Assessing and Charting the Future Path : Addressing the Decline of Brain Tumor Specialists in Korea - Insights from the Korea Brain Tumor Society (KBTS) Future Strategy Committee of 2023.

Objective: Although Republic of Korea is an advanced country in medical technology with a successful treatment rate for serious diseases, such as cancer, and has improved technology for highly difficult surgery, many excellent medical doctors and physicians are struggling due to the recent unreasonable medical environment. Specialization in brain tumor surgery also faces challenges in Republic of Korea, including low financial incentives, legal threats, and limited career prospects. In response, the Korea Brain Tumor Society (KBTS) formed the Future Strategy Committee to assess these obstacles and propose solutions. Methods: A survey was conducted among the KBTS members to understand their perceptions and concerns across different career stages. Results: The findings revealed a decline in interest among chief residents in brain tumor surgery, owing to limited job opportunities and income prospects. Neurosurgical fellows expressed neutral satisfaction but highlighted challenges, such as low patient numbers and income. Faculty members with varying levels of experience echoed similar concerns, emphasizing the need for improved financial incentives and job stability. Despite these challenges, the respondents expressed dedication to the field and suggested strategies for improvement. Conclusion: The KBTS outlines a vision that focuses on practical excellence, comprehensive research, professional education, responsibilities, and member satisfaction. Addressing these challenges requires collaborative efforts among healthcare institutions, professional societies, and policymakers to support brain tumor specialists and enhance patient care.

GT-Net: global transformer network for multiclass brain tumor classification using MR images.

Multiclass classification of brain tumors from magnetic resonance (MR) images is challenging due to high inter-class similarities. To this end, convolution neural networks (CNN) have been widely adopted in recent studies. However, conventional CNN architectures fail to capture the small lesion patterns of brain tumors. To tackle this issue, in this paper, we propose a global transformer network dubbed GT-Net for multiclass brain tumor classification. The GT-Net mainly comprises a global transformer module (GTM), which is introduced on the top of a backbone network. A generalized self-attention block (GSB) is proposed to capture the feature inter-dependencies not only across spatial dimension but also channel dimension, thereby facilitating the extraction of the detailed tumor lesion information while ignoring less important information. Further, multiple GSB heads are used in GTM to leverage global feature dependencies. We evaluate our GT-Net on a benchmark dataset by adopting several backbone networks, and the results demonstrate the effectiveness of GTM. Further, comparison with state-of-the-art methods validates the superiority of our model.

Psychosocial and executive functioning late effects in pediatric brain tumor survivors after proton radiation.

PURPOSE: Pediatric brain tumor survivors can experience detrimental effects from radiation treatment. This cross-sectional, large cohort study examined late psychosocial and executive functioning effects in pediatric patients treated ≥ 3 years after proton radiation therapy (PRT). METHODS: Parents of 101 pediatric brain tumor survivors completed the Behavior Assessment System for Children and the Behavior Rating Inventory of Executive Function. Standard scores were compared to published normative means, rates of impairment (T-score > 65) were calculated, and demographic and clinical characteristics were examined. RESULTS: Mean age at PRT was 8.12 years and mean interval from PRT to assessment was 6.05 years. Half were female (49.5%), 45.5% received craniospinal irradiation (CSI), and 58.4% were diagnosed with infratentorial tumors. All mean T-scores were within normal range. Mean T-scores were significantly elevated compared to the norm on the withdrawal, initiate, working memory, and plan/organize scales. Rates of impairment were notably high in working memory (24.8%), initiate (20.4%), withdrawal (18.1%), and plan/organize (17.0%). Greater withdrawal was significantly associated with CSI and also with chemotherapy and diagnosis of hearing loss. Mean T-scores were significantly lower than the norm on the hyperactivity, aggression, conduct problems, and inhibition scales. No significant problems were identified with social skills or depression. Interval since treatment was not correlated with any scale. CONCLUSION: Although psychosocial and executive functioning was within the normal range, on average, social withdrawal and metacognitive executive functioning (working memory, initiating, planning/organizing) were areas of concern. Targeted yearly screening and proactive executive skill and social interventions are needed for this population.

Patient reported outcomes and short-term adjustment trajectories following gamma knife radiosurgery for benign brain tumor.

