A kinome drug screen identifies multi-TKI synergies and ERBB2 signaling as a therapeutic vulnerability in MYC/TYR subgroup ATRTs.

BACKGROUND: Atypical Teratoid Rhabdoid Tumor (ATRT) is a rare, devastating, and largely incurable pediatric brain tumor. Although recent studies have uncovered three molecular subgroups of ATRTs with distinct disease patterns, and signaling features, the therapeutic profiles of ATRT subgroups remain incompletely elucidated. METHODS: We examined the effect of 465 kinase inhibitors on a panel of ATRT subgroup-specific cell lines. We then applied multi-omics analyses to investigate the underlying molecular mechanism of kinase inhibitor efficacy in ATRT subgroups. RESULTS: We observed that ATRT cell lines are broadly sensitive to inhibitors of the PI3K and MAPK signaling pathways, as well as CDKs, AURKA/B kinases, and PLK1. We identified two classes of multi-kinase inhibitors (MKIs) predominantly targeting receptors tyrosine kinase (RTKs) including PDGFR and EGFR/ERBB2 in MYC/TYR ATRT cells. The PDGFRB inhibitor, Dasatinib, synergistically affected MYC/TYR ATRT cell growth when combined with broad-acting PI3K and MAPK pathway inhibitors, including Rapamycin and Trametinib. We observed that MYC/TYR ATRT cells were also distinctly sensitive to various inhibitors of ERBB2 signaling. Transcriptional, H3K27Ac ChIPSeq, ATACSeq, and HiChIP analyses of primary MYC/TYR ATRTs revealed ERBB2 expression which correlated with differential methylation and activation of a distinct enhancer element by DNA looping. Significantly, we show the brain penetrant EGFR/ERBB2 inhibitor, Afatinib, specifically inhibited in vitro and in vivo growth of MYC/TYR ATRT cells. CONCLUSIONS: Taken together our studies suggest combined treatments with PDGFR and ERBB2-directed TKIs with inhibitors of the PI3K and MAPK pathways as an important new therapeutic strategy for the MYC/TYR subgroup of ATRTs.

Exploring approaches to tackle cross-domain challenges in brain medical image segmentation: a systematic review.

Introduction: Brain medical image segmentation is a critical task in medical image processing, playing a significant role in the prediction and diagnosis of diseases such as stroke, Alzheimer's disease, and brain tumors. However, substantial distribution discrepancies among datasets from different sources arise due to the large inter-site discrepancy among different scanners, imaging protocols, and populations. This leads to cross-domain problems in practical applications. In recent years, numerous studies have been conducted to address the cross-domain problem in brain image segmentation. Methods: This review adheres to the standards of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for data processing and analysis. We retrieved relevant papers from PubMed, Web of Science, and IEEE databases from January 2018 to December 2023, extracting information about the medical domain, imaging modalities, methods for addressing cross-domain issues, experimental designs, and datasets from the selected papers. Moreover, we compared the performance of methods in stroke lesion segmentation, white matter segmentation and brain tumor segmentation. Results: A total of 71 studies were included and analyzed in this review. The methods for tackling the cross-domain problem include Transfer Learning, Normalization, Unsupervised Learning, Transformer models, and Convolutional Neural Networks (CNNs). On the ATLAS dataset, domain-adaptive methods showed an overall improvement of ~3 percent in stroke lesion segmentation tasks compared to non-adaptive methods. However, given the diversity of datasets and experimental methodologies in current studies based on the methods for white matter segmentation tasks in MICCAI 2017 and those for brain tumor segmentation tasks in BraTS, it is challenging to intuitively compare the strengths and weaknesses of these methods. Conclusion: Although various techniques have been applied to address the cross-domain problem in brain image segmentation, there is currently a lack of unified dataset collections and experimental standards. For instance, many studies are still based on n-fold cross-validation, while methods directly based on cross-validation across sites or datasets are relatively scarce. Furthermore, due to the diverse types of medical images in the field of brain segmentation, it is not straightforward to make simple and intuitive comparisons of performance. These challenges need to be addressed in future research.

Demystifying the potential of lipid-based nanocarriers in targeting brain malignancies.

