Search Results (575)

Automated brain tumor classification from MRI scans requires optimized CNN architectures deployable on embedded FPGA platforms. This paper presents an integrated approach combining the Multi-Chaotic Enhanced HEOA (MC-HEOA) for automatic CNN architecture discovery with deployment validation on a Xilinx Zynq-7000 FPGA. A CEC2023 benchmark across 10 test functions evaluates 6 chaotic maps and selects the Tent map as the optimal diversity generator. The NAS search space spans a massive combinatorial space of $1.31 \times {10}^{16}$ configurations encoding architectural choices (layers, convolutions, channels, pooling) under a strict constraint of fewer than one million parameters for FPGA compatibility. The optimal discovered architecture, trained and evaluated using single-channel grayscale input (224 × 224 × 1)—the natural representation for intrinsically monochromatic MRI data— achieves 91.33% test accuracy and 92.44% validation accuracy with 724,200 parameters on the 4-class Brain Tumor MRI dataset (glioma, meningioma, pituitary, no tumor). HLS synthesis on the Zynq-7000 (xc7z020clg484-1) validates embedded deployment feasibility, with DSP utilization of 16%, LUT utilization of 57%, FF utilization of 28%, and an inference latency of 374 ms at 100 MHz. This study demonstrates the effectiveness of MC-HEOA for discovering compact, high-performing CNN architectures compatible with FPGA deployment, opening new perspectives for real-time embedded medical diagnosis. Full article

Background/Objectives: Automated brain tumor classification from MRI is particularly challenging when restricted to single post-contrast axial T1-weighted slices without volumetric or clinical context. Methods: We present a four-class (glioma, meningioma, pituitary tumor, no tumor) slice-level classification framework that combines a fine-tuned Swin-Tiny Transformer with inverse-frequency class-weighted learning and a prototype SMT-based symbolic auditing layer for post hoc logical consistency checks. All architectures were trained and evaluated under identical preprocessing, augmentation, optimization, and evaluation protocols. Results: On an internal clinical dataset from Bandırma Onyedi Eylül University Hospital (n = 8040 slices), Swin-Tiny achieved 97.42% slice-level accuracy (macro-F1 97.42%, macro-AUC 0.994), exceeding matched convolutional baselines by approximately eight percentage points. Five-fold stratified cross-validation confirmed stability (mean accuracy 97.40% ± 0.28%). Zero-shot evaluation on the independent BRISC-2025 dataset (n = 6000 slices) yielded 94.82% accuracy and macro-AUC 0.97, indicating maintained performance under acquisition-related distribution shift. Per-class metrics were consistently high across tumor types, with residual errors dominated by glioma–meningioma confusion, reflecting known radiologic overlap on single contrast-enhanced T1 slices. The symbolic auditing layer flagged 1.2–2.9% of predictions as constraint-violating; most such cases were borderline but correctly classified, suggesting sensitivity of heuristic thresholds rather than systematic model failure. Conclusions: These findings support the value of hierarchical shifted-window attention for integrating local texture and broader spatial context in slice-level MRI classification. While patient-wise, multimodal, and prospective validation remain necessary for clinical deployment, this study provides a controlled empirical benchmark and a prototype mechanism for post hoc logical auditing in neuro-oncologic imaging. Full article

Background: Meningioma is the most common primary intracranial tumor in adults, and survival outcomes are influenced by histopathological grade, tumor characteristics, and treatment strategies. This study aimed to evaluate overall survival (OS) and identify prognostic factors in patients with meningioma treated at a national referral cancer center in Indonesia. Methods: A retrospective cohort study was conducted at Dharmais National Cancer Center Hospital, including adult patients with histopathologically confirmed intracranial meningioma who underwent surgical resection between January 2019 and 17 August 2025. Overall survival was calculated from the date of histopathological diagnosis to death or last follow-up and analyzed using Kaplan–Meier methods and Cox proportional hazards regression. Results: A total of 114 patients were included (mean age 48.1 ± 10.5 years; 86.8% female), with most tumors classified as WHO Grade I (64.0%) and located at the skull base (57.0%). Subtotal resection was more common (67.5%), and 71.9% did not receive adjuvant radiotherapy. During follow-up, 14.0% of patients died, with cumulative overall survival rates of 95.6% at 6 months and 86.0% at 96 months. On multivariate analysis, only WHO tumor grade remained an independent prognostic factor (HR 2.199; 95% CI 1.161–4.167; p = 0.016), with higher grades associated with worse survival. Extent of resection and adjuvant radiotherapy were not significantly associated with overall survival after adjustment. Conclusions: In this Indonesian tertiary referral cohort, WHO tumor grade emerged as the only independent predictor of overall survival, underscoring its important prognostic role in meningioma; however, these findings should be interpreted with caution due to incomplete clinical data and relatively short follow-up duration. The high proportion of complex cases, including skull base tumors, reflects referral patterns and may also influence treatment outcomes. Full article