PURPOSE: Patient reported outcomes (PROs) in the context of Gamma Knife Stereotactic Radiosurgery (GKSRS) for benign brain tumor have been under-researched. This study examined changes in PROs and adjustment trajectories post-GKSRS. METHOD: 50 adults (54% female) aged on average 53.18 (SD = 14.76) years with benign brain tumor were assessed 1 week before GKSRS, 1-2 weeks post-GKSRS, and at 3-month follow-up. Telephone-based questionnaires of anxiety and depressive symptoms, cognitive function, symptom burden, and health-related quality of life (HRQoL) were completed. RESULTS: Significant improvements in HRQoL, perceived cognitive ability, anxiety, and total brain tumor symptoms were evident between pre-GKSRS and 3-month follow-up. Conversely, there was a significant short-term increase in depressive symptoms at post-GKSRS; however, levels did not differ from pre-GKSRS at follow-up. No significant changes were evident on PROs of headaches or fatigue. About half of the participants (46-51%) experienced reliable improvement in global HRQoL, and one-third (31-34%) reported improved anxiety symptoms. Increased depressive symptoms was seen in 34% of participants post-GKSRS and 18% at follow-up. CONCLUSIONS: At 3 months post-GKSRS, improvements in HRQoL, anxiety, perceived cognitive ability, and total brain tumor symptoms were evident. Routine monitoring and support for pre-GKSRS anxiety and depressive symptoms post-GKSRS is recommended.

Individuals receiving Gamma Knife radiosurgery for benign brain tumor experience positive impacts on global, emotional, and physical HRQoL, perceived cognitive ability, anxiety, and overall brain tumor symptoms following treatment.Due to heightened levels of anxiety observed prior to treatment, routine screening and support for anxiety are recommended before individuals receive Gamma Knife radiosurgery.Symptoms of depression may increase following Gamma Knife radiosurgery, therefore routine monitoring and management of mood symptoms is recommended after treatment.Evidence-based psychological interventions are needed for healthcare professionals to support individuals undergoing Gamma Knife radiosurgery.

Brain tumor detection and segmentation using deep learning.

OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the heterogeneous nature of brain tumors. Different deep learning-based algorithms are available for object detection; however, the performance of detection algorithms on brain tumor data has not been widely explored. Therefore, we aim to compare different object detection algorithms (Faster R-CNN, YOLO & SSD) for brain tumor detection on MRI data. Furthermore, the best-performing detection network is paired with a 2D U-Net for pixel-wise segmentation of abnormal tumor cells. MATERIALS AND METHODS: The proposed model was evaluated on the Brain Tumor Figshare (BTF) dataset, and the best-performing detection network cascaded with 2D U-Net for pixel-wise segmentation of tumors. The best-performing detection network was also fine-tuned on BRATS 2018 data to detect and classify the glioma tumor. RESULTS: For the detection of three tumor types, YOLOv5 achieved the highest mAP of 89.5% on test data compared to other networks. For segmentation, YOLOv5 combined with 2D U-Net achieved a higher DSC compared to the 2D U-Net alone (DSC: YOLOv5 + 2D U-Net = 88.1%; 2D U-Net = 80.5%). The proposed method was compared with the existing detection and segmentation network i.e. Mask R-CNN and achieved a higher mAP (YOLOv5 + 2D U-Net = 89.5%; Mask R-CNN = 67%) and DSC (YOLOv5 + 2D U-Net = 88.1%; Mask R-CNN = 44.2%). CONCLUSION: In this work, we propose a deep-learning-based method for multi-class tumor detection, classification and segmentation that combines YOLOv5 with 2D U-Net. The results show that the proposed method not only detects different types of brain tumors accurately but also delineates the tumor region precisely within the detected bounding box.

Current immunotherapy techniques in meningioma.