Drug targeting for brain malignancies is restricted due to the presence of the blood-brain barrier (BBB) and blood-brain tumor barrier (BBTB), which act as barriers between the blood and brain parenchyma. Certainly, the limited therapeutic options for brain malignancies have made notable progress with enhanced biological understanding and innovative approaches, such as targeted therapies and immunotherapies. These advancements significantly contribute to improving patient prognoses and represent a promising shift in the landscape of brain malignancy treatments. A more comprehensive understanding of the histology and pathogenesis of brain malignancies is urgently needed. Continued research focused on unraveling the intricacies of brain malignancy biology holds the key to developing innovative and tailored therapies that can improve patient outcomes. Lipid nanocarriers are highly effective drug delivery systems that significantly improve their solubility, bioavailability, and stability while also minimizing unwanted side effects. Surface-modified lipid nanocarriers (liposomes, niosomes, solid lipid nanoparticles, nanostructured lipid carriers, lipid nanocapsules, lipid-polymer hybrid nanocarriers, lipoproteins, and lipoplexes) are employed to improve BBB penetration and uptake through various mechanisms. This systematic review illuminates and covers various topics related to brain malignancies. It explores the different methods of drug delivery used in treating brain malignancies and delves into the benefits, limitations, and types of brain-targeted lipid-based nanocarriers. Additionally, this review discusses ongoing clinical trials and patents related to brain malignancy therapies and provides a glance into future perspectives for treating this condition.

Familial schwannomatosis carrying LZTR1 variant p.R340X with brain tumor: A case report.

Schwannomatosis (SWN) is a rare genetic condition characterized by the risk of developing multiple benign peripheral nerve sheath tumors; however, the risk of developing malignant tumors in patients with SWN remains unclear. This study described the case of a 57-year-old Japanese man diagnosed with SWN whose older brother also had SWN. Whole-exome sequencing identified a heterozygous mutation [c.1018C > T (p.Arg340X)] in the LZTR1 gene, linked to the RAS/MAPK pathway, in the patient and his brother. Moreover, the patient had aphasia and right-sided paralysis because of a brain tumor. RNA sequencing revealed the remarkable upregulation of several genes associated with oxidative stress, such as the reactive oxygen species pathway and oxidative phosphorylation, a downstream effector of the RAS/MAPK pathway, in the the patient and his brother compared with healthy volunteers. The final diagnosis was LZTR1-related familial SWN, and the dysregulated RAS/MAPK pathway in this patient might be associated with brain tumorigenesis.

TULIPs decorate the three-dimensional genome of PFA ependymoma.

Posterior fossa group A (PFA) ependymoma is a lethal brain cancer diagnosed in infants and young children. The lack of driver events in the PFA linear genome led us to search its 3D genome for characteristic features. Here, we reconstructed 3D genomes from diverse childhood tumor types and uncovered a global topology in PFA that is highly reminiscent of stem and progenitor cells in a variety of human tissues. A remarkable feature exclusively present in PFA are type B ultra long-range interactions in PFAs (TULIPs), regions separated by great distances along the linear genome that interact with each other in the 3D nuclear space with surprising strength. TULIPs occur in all PFA samples and recur at predictable genomic coordinates, and their formation is induced by expression of EZHIP. The universality of TULIPs across PFA samples suggests a conservation of molecular principles that could be exploited therapeutically.

Beyond boundaries: The therapeutic potential of exosomes in neural microenvironments in neurological disorders.

Neurological disorders are a diverse group of conditions that can significantly impact individuals' quality of life. The maintenance of neural microenvironment homeostasis is essential for optimal physiological cellular processes. Perturbations in this delicate balance underlie various pathological manifestations observed across various neurological disorders. Current treatments for neurological disorders face substantial challenges, primarily due to the formidable blood-brain barrier and the intricate nature of neural tissue structures. These obstacles have resulted in a paucity of effective therapies and inefficiencies in patient care. Exosomes, nanoscale vesicles that contain a complex repertoire of biomolecules, are identifiable in various bodily fluids. They hold substantial promise in numerous therapeutic interventions due to their unique attributes, including targeted drug delivery mechanisms and the ability to cross the BBB, thereby enhancing their therapeutic potential. In this review, we investigate the therapeutic potential of exosomes across a range of neurological disorders, including neurodegenerative disorders, traumatic brain injury, peripheral nerve injury, brain tumors, and stroke. Through both in vitro and in vivo studies, our findings underscore the beneficial influence of exosomes in enhancing the neural microenvironment following neurological diseases, offering promise for improved neural recovery and management in these conditions.