Background/Objectives: Loss of heterozygosity (LOH) in meningioma has been known for more than two decades. It has been shown that LOH on chromosome 1p36 is an independent marker of meningioma recurrence and progression. ARID1A, a tumor suppressor gene located on chromosome 1p36.11, is part of the chromatin-regulating SWI/SNF complex whose subunits are altered in 20% of cases across all tumor entities. Methods: Using our newly developed indirect enzyme-linked immunosorbent assay (ELISA), we investigated whether tumors with or without LOH 1p differ in ARID1A expression in 61 meningiomas. To study possible links between ARID1A and ARID1B, we tested for LOH 6q in association with LOH 1p using a PCR-based microsatellite approach. ARID1B, another member of the SWI/SNF complex, is located on 6q25.3. Additionally, we compared our ELISA results with immunohistochemistry data staining of ARID1A in tissue sections known to harbor LOH 1p. Results: Our results indicate that meningiomas harboring LOH 1p have significantly lower ARID1A levels compared to tumors without LOH 1p. In free nuclear protein fractions, reductions were up to 32% (CI: 6–58.7%). Interestingly, we found that ARID1A levels were significantly lower in tumors with recurrence and/or multiple localizations. In addition, our analysis of chromosome 6q uncovered a significantly strong correlation between LOH 1p and LOH 6q (p < 0.0001). Conclusions: These results highlight the importance of ARID1A in meningioma malignization and indicate for the first time functional evidence for LOH 1p. Full article

Background: Population-level evidence on delivery of neuro-oncology care is essential for evaluating access, equity, and quality of treatment pathways. However, real-world data describing how patients with central nervous system (CNS) tumors, especially with glioblastoma, are managed across healthcare systems remain limited. This study aimed to characterize treatment pathways using linked registry and administrative data within a regional care network. Methods: All adult CNS tumors diagnosed between 2016 and 2020 were identified in the Veneto Cancer Registry. Tumor grading was derived using a validated text-mining algorithm, and surgical, radiotherapy, and systemic treatments were captured through linkage with regional healthcare utilization databases. Patterns of care were evaluated by tumor subtype, grade, and diagnostic pathway. Results: Among 1634 histologically confirmed tumors, glioblastoma represented the largest group. Surgical intervention was widely implemented, with high resection rates in glioblastoma and meningioma. Combined chemoradiotherapy constituted the primary adjuvant approach for glioblastoma and high-grade diffuse gliomas, whereas management of lower-grade tumors showed greater variability. Approximately one-third of patients received no oncologic therapy, primarily associated with older age or diagnostic uncertainty. Analysis of recurrent glioblastoma showed heterogeneous systemic treatment use, reflecting evolving therapeutic practice. Conclusions: Linking population-based registry and administrative data provides actionable insight into real-world delivery of neuro-oncology care, in particular for glioblastoma patients. This approach enables monitoring of treatment variability, identification of potential access gaps, and evaluation of system-level performance, supporting data-driven planning of multidisciplinary services and future quality improvement initiatives. Full article

Background: Meningiomas (MG) are the most common form of benign intracranial tumors. Neuropsychological deficits are often noticed preoperatively. After surgical removal, both improvements and persistent neuropsychological deficits have been reported. Here we present the neuropsychological characteristics of a larger patient group following acute treatment for meningioma. Methods: This retrospective study is part of an overall project investigating the postoperative characteristics and rehabilitation outcomes of 151 patients following surgical removal of MG. Patients were recruited at the neurological department of m&i-Fachklinik Enzensberg between 2019 and 2024. In addition to demographic data and tumor characteristics, the neuropsychological reports were evaluated by two experienced (neuro)psychologists. Results: 69 patients underwent standardized testing in the neuropsychology department and were thus included in the analysis. Upon admission, 52.2% of these patients exhibited attention deficits, 48% showed executive deficits, and 44% had memory impairments. No correlation was found between the extent of resection or the occurrence of complications during surgery and cognitive deficits. However, there was a trend showing that higher-grade tumors were more likely to cause cognitive impairment. The location of the tumor did not correlate with the impaired cognitive domains. At discharge, fewer patients exhibited attention deficits, and those that did had less severe symptoms. Conclusions: Meningiomas are considered to be easily treatable. However, our data show that neuropsychological impairments frequently occur after acute treatment, which may not be given sufficient attention in practice. Even mild cognitive impairments can lead to problems in everyday life or at work. We therefore recommend detailed neuropsychological diagnosis and, if necessary, therapy for all patients after acute treatment. Full article