INTRODUCTION: Although meningiomas are the most common primary brain tumor, there are limited treatment options for recurrent or aggressive lesions. Compared to other brain tumors, meningiomas may be uniquely amenable to immunotherapy by virtue of their location outside the blood-brain barrier. AREAS COVERED: This review describes our current understanding of the immunology of the meninges, as well as immune cell infiltration and immune signaling in meningioma. Current literature on meningioma immunology and immunotherapy was comprehensively reviewed and summarized by a comprehensive search of MEDLINE (1/1/1990-6/1/2024). Further, we describe the current state of immunotherapeutic approaches, as well as potential future targets. Potential immunotherapeutic approaches include immune checkpoint inhibition, CAR-T approaches, tumor vaccine therapy, and immunogenic molecular markers. EXPERT OPINION: Meningioma immunotherapy is in early stages, as no immunotherapies are currently included in treatment guidelines. There is substantial heterogeneity in immune cell infiltration, immunogenicity, and immune escape across tumors, even within tumor grade. Furthering our understanding of meningioma immunology and tumor classification will allow for careful selection of tumors and patient populations that may benefit from primary or adjunctive immunotherapy for meningioma.

Application of the bicharacteristic attention residual pyramid for the treatment of brain tumors.

Currently, surgery remains the primary treatment for craniocerebral tumors. Before doctors perform surgeries, they need to determine the surgical plan according to the shape, location, and size of the tumor; however, various conditions of different patients make the tumor segmentation task challenging. To improve the accuracy of determining tumor shape and realizing edge segmentation, a U-shaped network combining a residual pyramid module and a dual feature attention module is proposed. The residual pyramid module can enlarge the receptive field, extract multiscale features, and fuse original information, which solves the problem caused by the feature pyramid pooling where the local information is not related to the remote information. In addition, the dual feature attention module is proposed to replace the skip connection in the original U-Net network, enrich the features, and improve the attention of the model to space and channel features with large amounts of information to be used for more accurate brain tumor segmentation. To evaluate the performance of the proposed model, experiments were conducted on the public datasets Kaggle_3M and BraTS2021. Because the model proposed in this study is applicable to two-dimensional image segmentation, it is necessary to obtain the crosscutting images of fair class in the BraTS2021 dataset in advance. Results show that the model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and false negative rate (FNR) on the Kaggle_3M dataset are 0.9395, 0.8812, 0.8958, and 0.007, respectively. The model accuracy, Jaccard similarity coefficient, Dice similarity coefficient, and FNR on the BraTS2021 dataset were 0.9375, 0.9072, 0.8981, and 0.0087, respectively. Compared with existing algorithms, all the indicators of the proposed algorithm have been improved, but the proposed model still has certain limitations and has not been applied to actual clinical trials. For specific datasets, the generalization ability of the model needs to be further improved. In the future work, the model will be further improved to address the aforementioned limitations.

Treatment Reproducibility in Brain Stereotactic Radiotherapy Using a Shim Mask Versus a Standard Mask.

Introduction This study aimed to evaluate the setup accuracy of the new shim mask with mouth bite compared to the standard full brain mask in stereotactic radiosurgery (SRS) and radiotherapy (SRT) treatments for brain metastases or tumors. Method A combined retrospective and prospective design was employed, involving 40 patients treated at our center. Patients previously treated using standard head masks formed the retrospective cohort, while those treated with the Shim mask and mouth bite formed the prospective cohort. Daily cone-beam computed tomography (CBCT) scans were obtained before each treatment session to ensure patient setup accuracy. Key metrics included absolute shifts in translational and rotational directions, the number of repeat CBCTs, and the time interval between CBCTs. Results The Shim mask significantly reduced the mean setup errors in the lateral translation (p=0.022) from 0.17 cm (SD=0.10) to 0.10 cm (SD=0.10), and in X-axis rotation (p=0.030) from 0.79° (SD=0.43) to 0.47° (SD=0.47). By considering cutoff points of 1 mm in translational and 1° in rotational directions, the Shim mask was significantly more accurate in the lateral direction (p=0.004). Moreover, while 70% of patients in the standard group required repeat CBCT scans, none in the Shim group did, resulting in an average time saving of 10.4 minutes per patient. Conclusion The Shim mask with mouth bite offers enhanced immobilization accuracy in SRT/SRS treatments, leading to time and potential cost savings by reducing the need for repeat CBCT scans. This underscores the importance of adopting innovative immobilization techniques to optimize patient outcomes.

Integrative Oncology for High-Grade Glioma: A Case Report on the Combined Effects of Oncothermia and Complementary Therapies.