Breaking the barrier: Nanoparticle-enhanced radiotherapy as the new vanguard in brain tumor treatment.

The pursuit of effective treatments for brain tumors has increasingly focused on the promising area of nanoparticle-enhanced radiotherapy (NERT). This review elucidates the context and significance of NERT, with a particular emphasis on its application in brain tumor therapy-a field where traditional treatments often encounter obstacles due to the blood-brain barrier (BBB) and tumor cells' inherent resistance. The aims of this review include synthesizing recent advancements, analyzing action mechanisms, and assessing the clinical potential and challenges associated with nanoparticle (NP) use in radiotherapy enhancement. Preliminary preclinical studies have established a foundation for NERT, demonstrating that nanoparticles (NPs) can serve as radiosensitizers, thereby intensifying radiotherapy's efficacy. Investigations into various NP types, such as metallic, magnetic, and polymeric, have each unveiled distinct interactions with ionizing radiation, leading to an augmented destruction of tumor cells. These interactions, encompassing physical dose enhancement and biological and chemical radio sensitization, are crucial to the NERT strategy. Although clinical studies are in their early phases, initial trials have shown promising results in terms of tumor response rates and survival, albeit with mindful consideration of toxicity profiles. This review examines pivotal studies affirming NERT's efficacy and safety. NPs have the potential to revolutionize radiotherapy by overcoming challenges in targeted delivery, reducing off-target effects, and harmonizing with other modalities. Future directions include refining NP formulations, personalizing therapies, and navigating regulatory pathways. NERT holds promise to transform brain tumor treatment and provide hope for patients.

Deep learning-based deformable image registration with bilateral pyramid to align pre-operative and follow-up magnetic resonance imaging (MRI) scans.

Background: The evaluation of brain tumor recurrence after surgery is based on the comparison between tumor regions on pre-operative and follow-up magnetic resonance imaging (MRI) scans in clinical practice. Accurate alignment of MRI scans is important in this evaluation process. However, existing methods often fail to yield accurate alignment due to substantial appearance and shape changes of tumor regions. The study aimed to improve this misalignment situation through multimodal information and compensation for shape changes. Methods: In this work, a deep learning-based deformation registration method using bilateral pyramid to create multi-scale image features was developed. Moreover, morphology operations were employed to build correspondence between the surgical resection on the follow-up and pre-operative MRI scans. Results: Compared with baseline methods, the proposed method achieved the lowest mean absolute error of 1.82 mm on the public BraTS-Reg 2022 dataset. Conclusions: The results suggest that the proposed method is potentially useful for evaluating tumor recurrence after surgery. We effectively verified its ability to extract and integrate the information of the second modality, and also revealed the micro representation of tumor recurrence. This study can assist doctors in registering multiple sequence images of patients, observing lesions and surrounding areas, analyzing and processing them, and guiding doctors in their treatment plans.

CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation.

Background: The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation. Methods: We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions. Results: Evaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations. Conclusions: The experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

Maternal obesogenic diet operates at the tumor cell of origin to increase incidence and decrease latency of Neurofibromatosis Type 1 optic pathway glioma.

BACKGROUND: Pediatric low-grade glioma incidence has been rising in the U.S., mirroring the rising rates of pediatric and maternal obesity. Recently, children of obese mothers were demonstrated to develop brain tumors at higher rates. Importantly, obesity in the U.S. is largely driven by diet, given the prevalence of high fat and high sugar (HFHS) food choices. Since high-fat diet exposure can increase embryonic neuroglial progenitor cell (NPC) proliferation, the potential cells of origin for low-grade glioma, we hypothesized that in utero exposure to an obesogenic diet would modify pediatric brain penetrance and latency by affecting the tumor cell of origin. METHODS: We employed several murine models of the Neurofibromatosis type 1 (NF1) pediatric brain tumor predisposition syndrome, in which optic pathway gliomas (Nf1-OPGs) arise from NPCs in the embryonic third ventricular zone (TVZ). We exposed dams and offspring to an obesogenic HFHS diet or control chow and analysed fetal neurodevelopment at E19.5 and tumor formation at 6w-3mo. RESULTS: Progeny from HFHS diet-exposed dams demonstrated increased TVZ NPC proliferation and glial differentiation. Dietary switch cohorts confirmed that these effects were dependent upon maternal diet, rather than maternal weight. Obesogenic diet (Ob) similarly accelerated glioma formation in a high-penetrance Nf1-OPG strain and increased glioma penetrance in two low-penetrance Nf1-OPG strains. In contrast, Ob exposure in the postnatal period alone did not recapitulate these effects. CONCLUSIONS: These findings establish maternal obesogenic diet as a risk factor for murine Nf1-OPG formation, acting in part through in utero effects on the tumor cell of origin.

