Cases Of Brain Tumors Research Articles

Analysis of geographical distribution of high-grade glioma diagnoses in the Alessandria province: pilot study preliminary results

Background: one under-researched line of investigation is gliomas, which are a group of malignant brain tumours with highly poor prognoses. Despite efforts to identify environmental risk factors for gliomas, their etiology still remains unclear. It turns out that several future developments are needed in order to understand other potential risk factors. To this end, the primary objective of the present pilot study is to conduct an assessment of the spatial distribution of high-grade brain tumor cases in the province of Alessandria, where possible risk factors such as the presence of chemical industries and asbestiform fibers are found. Materials and Methods: the study sample consisted of all patients diagnosed with high-grade glioma between January 2018 and April 2023, residents in the province of Alessandria at the time of diagnosis and referred to the health facilities of the "Azienda Sanitaria Locale di Alessandria" and the "Azienda Universitario-Ospedaliera di Alessandria". Biographical and pathological history variables were collected, and finally, data were processed in aggregate and anonymized form. A total of 103 deceased patients were enrolled, stratified by age and type of diagnosis. Results: it was seen that the city with the most cases was Alessandria, with 27 diagnoses found during the period under review (26.2%), followed by cities of Acqui Terme, Casale Monferrato, and Novi Ligure with seven diagnoses (6.8%). Preliminary results presented in this paper were compared with data already published. Conclusions: analyses performed to arrive at an initial geographical distribution of high-grade glioma diagnoses are consistent relative to the population density of each municipality considered.

Development of Prediction Models to Detect the Presence of MGMT Promoter Methylation for Prognosis of Brain Tumor

Methylation of the MGMT promoter is a molecular marker of predictive relevance in brain tumors. Methylation of the MGMT promoter is related to an improved prognosis and may influence therapy decisions. According to the most recent Malaysian National Cancer Registry Report (MNCRR) for 2012–2016, there were 2097 cases of brain tumors in Malaysia overall, with 1117 cases involving men and 908 cases involving women. This research aims to create prediction models for detecting MGMT promoter methylation in brain tumors. So, a dataset from the BraTS (Brain Tumor Segmentation) challenge, which included MRI scans and clinical data for patients with brain tumors, was utilized and explored using Exploratory Data Analysis (EDA) and data visualization techniques. The dataset has 306 methylation cases identified as “1” and 276 unmethylated cases identified as “0”. The average number of scans of train data for modality per patient is between 127 and 171, which provides a wealth of information for pattern learning while the average number of scans of test data for modality per patient is between 124 and 165. In both sets, FLAIR has the least number of files while T2w has the highest number of files among them. Two models of deep learning approaches, ResNet50 and EfficientNetV2, were used to construct and form the prediction models. Various criteria were employed to evaluate the performance of the models. The T2w modality consistently achieved the highest validation accuracy, precision, recall, and F1-score for both the ResNet50 and EfficientNetV2 models. Specifically, the T2w modality achieved a validation accuracy of 0.9453 and a validation loss of 0.2417 for ResNet50. Furthermore, the brain tumor diagnosis interface utilized DICOM images from MRI scans to identify MGMTp methylation status, aiding in therapy effectiveness prediction. Pre-trained ResNet50 model on T2w images was used for classification. The interface displayed the original MRI image, predicted state, and treatment effectiveness indication, while promptly notifying users of invalid or inadequate data for accurate analysis.

Detection and classification on MRI images of brain tumor using YOLO NAS deep learning model

If a brain tumor is not properly diagnosed, it might result in fatal consequences and major health issues. As a result, a key component of diagnosis is the early identification of brain tumors and the precise categorization of brain tumor types. This research work focusses on detection and classification such as pituitary, meningioma, glioma, and non-tumorous tumors from brain tumor MRI image using YOLO NAS model. Advances in deep learning have led to an increasing awareness of computer-aided diagnosis technology. To improve classification performance, the input data set is acquired from the REMBRANDT repository using the Digital Database of Brain Tumor Magnetic Resonance Images. The primary phases of this task include segmentation, classification, and pre-processing. The researcher preprocessed the RGB image to exclude any undesired spots before locating the ROI. For each brain tumor image, we used the hybrid anisotropic diffusion filtering (HADF) technique to remove noise. The Encoder-Decoder Network (En–DeNet), which uses a deep neural network based on U-Net as the encoder and a pre-trained EfficientNet as the decoder, is then used to segment the MRI images. A proposed model was developed using a deep learning-based YOLO NAS technique to identify brain cancers from MRI images such as meningioma tumors, non-tumor, glioma tumors, and pituitary tumors. The suggested model's classification performance is weighed based on parameters such as precision, F1-score, sensitivity, accuracy, and specificity. The proposed fusion method YOLO NAS has improved classification results of accuracy (AC = 0.997%), specificity (SP = 0.985%), precision (PR = 0.982%), F1-score (F1 = 0.992%), and sensitivity (SE = 0.985%) using 2570 MRI data for training and 630 data for testing and validation of brain tumor cases. The results show that the brain tumor type classification system based on the proposed YOLO NAS technique works better than the DNN, PDCNN, DenseNet-161, and DCNN-SGD model classifiers.