High-grade gliomas are aggressive brain tumors with a poor prognosis despite conventional treatments such as surgery, radiation, and chemotherapy. Integrative oncology, combining conventional and complementary therapies, may offer additional benefits in managing these complex cases. We present a 68-year-old male farmer diagnosed with high-grade glioma in the left medial temporal lobe. The patient presented with severe headache, disturbed sleep, and anxiety, and experienced an episode of fever and seizure. He refused conventional radiation therapy due to concerns about side effects and opted for an integrative medicine protocol. This protocol included oncothermia, high-dose vitamin C therapy, hydrogen inhalation, ozone therapy, magnet therapy, fasting, acupuncture, pulsed electromagnetic field therapy, yoga therapy, hydrotherapy, biologicals, and dietary modifications. The patient underwent 12 sessions of oncothermia over 24 days, combined with other integrative therapies. MRI scans before and after treatment showed a reduction in tumor size from 3.6 x 2.9 x 2.5 cm to 3.4 x 2.7 x 2.5 cm, corresponding to a 12% decrease in volume. Hematological parameters (complete blood count, liver function test, kidney function test, C-reactive protein), cancer markers (carcinoembryonic antigen, lactate dehydrogenase), and mental health indices (quality of life, survival rate) also showed significant improvement. The patient experienced no adverse events and reported enhanced quality of life. This case report suggests that an integrative oncology approach, combining oncothermia and various complementary therapies, may be an effective treatment option for high-grade gliomas, particularly for patients intolerant to conventional therapies. Further research, including randomized controlled trials, is necessary to validate these findings and determine the specific contributions of each therapy.

Primary Central Nervous System Lymphoma Presenting as a Solitary Fourth Ventricular Mass: A Case Report.

The occurrence of primary fourth ventricular lymphoma is an exceptionally uncommon phenomenon. Here, we present a case of lymphoma in the fourth ventricle in a 30-year-old male who presented with progressive headache and vertigo over the last one month of his presentation. Preoperative MRI revealed a space-occupying lesion of the fourth ventricle. Pathological analysis following complete resection confirmed the lesion as primary central nervous system lymphoma. The patient underwent chemotherapy following the MTR (methotrexate, temozolomide, and rituximab) protocol with four months of uneventful follow-up, indicating no disease recurrence. Therefore, clinicians are advised to consider the potential presence of lymphoma as part of the differential diagnosis for space-occupying lesions, especially when there is a combination of clinical deterioration and rapid imaging progression.

The Potential of Circulating Cell-Free RNA in CNS Tumor Diagnosis and Monitoring: A Liquid Biopsy Approach.

Early detection of malignancies, through regular cancer screening, has already proven to have potential to increase survival rates. Yet current screening methods rely on invasive, expensive tissue sampling that has hampered widespread use. Liquid biopsy is noninvasive and represents a potential approach to precision oncology, based on molecular profiling of body fluids. Among these, circulating cell-free RNA (cfRNA) has gained attention due to its diverse composition and potential as a sensitive biomarker. This review provides an overview of the processes of cfRNA delivery into the bloodstream and the role of cfRNA detection in the diagnosis of central nervous system (CNS) tumors. Different types of cfRNAs such as microRNAs (miRNAs), long noncoding RNAs (lncRNAs) and circular RNAs (circRNAs) have been recognized as potential biomarkers in CNS tumors. These molecules exhibit differential expression patterns in the plasma, cerebrospinalfluid (CSF) and urine of patients with CNS tumors, providing information for diagnosing the disease, predicting outcomes, and assessing treatment effectiveness. Few clinical trials are currently exploring the use of liquid biopsy for detecting and monitoring CNS tumors. Despite obstacles like sample standardization and data analysis, cfRNA shows promise as a tool in the diagnosis and management of CNS tumors, offering opportunities for early detection, personalized therapy, and improved patient outcomes.

Brain tumoroids: treatment prediction and drug development for brain tumors with fast, reproducible and easy-to-use personalized models.