Cervical Lymph Node Metastasis From Medulloblastoma in a Young Adult: Case Report and Literature Review.

Medulloblastoma, an embryonal tumor located in the posterior fossa of the brain, originates from the neuro-epidermal layer of the cerebellum. It is the most prevalent malignant tumor in children, while it is rare in adults and predominantly affects males. Multimodal therapeutic interventions, such as surgery, radiotherapy, and chemotherapy, have substantially enhanced the prognosis of this condition. Extraneural metastases are infrequent. We present a case of medulloblastoma relapse with nodal metastasis in a 28-year-old adult.

Relationships between cognitive flexibility performance and adaptive behavior outcomes in survivors of pediatric brain tumor.

Objective: Survivors of pediatric brain tumors are at increased risk of executive function (EF) and adaptive behavior difficulties. While previous research suggests that executive dysfunction impacts suboptimal adaptive outcomes, the specific elements of EF influencing this relationship remain unexplored. This study examines the relationship between cognitive flexibility and adaptive behavior in survivors compared to healthy controls. Methods: 86 survivors (Mage(SD)=23.41(4.24), 44 females) and 86 controls (Mage(SD)=23.09(4.50), 44 females) completed the Delis-Kaplan Executive Function System Trail Making Test (TMT) and Verbal Fluency Test (VFT). The Letter-Number Sequencing (LNS) and Category Switching (CS) conditions were isolated as measures of cognitive flexibility. Informants provided responses to obtain adaptive behavior ratings using the Scales of Independent Behavior-Revised (SIB-R). Linear regressions explored relationships between cognitive flexibility and SIB-R scores in survivors compared to controls. Results: For both TMT and VFT, the relationship between cognitive flexibility and adaptive behavior was significantly different between survivors and controls for SIB-R scores in Social Communication, Community Living, and Personal Living Skills (p<.0125). Survivors' better LNS performance predicted greater SIB-R scores across the same 3 domains (all p= <.001, r2semipartial=.08). Similarly, survivors' better CS performance predicted greater SIB-R scores across the same 3 domains (p = 0.002 to .02, r2semipartial =.03 to .04). No significant relationships were found in controls (all p >.05). After adjusting for working memory and inhibitory control, most relationships remained significant in survivors (p= <.001 to .046, r2semipartial=.02 to .08). Conclusion: These findings reveal a robust, positive relationship between cognitive flexibility performance and adaptive behaviors specific to survivors.

Global post­marketing safety surveillance of Tumor Treating Fields (TTFields) therapy in over 25,000 patients with CNS malignancies treated between 2011-2022.

BACKGROUND: Tumor Treating Fields (TTFields) are alternating electric fields that disrupt cancer cell processes. TTFields therapy is approved for recurrent glioblastoma (rGBM), and newly-diagnosed (nd) GBM (with concomitant temozolomide for ndGBM; US), and for grade IV glioma (EU). We present an updated global, post-marketing surveillance safety analysis of patients with CNS malignancies treated with TTFields therapy. METHODS: Safety data were collected from routine post-marketing activities for patients in North America, Europe, Israel, and Japan (October 2011-October 2022). Adverse events (AEs) were stratified by age, sex, and diagnosis. RESULTS: Overall, 25,898 patients were included (diagnoses: ndGBM [68%], rGBM [26%], anaplastic astrocytoma/oligodendroglioma [4%], other CNS malignancies [2%]). Median (range) age was 59 (3-103) years; 66% patients were male. Most (69%) patients were 18-65 years; 0.4% were < 18 years; 30% were > 65 years. All-cause and TTFields-related AEs occurred in 18,798 (73%) and 14,599 (56%) patients, respectively. Most common treatment-related AEs were beneath-array skin reactions (43%), electric sensation (tingling; 14%), and heat sensation (warmth; 12%). Treatment-related skin reactions were comparable in pediatric (39%), adult (42%), and elderly (45%) groups, and in males (41%) and females (46%); and similar across diagnostic subgroups (ndGBM, 46%; rGBM, 34%; anaplastic astrocytoma/oligodendroglioma, 42%; other, 40%). No TTFields-related systemic AEs were reported. CONCLUSIONS: This long-term, real-world analysis of > 25,000 patients demonstrated good tolerability of TTFields in patients with CNS malignancies. Most therapy-related AEs were manageable localized, non-serious skin events. The TTFields therapy safety profile remained consistent across subgroups (age, sex, and diagnosis), indicative of its broad applicability.

