Search Results (69)

Background: Gamma Knife radiosurgery (GKS) is widely used for the management of intracranial disorders. Emerging evidence suggests that incorporating the biological effective dose (BED) into GKS planning may improve the prediction of treatment efficacy and toxicity. This review aims to evaluate the role of BED in GKS across multiple intracranial indications. Methods: A qualitative review of published clinical studies was performed to assess the application of BED models in GKS for pituitary adenomas, vestibular schwannomas, meningiomas, arteriovenous malformations (AVMs), trigeminal neuralgia, and other disorders. The relationships between BED, treatment outcomes, and adverse effects were compared across indications. Results: The association between BED and clinical outcomes was most consistent in AVMs, where higher BED correlated closely with obliteration rates. In other diseases, BED-based analyses showed promising but variable predictive value. Notably, BED-derived parameters demonstrated improved prediction of post-GKS hypopituitarism in pituitary adenomas and AVM obliteration compared with physical dose alone. However, most available evidence was derived from retrospective studies. Conclusions: BED may serve as a valuable complement to conventional physical dose metrics in GKS planning, but its ability to replace physical dose remains uncertain. Prospective studies and histology-specific radiobiological parameter validation are required to establish the routine clinical utility of BED. Full article

Background/Objectives: Posterior fossa brain metastases (PFBMs) pose particular risks owing to their proximity to the brainstem and fourth ventricle. We evaluated the safety (treatment-related complications), local effectiveness, and procedural efficiency of volumetric modulated arc therapy (VMAT)-based stereotactic radiosurgery (VMAT-SRS) for PFBMs. Methods: This single-centre, retrospective study derived a PFBM subgroup from an overall institutional cohort of 123 patients treated with VMAT-RapidArc SRS/fSRS. The doses were 12–20 Gy (single fraction) or 5 × 6 Gy (selected cases). Local response (mRECIST) and predefined safety endpoints (symptomatic oedema with brainstem/IV-ventricle compromise, obstructive hydrocephalus, haemorrhagic transformation, CSF diversion, and urgent neurosurgery) were assessed. Overall survival and procedural time were analysed. Results: Thirty-one patients (39 lesions) were included; 76.9% of them received single-fraction SRS. In addition, 74.2% of patients had supratentorial metastases with posterior fossa involvement. Kaplan–Meier overall survival at 6, 12, 24, and 48 months was 74%, 58%, 26%, and 9.7%, respectively; the median survival time was 12.6 months. Among evaluable lesions, local control was 84.5% (per-lesion response: 15.5% PD, 28.1% SD, 34.4% PR, and 22.0% CR). No clinically significant posterior fossa local complications were observed. Three patients developed radiation-induced leukoencephalopathy after whole-brain radiotherapy (WBRT) and radiosurgery for synchronous supratentorial metastases. The median procedural time was 25.0 min (IQR 9.0) with one isocentre versus 52.5 min (IQR 9.75) with two. Conclusions: VMAT-SRS/fSRS for PFBMs achieved high local control, very low posterior fossa toxicity, and favourable procedural efficiency, supporting its use as a safe, rapid, frameless alternative to WBRT and other radiosurgical platforms such as Gamma Knife in appropriately selected patients. Full article

Background and Clinical Significance: Meningiomas are among the most common primary intracranial tumors, usually benign and slow-growing. Extracranial extension is exceptionally rare, particularly when arising from convexity meningiomas extending into the temporal fossa. Such cases pose unique diagnostic and therapeutic challenges due to their atypical growth patterns and anatomical complexity. Case Presentation: A 63-year-old woman previously treated for a right temporal convexity meningioma with subtotal resection and Gamma Knife radiosurgery demonstrated progressive extracranial tumor growth over five years, while the intracranial component remained stable. MRI revealed infiltration of the temporalis and lateral pterygoid muscles and erosion of the temporal bone. Due to extensive extracranial involvement and limited neurosurgical accessibility, resection was performed by a maxillofacial surgical team through a preauricular approach. Intraoperatively, the tumor was encapsulated but adherent to the deep temporal fascia and zygomatic arch. The temporal branch of the facial nerve was identified and preserved. Histopathology confirmed a meningothelial meningioma, WHO Grade I, with low proliferative activity (Ki-67 < 1%). Postoperative recovery was uneventful, with transient facial nerve weakness that resolved within weeks. Conclusions: This report adds to the limited literature describing temporal fossa involvement by convexity meningiomas and illustrates the value of collaboration between neurosurgical and maxillofacial teams. Regular MRI surveillance every 6–12 months is advised for early detection of recurrence. Full article

