During the last few years, the superior eyelid endoscopic transorbital approach has been proposed as a new minimally invasive pathway to access skull base lesions, mostly in ophthalmologic, otolaryngologic, and maxillofacial surgeries. However, most neurosurgeons performing minimally invasive endoscopic neurosurgery do not usually employ the orbit as a surgical corridor. The authors undertook this technical and anatomical study to contribute a neurosurgical perspective, exploring the different possibilities of this novel route. Ten dissections were performed on ten formalin-fixed specimens to further refine the transorbital technique. As part of the study, the authors also report an illustrative transorbital surgery case to further detail key surgical landmarks. Herein, we would like to discuss equipment, key anatomical landmarks, and surgical skills and stress the steps and details to ensure a safe and successful procedure. We believe it could be critical to promote and encourage the neurosurgical community to overcome difficulties and ensure a successful surgery by following these key recommendations.

Background: Radiomics has emerged as a promising approach to non-invasively characterize the molecular landscape of gliomas, providing quantitative, high-dimensional data derived from routine MRI. Given the recent shift toward molecularly driven classification, radiomics may support precision oncology by predicting key genomic, epigenetic, and phenotypic alterations without the need for invasive tissue sampling. This systematic review aimed to synthesize current radiomics applications for the non-invasive prediction of molecular biomarkers in gliomas, evaluating methodological trends, performance metrics, and translational readiness. Methods: This review followed the PRISMA 2020 guidelines. A systematic search was conducted in PubMed, Ovid MEDLINE, and Scopus on 10 January 2025, and updated on 1 February 2025, using predefined MeSH terms and keywords related to glioma, radiomics, machine learning, deep learning, and molecular biomarkers. Eligible studies included original research using MRI-based radiomics to predict molecular alterations in human gliomas, with reported performance metrics. Data extraction covered study design, cohort size, MRI sequences, segmentation approaches, feature extraction software, computational methods, biomarkers assessed, and diagnostic performance. Methodological quality was evaluated using the Radiomics Quality Score (RQS), Image Biomarker Standardization Initiative (IBSI) criteria, and Newcastle–Ottawa Scale (NOS). Due to heterogeneity, no meta-analysis was performed. Results: Of 744 screened records, 70 studies met the inclusion criteria. A total of 10,324 patients were included across all studies (mean 140 patients/study, range 23–628). The most frequently employed MRI sequences were T2-weighted (59 studies, 84.3%), contrast-enhanced T1WI (53 studies, 75.7%), T1WI (50 studies, 71.4%), and FLAIR (48 studies, 68.6%); diffusion-weighted imaging was used in only 7 studies (12.8%). Manual segmentation predominated (52 studies, 74.3%), whereas automated approaches were used in 13 studies (18.6%). Common feature extraction platforms included 3D Slicer (20 studies, 28.6%) and MATLAB-based tools (17 studies, 24.3%). Machine learning methods were applied in 47 studies (67.1%), with support vector machines used in 29 studies (41.4%); deep learning models were implemented in 27 studies (38.6%), primarily convolutional neural networks (20 studies, 28.6%). IDH mutation was the most frequently predicted biomarker (49 studies, 70%), followed by ATRX (27 studies, 38.6%), MGMT methylation (8 studies, 11,4%), and 1p/19q codeletion (7 studies, 10%). Reported AUC values ranged from 0.80 to 0.99 for IDH, approximately 0.71–0.953 for 1p/19q, 0.72–0.93 for MGMT, and 0.76–0.97 for ATRX, with deep learning or hybrid pipelines generally achieving the highest performance. RQS values highlighted substantial methodological variability, and IBSI adherence was inconsistent. NOS scores indicated high-quality methodology in a limited subset of studies. Conclusions: Radiomics demonstrates strong potential for the non-invasive prediction of key glioma molecular biomarkers, achieving high diagnostic performance across diverse computational approaches. However, widespread clinical translation remains hindered by heterogeneous imaging protocols, limited standardization, insufficient external validation, and variable methodological rigor.

Virtual reality-based learning of neuroanatomy is a new feasible method to explore, visualize, and "dissect" interactively complex anatomical regions. We provide a new interactive photorealistic 3D-model of sellar region microsurgical anatomy which allows to explore side-by-side views of exocranial and endocranial surfaces with the aim of assisting young neurosurgery residents in learning microsurgical anatomy of this complex region. Four head specimens underwent endoscopic endonasal approach extended to anterior and posterior skull base to expose main bony anatomical landmarks of sellar region. The same bony structures were exposed from transcranial perspective. By using photogrammetry method, multiple photos from both endocranial and exocranial perspectives, different for angulations and depth, were captured, fused and processed through dedicated software. All relevant bony structures were clearly distinguishable in the 3D-model reconstruction, which provides several benefits in neuroanatomy learning: first, it replicates bony structures with high degrees of realism, accuracy and fidelity; in addition, it provides realistic spatial perception of depth of the visualized structures and their anatomical relationships; again, the 3D model is interactive and allows a 360 degrees self-guided tour of the reconstructed object, so that the learner can "read the bones" and their anatomical relationship from all desired points of view. Detailed knowledge of key surgical landmarks representing keyholes and/or anatomical structures to not violate is mandatory for a safer surgery, especially for complex region like skull base. Highly accurate virtual and functional neurosurgical models, like photogrammetry, can generate realistic look to further improve surgical simulators and learning neuroanatomy.

Recently, advances in molecular biology and bioinformatics have allowed a more thorough understanding of tumorigenesis in aggressive PitNETs (pituitary neuroendocrine tumors) through the identification of specific essential genes, crucial molecular pathways, regulators, and effects of the tumoral microenvironment. Target therapies have been developed to cure oncology patients refractory to traditional treatments, introducing the concept of precision medicine. Preliminary data on PitNETs are derived from preclinical studies conducted on cell cultures, animal models, and a few case reports or small case series. This study comprehensively reviews the principal pathways involved in aggressive PitNETs, describing the potential target therapies. A search was conducted on Pubmed, Scopus, and Web of Science for English papers published between 1 January 2004, and 15 June 2023. 254 were selected, and the topics related to aggressive PitNETs were recorded and discussed in detail: epigenetic aspects, membrane proteins and receptors, metalloprotease, molecular pathways, PPRK, and the immune microenvironment. A comprehensive comprehension of the molecular mechanisms linked to PitNETs’ aggressiveness and invasiveness is crucial. Despite promising preliminary findings, additional research and clinical trials are necessary to confirm the indications and effectiveness of target therapies for PitNETs.