BACKGROUND: generation of patient avatar is critically needed in neuro-oncology for treatment prediction and preclinical therapeutic development. Our objective was to develop a fast, reproducible, low-cost and easy-to-use method of tumoroids generation and analysis, efficient for all types of brain tumors, primary and metastatic. METHODS: tumoroids were generated from 89 patients: 81 primary tumors including 77 gliomas, and 8 brain metastases. Tumoroids morphology, cellular and molecular characteristics were compared with the ones of the parental tumor by using histology, methylome profiling, pTERT mutations and multiplexed spatial immunofluorescences. Their cellular stability overtime was validated by flow cytometry. Therapeutic sensitivity was evaluated and predictive factors of tumoroid generation were analyzed. RESULTS: All the tumoroids analyzed had similar histological (N=21) and molecular features (N=7) than the parental tumor. Median generation time was 5 days. Success rate was 65 %: it was higher for high grade gliomas and brain metastases versus IDH mutated low grade gliomas. For high-grade gliomas, neither other clinical, neuro-imaging, histological nor molecular factors were predictive of tumoroid generation success. The cellular organization inside tumoroids analyzed by MACSima revealed territories dedicated to specific cell subtypes. Finally, we showed the correlation between tumoroid and patient treatment responses to radio-chemotherapy and their ability to respond to immunotherapy thanks to a dedicated and reproducible 3D analysis workflow. CONCLUSION: patient-derived tumoroid model that we developed offers a robust, user-friendly, low-cost and reproducible preclinical model valuable for therapeutic development of all type of primary or metastatic brain tumors, allowing their integration into forthcoming early-phase clinical trials.

Locoregional CAR T Cells for the Treatment of CNS Tumors in Children: Investigational Drug Service Pharmacy Activities.

BACKGROUND: A major obstacle in translating the therapeutic potential of chimeric antigen receptor (CAR) T cells to children with central nervous system (CNS) tumors is the blood-brain barrier. To overcome this limitation, preclinical and clinical studies have supported the use of repeated, locoregional intracranial CAR T-cell delivery. However, there is limited literature available describing the process for the involvement of an investigational drug service (IDS) pharmacy, particularly in the setting of a children's hospital with outpatient dosing for CNS tumors. OBJECTIVES: To describe Seattle Children's Hospital's experience in clinically producing CAR T cells and the implementation of IDS pharmacy practices used to deliver more than 300 intracranial CAR T-cell doses to children, as well as to share how we refined the processing techniques from CAR T-cell generation to the thawing of fractionated doses for intracranial delivery. METHODS: Autologous CD4+ and CD8+ T cells were collected and transduced to express HER2, EGFR, or B7-H3-specific CAR T cells. Cryopreserved CAR T cells were thawed by the IDS pharmacy before intracranial delivery to patients with recurrent/refractory CNS tumors or with diffuse intrinsic pontine glioma/diffuse midline glioma. RESULTS: The use of a thaw-and-dilute procedure for cryopreserved individual CAR T-cell doses provides reliable viability and is more efficient than typical thaw-and-wash protocols. Cell viability with the thaw-and-dilute protocol was approximately 75% and was always within 10% of the viability assessed at cryopreservation. Cell viability was preserved through 6 hours after thawing, which exceeded the 1-hour time frame from thawing to infusion. CONCLUSION: As the field of adoptive immunotherapy grows and continues to bring hope to patients with fatal CNS malignancies, it is critical to focus on improving the preparatory steps for CAR T-cell delivery.

A Deep Learning Based Intelligent Decision Support System for Automatic Detection of Brain Tumor.

Brain tumor (BT) is an awful disease and one of the foremost causes of death in human beings. BT develops mainly in 2 stages and varies by volume, form, and structure, and can be cured with special clinical procedures such as chemotherapy, radiotherapy, and surgical mediation. With revolutionary advancements in radiomics and research in medical imaging in the past few years, computer-aided diagnostic systems (CAD), especially deep learning, have played a key role in the automatic detection and diagnosing of various diseases and significantly provided accurate decision support systems for medical clinicians. Thus, convolution neural network (CNN) is a commonly utilized methodology developed for detecting various diseases from medical images because it is capable of extracting distinct features from an image under investigation. In this study, a deep learning approach is utilized to extricate distinct features from brain images in order to detect BT. Hence, CNN from scratch and transfer learning models (VGG-16, VGG-19, and LeNet-5) are developed and tested on brain images to build an intelligent decision support system for detecting BT. Since deep learning models require large volumes of data, data augmentation is used to populate the existing dataset synthetically in order to utilize the best fit detecting models. Hyperparameter tuning was conducted to set the optimum parameters for training the models. The achieved results show that VGG models outperformed others with an accuracy rate of 99.24%, average precision of 99%, average recall of 99%, average specificity of 99%, and average f1-score of 99% each. The results of the proposed models compared to the other state-of-the-art models in the literature show better performance of the proposed models in terms of accuracy, sensitivity, specificity, and f1-score. Moreover, comparative analysis shows that the proposed models are reliable in that they can be used for detecting BT as well as helping medical practitioners to diagnose BT.