Background: Radiation therapy is a key treatment modality for brain metastases. While providing a treatment alternative, post-treatment imaging often presents diagnostic challenges, particularly in distinguishing tumor recurrence from radiation-induced changes such as necrosis. Advanced imaging techniques and artificial intelligence (AI)-based radiomic analyses emerge as alternatives to help lesion characterization. The objective of this study was to assess the capacity of machine learning algorithms to distinguish between brain metastases recurrence and radiation necrosis. Methods: The research was conducted in two phases and used publicly available MRI data from patients treated with Gamma Knife radiosurgery. In the first phase, 30 cases of local recurrence of brain metastases and 30 cases of radiation-induced necrosis were considered. Image segmentation and radiomic feature extraction were performed on these data using MatRadiomics_1_5_3, a MATLAB-based framework integrating PyRadiomics. Features were then selected using point-biserial correlation. In the second phase, a classification was performed using a Support Vector Machine model with repeated stratified cross-validation settings. Results: The results achieved an accuracy on the test set of 83% for distinguishing metastases from necrosis. Conclusions: The results of this feasibility study demonstrate the potential of radiomics and AI to improve diagnostic accuracy and personalized care in neuro-oncology. Full article

Background/Objectives: GK plan quality is strongly affected by lesion size and shape, and the same evaluation metrics may not be directly comparable across patients with different anatomies. This study proposes a deep learning-based method to predict achievable, clinically acceptable plan quality from patient-specific geometry. Methods: A hierarchically densely connected U-Net (HD-U-Net) was trained at the lesion level to predict 3D dose distributions for the estimation of plan quality metrics, including coverage, selectivity, gradient index (GI), and conformity index at a 50% prescription dose (CI50). To improve the prediction accuracy of plan quality metrics, Dice similarity coefficient losses for the 100% and 50% isodose lines were incorporated with conventional mean squared error (MSE) loss. Results: Ten-fold cross-validation on 463 brain metastases (BMs) from 175 patients showed that our method achieved smaller mean absolute errors across all four metrics than the HD-U-Net baseline trained with MSE loss. Improvements were pronounced in all metrics for small metastases, and were observed primarily in GI and CI50 for medium and large lesions. Paired Wilcoxon signed-rank tests confirmed the statistical significance of these improvements (p < 0.05). Conclusions: The proposed method outperformed the baseline model in capturing overall trends, improving per-lesion accuracy, and enhancing robustness to dataset variability. It can serve as a pre-planning tool to guide planners in constraint setting and priority tuning, a post-planning quality control tool to identify subpar plans that could be substantially improved, and as a foundation for developing deep reinforcement learning-based automated planning of GK treatments for brain metastases. Full article

Background/Objectives: Recurrent meningiomas remain difficult to manage due to the absence of effective systemic therapies and comparatively high treatment failure rates, particularly in high-grade tumors. Stereotactic radiosurgery (SRS) offers a minimally-invasive and precise option, particularly for tumors in surgically complex locations. However, the risks associated with re-irradiation, and recent changes in the WHO classification of CNS tumors highlight the need for more personalized and strategic treatment approaches. This systematic review evaluates the safety, efficacy, and clinical considerations for use of SRS for recurrent meningiomas. Methods: In accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature search was conducted using the PubMed, Scopus, and Web of Science databases for studies reporting outcomes of SRS in recurrent, pathologically confirmed intracranial meningiomas. Studies were excluded if they were commentaries, reviews, case reports with fewer than three cases, or had inaccessible full text. The quality and risk of bias of the included studies were assessed using the modified Newcastle-Ottawa Scale. Data on patient and tumor characteristics, SRS treatment parameters, clinical outcomes, adverse effects, and statistical analysis results were extracted. Results: Sixteen studies were included. For WHO Grade I tumors, 3- to 5-year progression-free survival (PFS) ranged from 85% to 100%. Grade II meningiomas demonstrated more variable outcomes, with 3-year PFS ranging from 23% to 100%. Grade III tumors had consistently poorer outcomes, with reported 1-year and 2-year PFS rates as low as 0% and 46%, respectively. SRS performed after surgery alone was associated with superior outcomes, with local control rates of 79% to 100% and 5-year PFS ranging from 40.4% to 91%. In contrast, tumors previously treated with radiotherapy, with or without surgery, showed substantially poorer outcomes, with 3- to 5-year PFS ranging from 26% to 41% and local control rates as low as 31%. Among patients with prior radiotherapy, outcomes were particularly poor in Grade II and III recurrent tumors. Toxicity rates ranged from 3.7% to 37%, and were generally higher for patients with prior radiation. Predictors of worse PFS included prior radiation, older age, and Grade III histology. Conclusions: SRS may represent a reasonable salvage option for carefully selected patients with recurrent meningioma, particularly following surgery alone. Outcomes were notably worse in high-grade recurrent meningiomas following prior radiotherapy, emphasizing the prognostic significance of both histological grade and treatment history. Notably, the lack of molecular and genetic data in most existing studies represents a key limitation in the current literature. Future prospective studies incorporating molecular profiling may improve risk stratification and support more personalized treatment strategies. Full article

