Giulio Anichini

Diffuse gliomas are the commonest malignant primary brain tumour in adults. Herein, we present analysis of the genomic landscape of adult glioma, by whole genome sequencing of 403 tumours (256 glioblastoma, 89 astrocytoma, 58 oligodendroglioma; 338 primary, 65 recurrence). We identify an extended catalogue of recurrent coding and non-coding genetic mutations that represents a source for future studies and provides a high-resolution map of structural variants, copy number changes and global genome features including telomere length, mutational signatures and extrachromosomal DNA. Finally, we relate these to clinical outcome. As well as identifying drug targets for treatment of glioma our findings offer the prospect of improving treatment allocation with established targeted therapies.

Background: Intraoperative ultrasound (ioUS) provides real-time imaging during neurosurgical procedures, with advantages such as portability and cost-effectiveness. Accurate tumor segmentation has the potential to substantially enhance the interpretability of ioUS images; however, its implementation is limited by persistent challenges, including noise, artifacts, and anatomical variability. This study aims to develop a convolutional neural network (CNN) model for glioma segmentation in ioUS images via a multicenter dataset. Methods: We retrospectively collected data from the BraTioUS and ReMIND datasets, including histologically confirmed gliomas with high-quality B-mode images. For each patient, the tumor was manually segmented on the 2D slice with its largest diameter. A CNN was trained using the nnU-Net framework. The dataset was stratified by center and divided into training (70%) and testing (30%) subsets, with external validation performed on two independent cohorts: the RESECT-SEG database and the Imperial College NHS Trust London cohort. Performance was evaluated using metrics such as the Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), and 95th percentile Hausdorff distance (HD95). Results: The training cohort consisted of 197 subjects, 56 of whom were in the hold-out testing set and 53 in the external validation cohort. In the hold-out testing set, the model achieved a median DSC of 0.90, ASSD of 8.51, and HD95 of 29.08. On external validation, the model achieved a DSC of 0.65, ASSD of 14.14, and HD95 of 44.02 on the RESECT-SEG database and a DSC of 0.93, ASSD of 8.58, and HD95 of 28.81 on the Imperial-NHS cohort. Conclusions: This study supports the feasibility of CNN-based glioma segmentation in ioUS across multiple centers. Future work should enhance segmentation detail and explore real-time clinical implementation, potentially expanding ioUS’s role in neurosurgical resection.

Background: Neuroepithelial cysts (NECs) are rare entities, occasionally causing neurological symptoms that can be overlooked. Case Description: A case of an occipital neuroepithelial cyst is discussed. The initial presentation consisted of mild homonymous hemianopia and gait impairment. Conservative management was suggested to start with, but at 6 months follow-up, the patient’s symptoms were worsening. Surgery was performed under general anesthetic and using visual-evoked potentials. The cyst was marsupialized and connected with subdural space, and a few samples were sent for histological analysis. The patient experienced immediate improvement in her symptoms, and the visual tests at follow-up confirmed the resolution of the previously documented hemianopia. Conclusion: NECs should be carefully assessed to rule out symptoms associated with mass effects. This case and others reported in the international literature show that occipital neuroepithelial cysts can benefit from surgical treatment with meticulous preoperative planning. The aid of neuromonitoring is crucial to identify anatomical variations and cortical functionality that are potentially distorted in the presence of these lesions.

Background: Glioblastoma is the most common and aggressive primary brain tumour, characterised by its invasive nature and complex metabolic profile. Emerging research highlights the role of amino acids (AAs) in glioblastoma metabolism, influencing tumour growth and the surrounding microenvironment. Methods: This narrative review synthesises recent pre-clinical studies focusing on the metabolic functions of AAs in glioblastoma. Key areas include the effects of AA deprivation on tumour growth, adaptive mechanisms, and the tumour microenvironment. Results: The effects related to arginine, glutamine, methionine, and cysteine deprivation have been more extensively reported. Arginine deprivation in arginine-auxotrophic glioblastomas induces apoptosis and affects cell adhesion, while glutamine deprivation disrupts metabolic pathways and enhances autophagy. Methionine and cysteine deprivation impact lipid metabolism and ferroptosis. Tumour adaptive mechanisms present challenges, and potential compensatory responses have been identified. The response of the microenvironment to AA deprivation, including immune modulation, is critical to determining therapeutic outcomes. Conclusions: Targeting AA metabolism offers a promising approach for glioblastoma treatment, with potential targeted drugs showing clinical promise. However, the complexity of tumour adaptive mechanisms and their impact on the microenvironment necessitates further research to optimise combination therapies and improve therapeutic efficacy.

