumoral region in the postoperative scan and the enhancing tumor in the follow-up scan. Peritumoral voxels overlapping with enhancing tumor voxels were labeled as recurrence, while non-overlapping voxels were labeled as nonrecurrence. Voxel-based radiomic features were extracted from the peritumoral region. Four machine learning-based classifiers were trained for recurrence prediction. A region-based evaluation approach was used for model evaluation. The Categorical Boosting (CatBoost) classifier obtained the best performance on the testing dataset with an average area under the curve (AUC) of 0.81 ± 0.09 and an accuracy of 0.84 ± 0.06, using region-based evaluation. There was a clear visual correspondence between predicted and actual recurrence regions. We have developed a method that accurately predicts the region of future tumor recurrence in MRI scans of glioblastoma patients. This could enable the adaptation of surgical and radiotherapy treatment to these areas to potentially prolong the survival of these patients.

Anterior clinoidectomy is a difficult yet essential technique in skull base surgery. Two main techniques (extradural and intradural) with multiple modifications have been proposed to increase efficiency and avoid complications. In this study, the authors sought to develop a hybrid technique based on localization of the optic strut (OS) to combine the advantages and avoid the disadvantages of both techniques.Ten cadaveric specimens were prepared for surgical simulation. After a standard pterional craniotomy, the anterior clinoid process (ACP) was resected in 2 steps. The segment anterior to the OS was resected extradurally, while the segment posterior to the OS was resected intradurally. The proposed technique was performed in 6 clinical cases to evaluate its safety and efficiency.Anterior clinoidectomy was successfully performed in all cadaveric specimens and all 6 patients by using the proposed technique. The extradural phase enabled early decompression of the optic nerve while avoiding the adjacent internal carotid artery. The OS was drilled intradurally under direct visualization of the adjacent neurovascular structures. The described landmarks were easily identifiable and applicable in the surgically treated patients. No operative complication was encountered.A proposed 2-step hybrid technique combines the advantages of the extradural and intradural techniques while avoiding their disadvantages. This technique allows reduced intradural drilling and subarachnoid bone dust deposition. Moreover, the most critical part of the clinoidectomy—that is, drilling of the OS and removal of the body of the ACP—is left for the intradural phase, when critical neurovascular structures can be directly viewed.

Radiomics, in combination with artificial intelligence, has emerged as a powerful tool for the development of predictive models in neuro-oncology. Our study aims to find an answer to a clinically relevant question: is there a radiomic profile that can identify glioblastoma (GBM) patients with short-term survival after complete tumor resection? A retrospective study of GBM patients who underwent surgery was conducted in two institutions between January 2019 and January 2020, along with cases from public databases. Cases with gross total or near total tumor resection were included. Preoperative structural multiparametric magnetic resonance imaging (mpMRI) sequences were pre-processed, and a total of 15,720 radiomic features were extracted. After feature reduction, machine learning-based classifiers were used to predict early mortality (<6 months). Additionally, a survival analysis was performed using the random survival forest (RSF) algorithm. A total of 203 patients were enrolled in this study. In the classification task, the naive Bayes classifier obtained the best results in the test data set, with an area under the curve (AUC) of 0.769 and classification accuracy of 80%. The RSF model allowed the stratification of patients into low- and high-risk groups. In the test data set, this model obtained values of C-Index = 0.61, IBS = 0.123 and integrated AUC at six months of 0.761. In this study, we developed a reliable predictive model of short-term survival in GBM by applying open-source and user-friendly computational means. These new tools will assist clinicians in adapting our therapeutic approach considering individual patient characteristics.

OBJECTIVE: Reliable tools are lacking to predict shunt-dependent hydrocephalus (SDHC) development after aneurysmal subarachnoid hemorrhage (aSAH). Quantitative volumetric measurement of hemorrhagic blood is a good predictor of SDHC but might be impractical in the clinical setting. Qualitative assessment performed using scales such as the modified Fisher scale (mFisher) and the original Graeb scale (oGraeb) is easier to conduct but provides limited predictive power. In between, the modified Graeb scale (mGraeb) keeps the simplicity of the qualitative scales yet adds assessment of acute hydrocephalus, which might improve SDHC-predicting capabilities. In this study the authors investigated the likely capabilities of the mGraeb and compared them with previously validated methods. This research also aimed to define a tailored mGraeb cutoff point for SDHC prediction.METHODS: The authors performed retrospective analysis of patients admitted to their institution with the diagnosis of aSAH between May 2013 and April 2016. Out of 168 patients, 78 were included for analysis after the application of predefined exclusion criteria. Univariate and multivariate analyses were conducted to evaluate the use of all 4 methods (quantitative volumetric assessment and the mFisher, oGraeb, and mGraeb scales) to predict the likelihood of SDHC development based on clinical data and blood amount assessment on initial CT scans.RESULTS: The mGraeb scale was demonstrated to be the most robust predictor of SDHC, with an area under the curve (AUC) of 0.848 (95% CI 0.763–0.933). According to the AUC results, the performance of the mGraeb scale was significantly better than that of the oGraeb scale (χ² = 4.49; p = 0.034) and mFisher scale (χ² = 7.21; p = 0.007). No statistical difference was found between the AUCs of the mGraeb and the quantitative volumetric measurement models (χ² = 12.76; p = 0.23), but mGraeb proved to be the simplest model since it showed the lowest Akaike information criterion (66.4), the lowest Bayesian information criterion (71.2), and the highest R²_Nagelkerke coefficient (39.7%). The initial mGraeb showed more than 85% specificity for predicting the development of SDHC in patients presenting with a score of 12 or more points.CONCLUSIONS: According to the authors’ data, the mGraeb scale is the simplest model that correlates well with SDHC development. Due to limited scientific evidence of treatments aimed at SDHC prevention, we propose an mGraeb score higher than 12 to identify patients at risk with high specificity. This mGraeb cutoff point might also serve as a useful prognostic tool since patients with SDHC after aSAH have worse functional outcomes.

Delayed cerebral ischemia (DCI) is a dreadful complication present in 30% of subarachnoid hemorrhage (SAH) patients. DCI prediction and prevention are burdensome in poor grade SAH patients (WFNS 4–5). Therefore, defining an optimal neuromonitoring strategy might be cumbersome. Cerebral microdialysis (CMD) offers near-real-time regional metabolic data of the surrounding brain. However, unilateral neuromonitoring strategies obviate the diffuse repercussions of SAH. To assess the utility, indications and therapeutic implications of bilateral CMD in poor grade SAH patients. Poor grade SAH patients eligible for multimodal neuromonitoring were prospectively collected. Aneurysm location and blood volume were assessed on initial Angio-CT scans. CMD probes were bilaterally implanted and maintained, at least, for 48 hours (h). Ischemic events were defined as a Lactate/Pyruvate ratio >40 and Glucose concentration <0.7 mmol/L. 16 patients were monitored for 1725 h, observing ischemic events during 260 h (15.1%). Simultaneous bilateral ischemic events were rare (5 h, 1.9%). The established threshold of ≥7 ischemic events displayed a specificity and sensitivity for DCI of 96.2% and 83.3%, respectively. Bilateral CMD is a safe and useful strategy to evaluate areas at risk of suffering DCI in SAH patients. Metabolic crises occur bilaterally but rarely simultaneously. Hence, unilateral neuromonitoring strategies underestimate the risk of infarction and the possibility to offset its consequences.