Classification of brain tumor types through MRIs using parallel CNNs and firefly optimization.

Image segmentation is a critical and challenging endeavor in the field of medicine. A magnetic resonance imaging (MRI) scan is a helpful method for locating any abnormal brain tissue these days. It is a difficult undertaking for radiologists to diagnose and classify the tumor from several pictures. This work develops an intelligent method for accurately identifying brain tumors. This research investigates the identification of brain tumor types from MRI data using convolutional neural networks and optimization strategies. Two novel approaches are presented: the first is a novel segmentation technique based on firefly optimization (FFO) that assesses segmentation quality based on many parameters, and the other is a combination of two types of convolutional neural networks to categorize tumor traits and identify the kind of tumor. These upgrades are intended to raise the general efficacy of the MRI scan technique and increase identification accuracy. Using MRI scans from BBRATS2018, the testing is carried out, and the suggested approach has shown improved performance with an average accuracy of 98.6%.

H3K27-Altered Diffuse Midline Glioma of the Brainstem: From Molecular Mechanisms to Targeted Interventions.

Pediatric high-grade gliomas are a devastating subset of brain tumors, characterized by their aggressive pathophysiology and limited treatment options. Among them, H3 K27-altered diffuse midline gliomas (DMG) of the brainstem stand out due to their distinct molecular features and dismal prognosis. Recent advances in molecular profiling techniques have unveiled the critical role of H3 K27 alterations, particularly a lysine-to-methionine mutation on position 27 (K27M) of the histone H3 tail, in the pathogenesis of DMG. These mutations result in epigenetic dysregulation, which leads to altered chromatin structure and gene expression patterns in DMG tumor cells, ultimately contributing to the aggressive phenotype of DMG. The exploration of targeted therapeutic avenues for DMG has gained momentum in recent years. Therapies, including epigenetic modifiers, kinase inhibitors, and immunotherapies, are under active investigation; these approaches aim to disrupt aberrant signaling cascades and overcome the various mechanisms of therapeutic resistance in DMG. Challenges, including blood-brain barrier penetration and DMG tumor heterogeneity, require innovative approaches to improve drug delivery and personalized treatment strategies. This review aims to provide a comprehensive overview of the evolving understanding of DMG, focusing on the intricate molecular mechanisms driving tumorigenesis/tumor progression and the current landscape of emerging targeted interventions.

Exploring Memory Systems After Pediatric Posterior Fossa Tumor: From Memory Profile Comparisons in Nonirradiated Versus Irradiated Patients to Episodic Memory Tests Capable of Detecting Radiation-induced Hippocampal Damage.

AIMS: Pediatric posterior fossa tumor (PFT) survivors experience long-term cognitive sequelae, including memory disorders, for which irradiation is one of the main risk factors. The aims of the present study were to (1) explore the profile of impairment in episodic, semantic, working and procedural memory systems in irradiated versus nonirradiated PFT survivors, and (2) test whether an autobiographical questionnaire and a two-phase ecological test (Epireal) assessing episodic memory are more sensitive to radiation-induced hippocampal damage than commonly used tests. MATERIALS AND METHODS: A total of 60 participants (22 irradiated PFT survivors, 17 nonirradiated PFT survivors, and 21 controls) were included in the prospective IMPALA study. They all underwent a broad battery of tests assessing the different memory systems in two 2-day sessions 3 weeks apart. We performed between-groups comparisons and analyzed impairment profiles, using -1.65 SDs as a cut-off. For irradiated patients, correlations were calculated between mean radiation doses to key brain structures involved in memory (hippocampus, cerebellum, and striatum) and corresponding memory scores. RESULTS: PBT survivors performed significantly more poorly than controls (p < 0.001) on conventional tests of episodic, semantic and working memory: 64% of irradiated patients and 35% of nonirradiated patients had a deficit in at least two memory systems, with episodic memory impairment being more specific to the irradiated group. Epireal had a larger effect size than the other episodic memory tests, allowing us to detect deficits in a further 18% of irradiated patients. These deficits were correlated with the mean radiation dose to the left hippocampus. CONCLUSION: Memory impairment is a frequent long-term cognitive sequela in PFT survivors, especially after radiation therapy. New ecological tests of episodic memory that are more sensitive to radiation-induced deficits than conventional tests could yield specific markers of the toxicity of medial temporal lobe irradiation.