Objective: Traditional imaging modalities for the planning of Gamma Knife radiosurgery (GKRS) are non-specific and do not accurately delineate intracranial neoplasms. This study aimed to evaluate the utility of positron emission tomography (PET) for the planning of GKRS for intracranial neoplasms (ICNs) and the post-GKRS applications of PET for patient care. Methods: PubMed, Scopus, and ScienceDirect were searched in order to assemble relevant studies regarding the uses of PET in conjunction with GKRS for ICN treatment. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines were followed to identify relevant studies on the use of PET in conjunction with GKRS. Particular emphasis was placed on review articles and medical research investigating tumor delineation and post-operative care. Relevant studies were selected and assessed based on quality measures, including study design, sample size, and significance. Inclusion and exclusion criteria were used to examine the yield of the initial search (n = 105). After a secondary review, the included results were identified (n = 50). Results: This study revealed that PET imaging is highly accurate for the planning of GKRS. In fact, many cases indicate that it is more specific than traditional imaging modalities. PET is also capable of complementing traditional imaging techniques through combination imaging. This showed significant efficacy for the planning of GKRS for ICNs. Conclusions: While PET shows a multitude of applications for the treatment of ICNs with GKRS, further research is necessary to assemble a complete set of clinical guidelines for treatment specifications. Importantly, future studies need a greater standardization of methods and expanded trials with a multitude of radiotracers. Full article

Stereotactic radiosurgery (SRS) deploys image-guidance to deliver multiple beams of highly focused ionizing radiation to tightly conformed anatomical targets, leading to precise dosing of radiation-induced cellular injury and predictable biological responses that can be applied to treat a multitude of central nervous system (CNS) disorders. Herein we review the principles of CNS radiobiology, comparing differences between SRS and conventional radiation therapy. We then review the radiobiology of SRS as it pertains to the treatment of CNS tumors and vascular malformations and the emerging application of SRS for the treatment of functional and psychiatric neurological disorders. Finally, we look toward the future in combining SRS with other novel technologies to improve treatment outcomes for patients with CNS disorders. Full article

Introduction: The group of so-called “sellar-region masses” consists of a heterogeneous group of neoplasms and tumor-mimicking lesions, whose differential diagnosis may be challenging due to the overlapping of clinical and radiological features, which can be found both in “common” and “uncommon” lesions. The choice of a correct treatment strategy is still arduous and requires histological analysis. Gamma Knife Radiosurgery (GKRS) has already been reported as a safe and effective treatment in these cases. The objective of this study is to evaluate single-center pre-operative data, post-operative outcomes, and long-term follow-up in patients treated with GKRS for unusual sellar tumors. Methods: We retrospectively identified and analyzed nine patients treated with GKRS from 2004 to 2015, according to a standard protocol. Lesions consist of hypothalamic hamartoma (HH), Rathke’s cleft cist (RCC), Langerhans cell histiocytosis (LCH), spindle cell oncocytoma (SCO), choroid plexus papilloma (CPP), and ossifying fibroma (OF). The diagnosis was histologically confirmed in six patients that underwent surgery, while in three patients, diagnosis was based on characteristic clinical and radiological findings (two HH and one RCC). Pre-operative and post-operative data were retrieved from medical archives, and long-term follow-up was obtained through clinical and neuroradiological periodic examination. Results: In our series, all the “rare” sellar lesions treated, had a successful radiographic and clinical response in a medium-long follow-up period. Conclusions: The long-term follow-up results suggest that GKRS is a safe and effective treatment in rare sellar lesions, with very low toxicity. To the best of our knowledge, this report represents the largest series of unusual sellar lesions treated with GKRS in a single high-volume center, suggesting that GKRS might be an effective non-invasive adjuvant treatment option. Further studies and a larger number of patients are needed to confirm if residuals of these rare sellar lesions might regress on their own without treatment or if other non-invasive treatments could be as effective as GKRS. Full article