Background/Objectives: Depot medroxyprogesterone acetate (dMPA) is a synthetic progestin commonly used for contraception. Recent studies have reported an increased association between dMPA exposure and diagnosis of cerebral meningioma. The current systematic review aims to provide a review of literature on the topic of dMPA and cerebral meningioma as well as conduct a meta-analysis by the duration of dMPA use. Methods: The current study presented a systematic review and meta-analysis of observational studies of dMPA and cerebral meningioma derived from PubMed, Web of Science, and Embase database searches for relevant studies published through February 2026. Odds ratios (ORs) and associated 95% confidence intervals were reported to determine the pooled effect of dMPA on cerebral meningioma diagnosis. Quality of evidence was assessed through the GRADE methodology. Results: Nine case-control studies and one cohort study were selected for review and analysis. The overall pooled OR was 2.78 (95% CI 2.20–3.52). This association was strongest for prolonged (i.e., ≥two-years) dMPA exposure (OR 3.49, 95% CI 2.35–5.18). GRADE analysis suggested a moderate quality of evidence. Conclusions: The results of this meta-analysis indicate that dMPA exposure is associated with an over two-fold increased odds of cerebral meningioma. This effect is consistent across studies and is stronger for prolonged dMPA exposure relative to short-term exposure, suggesting a dose–response effect. Clinicians should consider discussing with patients the cerebral meningioma risks associated with dMPA use when considering long-term birth control options. Full article

Background: Meningiomas, the most common primary intracranial tumors, are predominantly benign, but high-grade variants show marked aggressiveness, histo-molecular heterogeneity, and treatment resistance. Although the 2021 WHO CNS classification integrates molecular and histopathologic criteria, substantial inter- and intratumoral variability still limits prognostic accuracy and treatment effectiveness. The goal was to provide insight regarding the histo-molecular intratumoral heterogeneity (ITH) of meningioma and examine its clinical implications. Methods: A narrative review was performed in accordance with PRISMA guidelines. PubMed and Google Scholar were screened for studies on “meningioma” and “intratumoral heterogeneity” published up to 28 July 2025. Eligible studies included original human research reporting histological or molecular heterogeneity with clinical relevance. Results: Eighteen studies comprising 2952 meningioma patients (mean age 59.4 ± 14.8 years, range 16–85) were included. Integrated cytogenetic, molecular, and spatial analyses, including FISH, karyotyping, scRNA-seq, CNV profiling, and spatial transcriptomics, revealed multilayered histo-molecular heterogeneity. Histologically, regional variations in morphology and proliferative index increased with tumor grade. Genomic diversity, marked by recurrent losses of 1p, 14q, and 22q and transcriptionally distinct subclones, defined a complex tumor architecture. Spatial and temporal analyses demonstrated subclonal expansion, stepwise clonal evolution, and therapy resistance, particularly in recurrent tumors. Functionally, SULT1E1⁺ subclones and COL6A3-mediated macrophage–tumor interactions emerged as potential key drivers of malignancy, recurrence, and radioresistance. Conclusions: Histo-molecular diversity underlies meningioma progression, recurrence, and therapeutic resistance. Standardization of ITH assessment, integration of AI-based spatial analytics, and the development of subclone-specific therapies are essential next steps toward advancing precision neuro-oncology. Full article