Background: We have retrospectively reviewed our series of brain tumor patients operated on using 3D IntraOperative UltraSound (IOUS) to report technical advantages and areas of improvement. Methods: Clinical and radiological data of patients with a diagnosis of high-grade glioma IV operated with and without IOUS were retrieved and analyzed. Results: We have found 391 patients operated using IOUS coupled with neuronavigation and 257 using neuronavigation standalone. We have selected a pool of 60 patients with a diagnosis of GlioBlastoma (GB), comparing two equally sized groups operated with and without IOUS, respectively. The average extent of resection (EOR) in the IOUS group was 93%, while in the control group, it was 80%. IOUS was significantly associated with improved EOR (P < 0.0004), even when accounting for other factors affecting EOR. The average overall survival (OS) was 13.4 months, and the average progression-free survival (PFS) was 7.4 months. The Cox proportional hazard model showed an advantage in OS on patients operated using the IOUS. No statistically significant effect was observed on PFS. Conclusion: Intraoperative ultrasound coupled with image guidance is associated with an improved EOR and possibly an improved OS. While we are aware of several limitations related to the present analysis, these data support the routine use of IOUS as a safe and reliable technology. Larger, prospective series with updated IOUS technology are desirable to verify the accuracy of these results.

SignificanceMueller matrix imaging (MMI) is a comprehensive form of polarization imaging useful for assessing structural changes. However, there is limited literature on the polarimetric properties of brain specimens, especially with multispectral analysis.AimWe aim to employ multispectral MMI for an exhaustive polarimetric analysis of brain structures, providing a reference dataset for future studies and enhancing the understanding of brain anatomy for clinicians and researchers.ApproachA multispectral wide-field MMI system was used to measure six fresh lamb brain specimens. Multiple decomposition methods (forward polar, symmetric, and differential) and polarization invariants (indices of polarimetric purity and anisotropy coefficients) have been calculated to obtain a complete polarimetric description of the samples. A total of 16 labels based on major brain structures, including grey matter (GM) and white matter (WM), were identified. K-nearest neighbors classification was used to distinguish between GM and WM and validate the feasibility of MMI for WM identification.ResultsAs the wavelength increases, both depolarization and retardance increase, suggesting enhanced tissue penetration into deeper layers. Moreover, utilizing multiple wavelengths allowed us to track dynamic shifts in the optical axis of retardance within the brain tissue, providing insights into morphological changes in WM beneath the cortical surface. The use of multispectral data for classification outperformed all results obtained with single-wavelength data and provided over 95% accuracy for the test dataset.ConclusionsThe consistency of these observations highlights the potential of multispectral wide-field MMI as a non-invasive and effective technique for investigating the brain’s architecture.

Intraoperative ultrasound imaging is used to facilitate safe brain tumour resection. However, due to challenges with image interpretation and the physical scanning, this tool has yet to achieve widespread adoption in neurosurgery. In this paper, we introduce the components and workflow of a novel, versatile robotic platform for intraoperative ultrasound tissue scanning in neurosurgery. An RGB-D camera attached to the robotic arm allows for automatic object localisation with ArUco markers, and 3D surface reconstruction as a triangular mesh using the ImFusion Suite software solution. Impedance controlled guidance of the US probe along arbitrary surfaces, represented as a mesh, enables collaborative US scanning, i.e., autonomous, teleoperated and hands-on guided data acquisition. A preliminary experiment evaluates the suitability of the conceptual workflow and system components for probe landing on a custom-made soft-tissue phantom. Further assessment in future experiments will be necessary to prove the effectiveness of the presented platform.

Objective: Addressing the challenges that come with identifying and delineating brain tumours in intraoperative ultrasound. Our goal is to both qualitatively and quantitatively assess the interobserver variation, amongst experienced neuro-oncological intraoperative ultrasound users (neurosurgeons and neuroradiologists), in detecting and segmenting brain tumours on ultrasound. We then propose that, due to the inherent challenges of this task, annotation by localisation of the entire tumour mass with a bounding box could serve as an ancillary solution to segmentation for clinical training, encompassing margin uncertainty and the curation of large datasets. Methods: 30 ultrasound images of brain lesions in 30 patients were annotated by 4 annotators - 1 neuroradiologist and 3 neurosurgeons. The annotation variation of the 3 neurosurgeons was first measured, and then the annotations of each neurosurgeon were individually compared to the neuroradiologist’s, which served as a reference standard as their segmentations were further refined by cross-reference to the preoperative MRI. The following statistical metrics were used: Intersection Over Union, Sørensen-Dice similarity coefficient and Hausdorff distance. These annotations were then converted into bounding boxes for the same evaluation. Results: There was a moderate level of interobserver variance between the neurosurgeons [IoU:0.789, DSC:0.876, HD:103.227] and a larger level of variance when compared against the MRI-informed reference standard annotations by the neuroradiologist, mean across annotators [IoU:0.723, DSC:0.813, HD:115.675]. After converting the segments to bounding boxes, all metrics improve, most significantly, the interquartile range drops by [IoU:37%, DSC:41%, HD:54%]. Conclusion: This study highlights the current challenges with detecting and defining tumour boundaries in neuro-oncological intraoperative brain ultra-Sound. We then show that bounding box annotation could serve as a useful complementary approach for both clinical and technical reasons.