Background/Objectives: Gamma knife radiosurgery (GKRS) is widely performed as an adjuvant management of patients with residual or recurrent pituitary adenoma (PA). However, the variability in the tumor volume response to GKRS emphasizes the need for reliable predictors of treatment outcomes. The application of radiomics, an analytical approach for quantitative imaging, remains unexplored in predicting treatment responses for PAs. This study aimed to pioneer the use of radiomic MRI analysis to predict the volumetric response of PA to GKRS. Methods: This retrospective observational cohort study involved 81 patients who underwent GKRS for PA. Pre-treatment 3-Tesla MRI scans were used to extract radiomic features capturing the intensity, shape, and texture of the tumors. Radiomic signatures were generated using the least absolute shrinkage and selection operator (LASSO) for feature selection, in conjunction with several classifiers: random forest, naïve Bayes, kNN, logistic regression, neural network, and SVM. Results: The models demonstrated predictive performance in the test folds, with AUC values ranging from 0.759 to 0.928 and R² values between 0.272 and 0.665. Single-sequence T1w, dual-sequence T1w + CE-T1w, and multi-modality including clinicopathological (CP) parameters (CP + T1w + CE-T1w) achieved rather similar prognostic performance in the test folds, with respective AUCs of 0.928, 0.899, and 0.909. All these radiomics models significantly outperformed a benchmark model involving only CP features (AUC = 0.846). Conclusions: This study represents a radiomic analysis focused on predicting the volume response of PAs to GKRS to facilitate treatment individualization. The developed MRI-based radiomics models exhibited superior classification performance compared with the benchmark model composed solely of standard clinicopathological parameters. Full article

Current standard of care treatment for patients with spine tumors includes multidisciplinary approaches, including the following: (1) surgical tumor debulking, epidural spinal cord decompression, and spine stabilization techniques; (2) systemic chemo/targeted therapies; (3) radiation therapy; and (4) surveillance imaging for local disease control and recurrence. Titanium pedicle screw and rod fixation have become commonplace in the spine surgeon’s armamentarium for the stabilization of the spine following tumor resection and separation surgery. However, the high degree of imaging artifacts seen with titanium implants on postoperative CT and MRI scans can significantly hinder the accurate delineation of vertebral anatomy and adjacent neurovascular structures to allow for the safe and effective planning of downstream radiation therapies and detection of disease recurrence. Carbon fiber-reinforced polyetheretherketone (CFR-PEEK) spine implants have emerged as a promising alternative to titanium due to the lack of artifact signals on CT and MRI, allowing for more accurate and safe postoperative radiation planning. In this article, we review the tenants of the surgical and radiation management of spine tumors and discuss the safety, efficacy, and current limitations of CFR-PEEK spine implants in the multidisciplinary management of spine oncology patients. Full article

Background/Objective: This study explores the use of AI-powered radiomics to classify and monitor brain metastasis progression and regression following Gamma Knife radiosurgery (GKRS) based on MRI imaging. A clinical decision support application was developed using Streamlit to provide real-time, AI-driven predictions for treatment monitoring. Methods: MRI scans from 60 patients (3194 images) were analyzed using a transfer learning-enhanced AlexNet deep learning model. Class imbalance was mitigated through dynamic class weighting and data augmentation to ensure equitable performance across all classes. Optimized preprocessing pipelines ensured dataset standardization. Model performance was evaluated using accuracy, precision, recall, F1-scores, and AUC, with 95% confidence intervals. Additionally, a comparative analysis of Gamma Knife radiosurgery (GKRS) outcomes and predictive modeling demonstrated strong correlations between tumor volume evolution and treatment response. The AI predictions and visualizations were integrated into a Streamlit-based application to ensure clinical usability and ease of access. The AI-driven approach effectively classified progression and regression patterns, reinforcing its potential for clinical integration. Results: The transfer learning model achieved flawless classification accuracy (100%; 95% CI: 100–100%) along with perfect precision, recall, and F1-scores. The AUC score of 1.0000 (95% CI: 1.0000–1.0000) indicated excellent discrimination between progression and regression cases. Compared to the baseline AlexNet model (99.53% accuracy; 95% CI: 98.90–100.00%), the TL-enhanced model resolved all misclassifications. Tumor volume analysis identified the baseline size as a key predictor of progression (Pearson r = 0.795, r = 0.795, r = 0.795, p < 0.0001, p < 0.0001, and p < 0.0001). The training time (420.12 s) was faster than ResNet-50 (443.38 s) and EfficientNet-B0 (439.87 s), while achieving equivalent metrics. Despite 100% accuracy, the model requires multi-center validation for generalizability. Conclusions: This study demonstrates that transfer learning with dynamic class weighting provides a highly accurate and reliable framework for monitoring brain metastases post-GKRS. The Streamlit-based AI application enhances clinical decision-making by improving diagnostic precision and reducing variability. Explainable AI techniques, such as Grad-CAM visualizations, improve interpretability and support clinical adoption. These findings emphasize the transformative potential of AI in personalized treatment strategies, extending applications to genomic profiling, survival modeling, and longitudinal follow-ups for brain metastasis management. Full article