Magnetic resonance imaging (MRI) is a critical clinical tool that requires precise and reliable interpretation for effective brain tumor diagnosis and timely treatment planning. Deep learning methods have advanced automated tumor classification greatly in the last few years, but many of the current methods are still challenged by a lack of interpretability, a lack of testing on region-focused data, and a lack of model robustness testing. Such limitations reduce clinical trust and limit the practice of automated diagnostic systems. To address these challenges, this study proposes an interpretable deep learning model for classifying brain tumors using the PMRAM dataset, which is a Bangladeshi brain MRI collection containing four categories: glioma, meningioma, pituitary tumor, and normal brain.. The proposed pipeline combines image preprocessing and feature enhancement methods, and then it trains a series of squeeze-and-excitation (SE)-enhanced convolutional neural networks such as VGG19, DenseNet201, MobileNetV3-Large, InceptionV3, and EfficientNetB3. The SE-enhanced EfficientNetB3 performed best, with 98.70% accuracy, 98.77% precision, 98.70% recall, and 98.70% F1-score. Cross-validation also demonstrated stable performance, with a mean accuracy of 96.89%. The model also exhibited efficient inference with low GPU memory consumption, enabling predictions in about 2–4 s per MRI image. Grad-CAM++ and saliency maps were used to improve the transparency of the results, and it was found that the network was concentrated on the clinically significant parts of the tumor, which affected the model predictions. Further robustness analysis and cross-dataset testing are additional evidence of the generalization possibility of the model. An online application was also implemented to allow real-time prediction and visual explanation of brain tumors. Overall, the proposed framework offers a precise, interpretable, and promising solution to automated brain tumor classification using MRI images. Full article

Background. Patients with malignant or benign central nervous system (CNS) tumours are evaluated for suitability of treatment modality based on multiple clinical and tumour-related factors. To obtain multidisciplinary consensus, a patient’s file and imaging are commonly reviewed by a tumour board (TB). There are three relevant weekly TB venues at our institute—gamma knife stereotactic radiosurgery (SRS) intake rounds, CNS rounds, and stereotactic body radiotherapy (SBRT) rounds—which are attended by non-overlapping clinician teams. We explored the clinical parameters prompting multiple TB reviews in patients with complex CNS tumours. Methods. Data were retrospectively obtained from electronic medical records. Patients referred for discussion at SRS rounds (November 2017–June 2020) were cross-referenced with those reviewed in CNS rounds and SBRT rounds. The cohort of interest included patients who underwent review at more than one TB for the same indication. Patient, tumour, and treatment factors were abstracted, and descriptive statistics were calculated. A sub-cohort of patients with pre-plans created for both SRS and conventionally fractionated external beam radiotherapy (EBRT) was identified. Dosimetric data were analyzed. Results. Of 1091 patients, 87 (8.0%) were discussed at more than one TB. 59/87 (67.8%) patients were reviewed at two TBs pertaining to the same CNS lesion and comprised the study cohort. The most common tumour type was meningioma (20/59), and the most common reason for multiple discussions was proximity to optic structures (19/59). After TB discussions, 25/59 patients were seen in consultation by one specialist, 29/59 by two, and 5/59 by none. Overall, the final treatment decisions were conventional EBRT in 21/59; SRS in 18/59; surveillance in 12/59; surgery in 3/59; systemic therapy in 3/59; proton referral in 1/59; and SBRT in 1/59. A total of 20/59 patients were treated with palliative intent. Among all patients who ultimately received radiotherapy, median interval between the first TB discussion and the first RT treatment was 56 days (IQR 7.5–65.5 d). The pre-plan sub-cohort consisted of four patients, all of whom were ultimately treated with conventional EBRT. Conclusions. Evidence to support optimal treatment for some complex CNS tumours can be limited. Multiple radiotherapy modalities may be equally favourable (or unfavourable) options. Proximity to the optic apparatus and previous CNS irradiation are common reasons for clinical equipoise. Tumour board review is an essential tool in formulating a multidisciplinary care plan; however, attention should be paid to ensuring that subsequent consultations and treatment initiation are not unduly delayed. Full article