In Vivo Mouse Models for Adult Brain Tumors: Exploring Tumorigenesis and Advancing Immunotherapy Development.

Brain tumors, particularly glioblastoma (GBM), are devastating and challenging to treat, with a low 5-year survival rate of only 6.6%. Mouse models are established to understand tumorigenesis and develop new therapeutic strategies. Large-scale genomic studies have facilitated the identification of genetic alterations driving human brain tumor development and progression. Genetically engineered mouse models (GEMMs) with clinically relevant genetic alterations are widely used to investigate tumor origin. Additionally, syngeneic implantation models, utilizing cell lines derived from GEMMs or other sources, are popular for their consistent and relatively short latency period, addressing various brain cancer research questions. In recent years, the success of immunotherapy in specific cancer types has led to a surge in cancer immunology-related research which specifically necessitates the utilization of immunocompetent mouse models. In this review, we provide a comprehensive summary of GEMMs and syngeneic mouse models for adult brain tumors, emphasizing key features such as model origin, genetic alteration background, oncogenic mechanisms, and immune-related characteristics. Our review serves as a valuable resource for the brain tumor research community, aiding in the selection of appropriate models to study cancer immunology.

The feasibility of robot assisted laser interstitial thermal therapy (LITT) for brain tumors in octogenarians.

BACKGROUND: The use of robot assisted laser interstitial thermal therapy (LITT) is emerging as a viable treatment option for brain tumors patients aged between 80-90 years (octogenarians). Correspondingly the aim of this study was to describe the clinical feasibility of octogenarians undergoing LITT procedure for brain tumors at our institution. METHODS: A retrospective review was conducted of all robot assisted LITT procedures performed at our institution between 2013-2023 for octogenarians. Comparison of continuous variables was by student t-tests, and Kaplan-Meier estimates were used to estimate survival outcomes. RESULTS: A total of 20/311 (6%) LITT patients in the search cohort were octogenarians. Mean age was 82.6 years (range, 80.1-88.0) with 13 (65%) females. Brain tumor lesions most commonly were located on the left side (65%), and for ablation, all were single trajectories with mean number of 2.3 ablations. No operative complications were seen during hospitalization, with mean length of stay of 1.6 days and most common disposition destination being home (95%). There were no 30- or 90-day readmissions or emergency room presentations. Mean follow-up was 12.4 months without any complications in that time. The most common pathology in our cohort was glioblastoma (GBM, 55%). CONCLUSION: Robot assisted LITT is a safe and effective treatment option for brain tumors in octogenarians with a very low morbidity risk. Therefore, further investigations is required to understand how LITT can translate to therapeutic benefit in patients aged over 80 years old with brain tumors.

Enhancing brain tumor detection in MRI with a rotation invariant Vision Transformer.

Background: The Rotation Invariant Vision Transformer (RViT) is a novel deep learning model tailored for brain tumor classification using MRI scans. Methods: RViT incorporates rotated patch embeddings to enhance the accuracy of brain tumor identification. Results: Evaluation on the Brain Tumor MRI Dataset from Kaggle demonstrates RViT's superior performance with sensitivity (1.0), specificity (0.975), F1-score (0.984), Matthew's Correlation Coefficient (MCC) (0.972), and an overall accuracy of 0.986. Conclusion: RViT outperforms the standard Vision Transformer model and several existing techniques, highlighting its efficacy in medical imaging. The study confirms that integrating rotational patch embeddings improves the model's capability to handle diverse orientations, a common challenge in tumor imaging. The specialized architecture and rotational invariance approach of RViT have the potential to enhance current methodologies for brain tumor detection and extend to other complex imaging tasks.