Vikas Singh

Glioblastoma (GBM) remains one of the most treatment‐refractory brain tumors, with poor survival driven by intratumoral heterogeneity, adaptive therapy resistance, and an immunosuppressive microenvironment that conventional preclinical models have failed to capture. Although these systems offer partial biological insights, none simultaneously preserve patient‐specific tumor architecture, microenvironmental complexity, and scalability needed for translational research. Patient‐derived GBM organoids address this gap by recapitulating 3D tumor architecture, glioma stem cell (GSC) hierarchies, extracellular matrix (ECM) interactions, and clinically relevant drug response phenotypes in a human‐specific system. This review examines GBM organoid biology and engineering, from GSC isolation and ECM scaffold design to maturation, vascular integration, and immune co‐culture. We discuss established platforms including NeoCOR and GLICO, and the rationale for incorporating functional vasculature, tumor‐immune microenvironment components, blood‐brain‐barrier (BBB), and blood‐cerebrospinal fluid barrier (BCSFB) interfaces. Critically, we propose that integrating artificial‐intelligence (AI) and machine learning (ML) with GBM organoid platforms represents a major opportunity in the field. Convolutional neural networks, ML‐based multi‐omics integration, drug‐genome correlation analysis, and predictive modelling of therapy resistance can transform organoid readouts into clinically actionable decisions. This AI‐organoid framework offers a path toward real‐time, patient‐specific therapy selection, supporting precision‐oncology workflows, informing clinical trial design, and reducing GBM therapy failure.

Introduction: Neurofibromatosis type 1 (NF-1) is an autosomal dominant genetic disorder notably linked to the develop­ment of central nervous system neoplasms – predominantly low-grade glial tumours like pilocytic astrocytoma. High-grade glial neoplasms (HGG) are rarer and more prevalent in adults, with very few comprehensive studies on imaging features of the same. This study aims to investigate the imaging characteristics of HGG in patients with NF-1, to identify alarming imaging features that potentially indicate higher-grade tumours.Material and methods: Conducting a retrospective analysis, we examined histologically confirmed cases of HGG within clinically diagnosed NF-1 patients over 8 years. Our analysis scrutinised various imaging parameters, and histopathological and molecular data.Results: Eight cases of NF-1-associated HGG were identified. Predominant features included large tumour size (> 5 cm) in most (77.8%), intra-tumoral necrosis (77.8%), and moderate to marked perilesional oedema (55.55%). Notably, more than half were centred in midline structures. Molecular analysis highlighted diverse statuses of ATRX, IDH1R132H, and P53.Conclusions: This retrospective analysis of the largest single-centre dataset on imaging of HGG in NF-1 patients reported in the literature underscores that it may be more common than previously surmised. The need to look for alarming imaging indicators and raise suspicion in atypical locations like midline structures is essential for early detection and appropriate treatment.

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: Accurate prognostic models are essential for optimizing treatment strategies for glioblastoma, the most aggressive primary brain tumor. While other neuroimaging modalities have demonstrated utility in predicting overall survival (OS), intraoperative ultrasound (iUS) remains underexplored for this purpose. This study aimed to evaluate the prognostic potential of iUS radiomics in glioblastoma patients in a multi-institutional cohort. Methods: This retrospective study included patients diagnosed with glioblastoma from the multicenter Brain Tumor Intraoperative (BraTioUS) database. A single 2D iUS slice, showing the largest tumor diameter, was selected for each patient. Radiomic features were extracted and subjected to feature selection, and clinical data were collected. Using a fivefold cross-validation strategy, Cox proportional hazards models were built using radiomic features alone, clinical data alone, and their combination. Model performance was assessed via the concordance index (C-index). Results: A total of 114 patients met the inclusion criteria, with a mean age of 56.88 years, a median OS of 382 days, and a median preoperative tumor volume of 32.69 cm³. Complete tumor resection was achieved in 51.8% of the patients. In the testing cohort, the combined model achieved a mean C-index of 0.87 (95% CI: 0.76–0.98), outperforming the radiomic model (C-index: 0.72, 95% CI: 0.57–0.86) and the clinical model (C-index: 0.73, 95% CI: 0.60–0.87). Conclusions: Intraoperative ultrasound relies on acoustic properties for tissue characterization, capturing unique features of glioblastomas. This study demonstrated that radiomic features derived from this imaging modality have the potential to support the development of survival models.

Purpose: The clinico-radiological dilemma in post-treatment high-grade gliomas, between disease recurrence (TR) and treatment-related changes (TRC), still persists. FET (Fluoro-ethyl-tyrosine) PET has been extensively used as problem-solving modality for cases where MR imaging is inconclusive. We incorporated a systematic imaging and clinical follow-up algorithm in a multi-disciplinary clinic (MDC) setting to analyse our cohort of FET PET in post-treatment gliomas. Methods: We retrospectively analyzed 171 patients of post-treatment grade III and IV glioma with equivocal findings on MRI. 185–222 MBq of 18 F-FET was injected and dedicated static imaging of brain was performed at 20 min. TBR (Tumor to background ratio) was used as semi-quantitative parameter. Cutoff of 2.5 was used for image interpretation. Imaging findings were confirmed with histopathological diagnosis, wherever available or in a multidisciplinary joint clinic based on serial imaging. Results: 121 of 171 patients showed recurrent disease on FET PET, on follow up, 109 were confirmed with recurrence; 7 patients showed TRC, whereas 5 were treated with bevacizumab, with no further clinico-radiological deterioration, thus confirming TRC. 50 patients showed TRC on FET PET, on follow up on follow up, 40 were confirmed as true-negative. 10 patients who showed TBR less than 2.5 had confirmed TR on subsequent MR imaging. The overall sensitivity and specificity was 91.6 and 76.9% respectively, with a diagnostic accuracy of 87.13%. Conclusion: There is potential for FET PET to be used along with MRI in the post treatment algorithm of high-grade glial tumors.

Background: Re-irradiation (ReRT) is an effective treatment modality in appropriately selected patients with recurrent/progressive high-grade glioma (HGG). The literature is limited regarding recurrence patterns following ReRT, which was investigated in the current study. Methods: Patients with available radiation (RT) contours, dosimetry, and imaging-based evidence of recurrence were included in the retrospective study. All patients were treated with fractionated focal conformal RT. Recurrence was detected on imaging with magnetic resonance imaging (MRI) and/ or amino-acid positron emission tomography (PET), which was co-registered with the RT planning dataset. Failure patterns were classified as central, marginal, and distant if >80%, 20–80%, or <20% of the recurrence volumes were within 95% isodose lines, respectively. Results: Thirty-seven patients were included in the current analysis. A total of 92% of patients had undergone surgery before ReRT, and 84% received chemotherapy. The median time to recurrence was 9 months. Central, marginal, and distant failures were seen in 27 (73%), 4 (11%), and 6 (16%) patients, respectively. None of the patient-, disease-, or treatment-related factors were significantly different across different recurrence patterns. Conclusion: Failures are seen predominantly within the high-dose region following ReRT in recurrent/ progressive HGG.