Background/Objectives: This study explores the application of vision transformers to predict early responses to stereotactic radiosurgery in patients with brain metastases using minimally pre-processed magnetic resonance imaging scans. The objective is to assess the potential of vision transformers as a predictive tool for clinical decision-making, particularly in the context of imbalanced datasets. Methods: We analyzed magnetic resonance imaging scans from 19 brain metastases patients, focusing on axial fluid-attenuated inversion recovery and high-resolution contrast-enhanced T1-weighted sequences. Patients were categorized into responders (complete or partial response) and non-responders (stable or progressive disease). Results: Despite the imbalanced nature of the dataset, our results demonstrate that vision transformers can predict early treatment responses with an overall accuracy of 99%. The model exhibited high precision (99% for progression and 100% for regression) and recall (99% for progression and 100% for regression). The use of the attention mechanism in the vision transformers allowed the model to focus on relevant features in the magnetic resonance imaging images, ensuring an unbiased performance even with the imbalanced data. Confusion matrix analysis further confirmed the model’s reliability, with minimal misclassifications. Additionally, the model achieved a perfect area under the receiver operator characteristic curve (AUC = 1.00), effectively distinguishing between responders and non-responders. Conclusions: These findings highlight the potential of vision transformers, aided by the attention mechanism, as a non-invasive, predictive tool for early response assessment in clinical oncology. The vision transformer (ViT) model employed in this study processes MRIs as sequences of patches, enabling the capture of localized tumor features critical for early response prediction. By leveraging patch-based feature learning, this approach enhances robustness, interpretability, and clinical applicability, addressing key challenges in tumor progression prediction following stereotactic radiosurgery (SRS). The model’s robust performance, despite the dataset imbalance, underscores its ability to provide unbiased predictions. This approach could significantly enhance clinical decision-making and support personalized treatment strategies for brain metastases. Future research should validate these findings in larger, more diverse cohorts and explore the integration of additional data types to further optimize the model’s clinical utility. Full article

Effective dose calculation is essential for optimizing Gamma Knife (GK) stereotactic radiosurgery (SRS) treatment plans. Modern GK systems allow independent sector activation, enabling complex dose distributions per shot. This study presents a dose approximation method designed to account for shot flexibility and generate 3D doses external to GammaPlan. A treatment plan was created with the TMR10 calculation for individual sector activations using a Radiosurgery Head Phantom. The resulting dose arrays established a basis set of sector-specific distributions, which were then referenced by shot parameters from the plan, allowing dose accumulation through superposition. This superposition approximation (SA) was compared to the original TMR10 using the Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and GK deliverability metrics: coverage, selectivity, and gradient index, across an isodose normalization range from 10% to 90%. In a cohort of 30 patients with 71 targets, strong agreement was observed between TMR10 and SA in the clinically used 50–60% isodose range, with DSC above 85% and HD95 under 2.18 mm. The average differences for the coverage, selectivity, and gradient index were 0.014, 0.008, and 0.118, respectively. This method accurately approximates TMR10 calculations within clinically relevant ranges, offering an external tool to assess 3D dose distributions for GK treatment plans. Full article

Background: This study presents a comprehensive analysis of 135 cases of vestibular schwannoma (VS) treated between 2006 and 2022 at the National Institute of Neurology and Neurovascular Diseases in Bucharest, Romania. The investigation focuses on the clinical presentation, treatment outcomes, and demographic trends of VS patients, highlighting region-specific insights that fill critical gaps in Eastern European data. Methods: Patients were treated with either open surgery (93.3%) or gamma knife radiosurgery (6.6%). The study identifies predominant symptoms, including hearing impairment, facial palsy, and balance disorders, with variations observed across age and gender subgroups. Comorbidities such as hypertension and obesity were prevalent, and they influenced perioperative risks. Results: Post-treatment outcomes showed a significant correlation between clinical symptoms and treatment modalities, with a majority achieving favorable results. The findings emphasize the need for tailored approaches in VS management and underscore the importance of region-specific factors in influencing clinical outcomes. Conclusions: This study contributes to refining treatment strategies and improving healthcare delivery for VS patients in Romania and beyond. Full article