Background: Difficult benign intracranial tumors (including meningiomas, schwannomas, neurofibromatosis-related tumors, and pituitary neuroendocrine tumors) have substantial morbidity in patients. Due to their limited treatment options, there is a need for individualized treatment beyond histological and surgical approaches. Objective: To summarize how novel treatment innovations have been implemented for these tumors, meningiomas and schwannomas are prioritized, followed by NF-associated neoplasms, and then pituitary neuroendocrine tumors in comparison to low-grade gliomas. Methods: We summarize the current knowledge relating to targeted therapies for gliomas, meningiomas, schwannomas, neurofibromatosis (NF) tumors, and pituitary neuroendocrine tumors to investigate an individual’s treatment options for difficult benign brain tumors. This review synthesizes evidence on tumor genomics and molecular markers, supported by methylation-based classification, immunohistochemistry, and functional assays, emphasizing current clinical applications. Evidence Synthesis: The recent data show that DNA methylation-based models can predict post-surgical outcomes and radiotherapy responses, enabling risk stratification and radiotherapy benefit prediction. Early signals support target-directed treatment, including cMET blockade that radiosensitizes NF2 schwannoma models, brigatinib-associated tumor shrinkage in NF2-deficient models, and PitNET organoid data. Conclusions: We support clinical decision-making that utilizes molecular profiling with functional testing to guide targeted treatment. We also identify evidence gaps such as biomarker-defined prospective trials that are needed for broader clinical implementation. Full article

Objective: Radiation-induced meningiomas (RIMs) are a rare but clinically relevant late complication of cranial irradiation, characterized by long latency and potentially aggressive behavior. This study aimed to systematically analyze the relationships between radiation dose, age at irradiation, latency period, histological grade, tumor multiplicity, and recurrence in RIMs. Methods: A systematic review and pooled case analysis of published cases of RIMs was performed, supplemented by a case series of four institutional patients. Data were extracted on primary tumor type, radiation dose, age at irradiation, latency period, World Health Organization (WHO) grade, tumor multiplicity, and recurrence. Radiation dose was categorized as low (<20 gray (Gy)), intermediate (20–40 Gy), or high (>40 Gy). Statistical analyses included χ² tests, Mann–Whitney U tests, Kruskal–Wallis tests, and Spearman correlation analyses. Results: A total of 1809 patients were included. A higher radiation dose was significantly associated with shorter latency (p < 0.001), a higher WHO grade (p < 0.001), and increased tumor multiplicity (p < 0.001). High-grade RIMs occurred predominantly after high-dose irradiation. Tumor recurrence was significantly more frequent in high-grade than low-grade meningiomas (51.5% vs. 18.3%, p < 0.001), but it was not associated with radiation dose. Older age at irradiation correlated with longer latency (Spearman’s ρ = 0.405, p < 0.001). No association was observed between primary tumor category and WHO grade. Conclusions: RIMs demonstrate dose- and age-dependent biological behavior, with higher radiation doses and younger age at irradiation predisposing to earlier onset and increased aggressiveness. These findings suggest that long-term, dose-adapted radiological surveillance may warrant consideration in irradiated patients. Full article

Intracranial meningiomas (ICMs) are the most common primary adult brain tumor. They are more frequent in women, respond to female hormones, are associated with breast cancer and are often progesterone receptor-positive (PR+), consistent with hormonal sensitivity. Yet <20% are weakly estrogen receptor-positive (ER+). This work reviews the literature to investigate this paucity of ER by first testing if Dexamethasone (Dex), which has been used since 1984 to reduce peritumoral brain edema, is suppressing ER. Ligand-binding assays after 1984 have shown a significant decrease in any and supra-threshold (>10 fmol/mg) ER+ from 68.5% and 39.6% to 25.5% and 12%, respectively (both p < 0.0001). This was confirmed as Dex-related in 93 patients with known Dex exposure (p = 0.0075). Immunohistochemical tests after 1984 have shown that 16% (95%CI 8.4–24.4) of ICMs have rare ER+ cells unrelated to PR and pS2 expression, consistent with Dex inhibition of ER transcription activity. Dex suppression of ER may be compounded by lower endogenous ER concentrations in ICMs compared to breast cancer. The difference in intra-tumoral estrogen concentration is proposed as a potential cause for lower ER in ICM. Replacement of Dex and more sensitive ER assays are needed to determine the role of hormones in the causation and treatment of ER+ ICM. Full article