Optimizing post-craniotomy recovery: insights from symptom network analysis in primary brain tumor patients.

BACKGROUND: Craniotomy to remove brain tumors is an intricate procedure with multiple postoperative symptoms. However, there has been limited research on the symptom networks of these patients. To this end, this study aims to explore these symptom networks, revealing their interplay to inform better symptom control, hasten the discovery of postoperative issues, and tailor Enhanced Recovery After Surgery (ERAS) protocols, all to enhance recovery and enhance patient care. METHODS: From September 2023 to March 2024, 211 patients with primary brain tumors who underwent craniotomy at Shanghai Tongji Hospital were recruited. Their symptoms were assessed using the MDASI-BT (M.D. Anderson Symptom Inventory Brain Tumor Module) one day post-craniotomy. The symptom network of 22 symptoms was visualized using R, with central and bridge symptoms identified. RESULTS: Sadness (rs=2.482) and difficulty in understanding (rs=1.138) have the highest strength of all symptoms, indicating they are the central symptoms. Sadness (rb=2.155) and loss of appetite (rb=1.828) have the highest value of betweenness, indicating they are the bridge symptoms. Strong correlations were found between difficulty in understanding and difficulty in speaking (r = 0.701), distress and sadness (r = 0.666), fatigue and lethargy (r = 0.632), and nausea and vomiting (r = 0.601). Subgroup analysis revealed that noninvasive tumor patients exhibited similar symptom networks to the overall cohort, whereas invasive tumor patients showed weak symptom connections, resulting in no discernible network. CONCLUSION: This study underscores the importance of understanding symptom networks in brain tumor patients post-craniotomy, highlighting key symptom interrelationships. These insights can guide more effective symptom management, early complication detection, and optimization of ERAS protocols, ultimately enhancing recovery and patient care.

Brain MRI Protocol and Systematic Approach to Interpretation of Brain Tumors on MRI.

MRI plays an integral role in the diagnosis of brain tumors in dogs and cats. Optimized image acquisition protocols in addition to a systematic approach to brain tumor evaluation on MRI using imaging characteristic interpretation criteria may allow for enhanced lesion detection, accurate presumptive diagnoses, and formulation of a prioritized differential diagnosis list.

Artificial Intelligence and Deep Learning in Revolutionizing Brain Tumor Diagnosis and Treatment: A Narrative Review.

The emergence of artificial intelligence (AI) in the medical field holds promise in improving medical management, particularly in personalized strategies for the diagnosis and treatment of brain tumors. However, integrating AI into clinical practice has proven to be a challenge. Deep learning (DL) is very convenient for extracting relevant information from large amounts of data that has increased in medical history and imaging records, which shortens diagnosis time, that would otherwise overwhelm manual methods. In addition, DL aids in automated tumor segmentation, classification, and diagnosis. DL models such as the Brain Tumor Classification Model and the Inception-Resnet V2, or hybrid techniques that enhance these functions and combine DL networks with support vector machine and k-nearest neighbors, identify tumor phenotypes and brain metastases, allowing real-time decision-making and enhancing preoperative planning. AI algorithms and DL development facilitate radiological diagnostics such as computed tomography, positron emission tomography scans, and magnetic resonance imaging (MRI) by integrating two-dimensional and three-dimensional MRI using DenseNet and 3D convolutional neural network architectures, which enable precise tumor delineation. DL offers benefits in neuro-interventional procedures, and the shift toward computer-assisted interventions acknowledges the need for more accurate and efficient image analysis methods. Further research is needed to realize the potential impact of DL in improving these outcomes.

Introduction and Summary of Diagnosis and Treatment of Brain Tumors in Dogs and Cats.

