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.
CONCLUSIONS: 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.

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.

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.

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.
CONCLUSIONS: 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 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.

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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.

OBJECTIVE: 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.
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%).
CONCLUSIONS: 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.

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.

UNASSIGNED: 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.
UNASSIGNED: A survey was conducted among the KBTS members to understand their perceptions and concerns across different career stages.
UNASSIGNED: 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.
UNASSIGNED: 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.