Background: Gamma Knife radiosurgery (GKS) delivers highly conformal intracranial irradiation, yet clinical decision-making still relies predominantly on physical dose metrics that do not account for fractionation, dose rate, treatment time, or DNA repair. Classical radiobiological models—including the linear–quadratic (LQ) formula and the Jones–Hopewell single-session repair model—do not extend naturally to 3- and 5-fraction GKS. Meanwhile, growing evidence suggests that biologically effective dose (BED) may better capture radiosurgical response in selected pathologies. A unified, biologically grounded, multi-fraction GKS framework has been lacking. Methods: We developed AI-BED-Fx, the first multi-fraction extension of the Jones–Hopewell radiobiological model capable of computing fraction-resolved BED for 1-, 3-, and 5-fraction GKS. The framework incorporates α/β ratio, dual-component repair kinetics, isocentre geometry, beam-on–time structure, and lesion-specific biological parameters. Four synthetic pathology-specific cohorts—arteriovenous malformation (AVM), meningioma (MEN), vestibular schwannoma (VS), and brain metastasis (BM)—were generated using distinct radiobiological signatures. Machine-learning models were trained to quantify the predictive value of physical dose versus BED for local control or obliteration. Additional experiments included Bayesian estimation of α/β and a neural-network surrogate for fast BED prediction. An exploratory comparison with a 60-lesion clinical brain–metastasis dataset was performed to assess whether key trends observed in the synthetic BM cohort were consistent with real radiosurgical outcomes. Results: AI-BED-Fx produced realistic pathology-specific BED distributions (AVM 60–210 Gy_2.47; MEN 41–85 Gy_3.5; VS 46–68 Gy₃; BM 37–75 Gy₁₀) and biologically coherent dose–response relationships. Predictive modeling demonstrated strong pathology dependence. In AVM, the three models achieved AUCs of 0.921 (Model A), 0.922 (Model B), and 0.924 (Model C), with corresponding Brier scores of 0.054, 0.051, and 0.051, with BED-based models performing best. In meningioma, BED was the dominant predictor, with AUCs of 0.642 (Model A), 0.660 (Model B), and 0.661 (Model C) and Brier scores of 0.181, 0.177, and 0.179, respectively. In vestibular schwannoma, the narrow BED range resulted in minimal BED contribution, with AUCs of 0.812, 0.827, and 0.830 and Brier scores of 0.165, 0.160, and 0.162, with physical dose and tumor volume determining performance. In brain metastases, outcomes were driven primarily by volume and physical dose, with AUCs of 0.614, 0.630, and 0.629 and Brier scores of 0.254, 0.250, and 0.253, showing negligible improvement from BED. AI-BED-Fx also accurately recovered the true α/β from synthetic outcomes (posterior mean 2.54 vs. true 2.47), and a neural-network surrogate reproduced full radiobiological BED calculations with near-perfect fidelity (R² = 0.9991). Conclusions: AI-BED-Fx provides the first unified, biologically explicit framework for modeling single- and multi-fraction Gamma Knife radiosurgery. The findings show that the predictive usefulness of BED is pathology-specific rather than universal, and that radiobiological dose provides additional predictive value only when repair kinetics and dose–response biology support it. By integrating mechanistic radiobiology with machine learning, AI-BED-Fx establishes the conceptual and computational foundations for biologically adaptive, AI-guided radiosurgery, and cross-pathology comparison of treatment response. This work uses large radiobiologically grounded synthetic cohorts for methodological validation; limited real-patient data are included only for exploratory consistency checks, and full clinical validation is planned. Full article

Boron neutron capture therapy (BNCT) is a biologically targeted, high–linear energy transfer radiotherapy that selectively delivers cytotoxic α-particles to boron-loaded tumor cells and has re-emerged with the development of hospital-compatible accelerator neutron sources and improved boron carriers. We performed a structured literature review of PubMed, Embase, and the Cochrane Library through October 2025 to summarize the radiobiological rationale, boron delivery strategies, and clinical outcomes of BNCT in glioblastoma (GBM) and other high-grade central nervous system tumors. Eligible clinical and translational studies were screened independently, and data on patient populations, boron agents, neutron source technologies, dosimetry, survival, response, and toxicity were extracted. Contemporary series and phase II trials indicate that BNCT is technically feasible and generally well tolerated, with encouraging survival outcomes in selected newly diagnosed and recurrent GBM, meaningful activity in recurrent high-grade meningiomas, and acceptable safety in limited pediatric cohorts. Current practice relies primarily on second-generation carriers such as boronophenylalanine and sodium borocaptate, while third-generation molecular and nanocarrier platforms remain in preclinical development. Overall, BNCT represents a promising high-LET, pharmacologically targeted modality for heavily pretreated and radioresistant CNS tumors, and ongoing prospective studies are needed to define its comparative effectiveness and optimal integration into patient care. Full article