Meningiomas are the most common tumor type in the brain in dogs and cats, and survival times are much higher for cats than dogs. Glioma is much more common in the dog, and median survival time is poor without definitive therapy. No recommendations currently exist for treatment of glioma in dogs, and there is ongoing research as the dog is a valid spontaneous model for the human equivalent disease. Other intracranial tumor types like lymphoma and histiocytic sarcoma do occur, though at a much lower frequency.

MR-guided focused ultrasound in pediatric neurosurgery: current insights, technical challenges, and lessons learned from 45 treatments at Children's National Hospital.

OBJECTIVE: MR-guided focused ultrasound (MRgFUS) is an evolving technology with numerous present and potential applications in pediatric neurosurgery. The aim of this study was to describe the use of MRgFUS, technical challenges, complications, and lessons learned at a single children's hospital. METHODS: A retrospective analysis was performed of a prospectively collected database of all pediatric patients undergoing investigational use of MRgFUS for treatment of various neurosurgical pathologies at Children's National Hospital. Treatment details, clinical workflow, and standard operating procedures are described. Patient demographics, procedure duration, and complications were obtained through a chart review of anesthesia and operative reports. RESULTS: In total, 45 MRgFUS procedures were performed on 14 patients for treatment of diffuse intrinsic pontine glioma (n = 12), low-grade glioma (n = 1), or secondary dystonia (n = 1) between January 2022 and April 2024. The mean age at treatment was 9 (range 5-22) years, and 64% of the patients were male. With increased experience, the total anesthesia time, sonication time, and change in core body temperature during treatment all significantly decreased. Complications affected 4.4% of patients, including 1 case of scalp edema and 1 patient with a postprocedure epidural hematoma. Device malfunction requiring abortion of the procedure occurred in 1 case (2.2%). Technical challenges related to transducer malfunction and sonication errors occurred in 6.7% and 11.1% of cases, respectively, all overcome by subsequent user modifications. CONCLUSIONS: The authors describe the largest series on MRgFUS technical aspects in pediatric neurosurgery at a single institution, comprising 45 total treatments. This study emphasizes potential technical challenges and provides valuable insights into the nuances of its application in pediatric patients.

Intraoperative ultrasound and magnetic resonance comparative analysis in brain tumor surgery: a valuable tool to flatten ultrasound's learning curve.

BACKGROUND: Intraoperative ultrasound (IOUS) is a profitable tool for neurosurgical procedures' assistance, especially in neuro-oncology. It is a rapid, ergonomic and reproducible technique. However, its known handicap is a steep learning curve for neurosurgeons. Here, we describe an interesting postoperative analysis that provides extra feedback after surgery, accelerating the learning process. METHOD: We conducted a descriptive retrospective unicenter study including patients operated from intra-axial brain tumors using neuronavigation (Curve, Brainlab) and IOUS (BK-5000, BK medical) guidance. All patients had preoperative Magnetic Resonance Imaging (MRI) prior to tumor resection. During surgery, 3D neuronavigated IOUS studies (n3DUS) were obtained through craniotomy N13C5 transducer's integration to the neuronavigation system. At least two n3DUS studies were obtained: prior to tumor resection and at the resection conclusion. A postoperative MRI was performed within 48 h. MRI and n3DUS studies were posteriorly fused and analyzed with Elements (Brainlab) planning software, permitting two comparative analyses: preoperative MRI compared to pre-resection n3DUS and postoperative MRI to post-resection n3DUS. Cases with incomplete MRI or n3DUS studies were withdrawn from the study. RESULTS: From April 2022 to March 2024, 73 patients were operated assisted by IOUS. From them, 39 were included in the study. Analyses comparing preoperative MRI and pre-resection n3DUS showed great concordance of tumor volume (p < 0,001) between both modalities. Analysis comparing postoperative MRI and post-resection n3DUS also showed good concordance in residual tumor volume (RTV) in cases where gross total resection (GTR) was not achieved (p < 0,001). In two cases, RTV detected on MRI that was not detected intra-operatively with IOUS could be reviewed in detail to recheck its appearance. CONCLUSIONS: Post-operative comparative analyses between IOUS and MRI is a valuable tool for novel ultrasound users, as it enhances the amount of feedback provided by cases and could accelerate the learning process, flattening this technique's learning curve.