PDOC-03 CASE SERIES ON PEDIATRIC PATIENTS WITH LEPTOMENINGEAL METASTASES FROM VARIOUS PRIMARY BRAIN TUMORS: FROM CLINICAL PRESENTATIONS TO OUTCOMES

Abstract Leptomeningeal disease (LMD), also known as carcinomatous meningitis, is when cancer cells are found in the thin layers covering the brain and spinal cord, in the spaces between these layers, or in the fluid surrounding the brain and spinal cord. In children with brain tumors, LMD is rare, occurring in about 1% to 2% of cases. It’s most commonly seen in certain types of tumors like medulloblastoma and primary central nervous system lymphoma, but it can also occur in other types such as pineoblastoma, Atypical Teratoid Rhabdoid Tumor, and both low-grade and high-grade gliomas. Although pilocytic astrocytomas, a type of low-grade glioma, seldom spread, there have been a few reported cases of them spreading to the leptomeninges. The exact prevalence of LMD in pediatric brain tumors isn’t well-documented due to differences in how it’s screened for across different types of tumors and at different stages of the disease. Improved surgical methods and various treatments targeting specific areas or affecting the whole body have resulted in better survival rates for patients with LMD. Detecting LMD early is important for treatment options. It can be found either at the same time as the primary brain tumor, when the tumor comes back, or as LMD without an initial tumor. There’s concern that procedures to divert cerebrospinal fluid might spread cancer cells. MRI scans are important for diagnosing LMD, but it can still be challenging to diagnose based on these scans alone. Usually, confirmation is done by analyzing the fluid around the brain and spinal cord for cancer cells. Given that LMD is a complex and rare complication of pediatric brain tumors, more research is needed to improve diagnosis and treatment. In this article, we’ll discuss three cases of pediatric brain tumors with LMD, including how they were diagnosed, treated, and their clinical outcomes.

PDOC-07 THE CLINICOPATHOLOGICAL PROFILE AND VALUE OF MULTIDISCIPLINARY MANAGEMENT OF PEDIATRIC BRAIN TUMORS IN A LOW-INCOME SETTING

Abstract OBJECTIVE The management of paediatric brain tumours in low-income countries is challenging, and survival rates are low, making multi-disciplinary teams (MDT) inevitable in quality management. In Uganda, paediatric neuro-oncology services are in their infancy. This study evaluated the management of paediatric brain tumours at the Uganda Cancer Institute (UCI) to assess the contributions of MDT management in improving patient care and outcomes. METHODS A retrospective records review of paediatric brain tumours at the UCI between 2017 and 2021 was conducted. Patient and tumour characteristics, as well as MDT management, were sourced. A survey among MDT members was conducted to explore their perspectives. RESULTS Thirty-five paediatric brain tumour cases were treated at UCI, with a median age of 8.0 years (IQR 4.0–11.0) and a median duration of symptoms of six months (IQR 3.0-8.5). The majority (25.7%, n=9) were craniopharyngiomas, 14.3% (5/35) were astrocytomas, 11.4% (4/35) were medulloblastomas, and 8.6% (3/35) each were ependymomas and pineoblastomas. Overall, 22 (62.9%) of the cases were discussed on the MDT. The number of cases discussed in the MDT increased over the five years: 0/3 (0.0%) in 2017, 3/5 (60.0%) in 2018, 2/4 (50.0%) in 2019, 6/9 (66.7%) in 2020, and 11/14 (78.6%) in 2021. The proportion of multidisciplinary management decisions implemented also increased: 0% in 2018, 50% in 2019, 83.3% in 2020, and 81.8% in 2021. The majority of patients (12/15, 80.0%) for whom MDT recommendations were implemented were alive. Lack of neurosurgical resources (46.7%), imaging (46.6%), diversity of MDT roles (26.7%), access to radiotherapy (20.0%), and pathology (20.0%) were recognized as major impediments to paediatric neuro-oncology management. CONCLUSIONS Implementing multidisciplinary management decisions benefited children with brain tumours and decreased abandonment and patient loss to follow-up at the Uganda Cancer Institute. Greater emphasis should be placed on increasing resources and actionable treatment recommendations.

OTHR-09 EPIDEMIOLOGY OF BRAIN TUMORS IN ZIMBABWE

Abstract OBJECTIVE Brain tumors, while rare, cause significant morbidity and mortality across all ages. This study was done to help understand the age and sex distribution of the common brain cancers diagnosed in Zimbabwe. METHODS A retrospective descriptive analysis was done of the epidemiology of brain tumor cases presenting over the past 5years at a single center in Zimbabwe. RESULTS The Total number of brain tumor cases attended to during this period was 162. Of these, 26% (42cases) were meningiomas, while 18.3% (30cases) were pituitary tumors. 12% (20cases) were posterior fossa tumors. 35 cases (22%) were unclassified brain tumors. Other brain tumors which were noted were glioma (8), craniopharyngioma (4), ependymoma (3), lymphoma (2) as well as confirmed metastatic disease (6). Meningiomas had a male to female ratio of 1:2, with a peak incidence of 40-50years (35%) whilst pituitary tumors had a 1:1 male to female ratio with 54% presenting between 30-50years. Posterior fossa tumors are most common in children and adolescents, with 53% of the cases presenting below the age of 20. They have a 1:1 ratio between males and females. CONCLUSION Most brain tumors had an equal distribution between males and females with the exception of meningioma which have a 1:2 male:female ratio. In the young and adolescents posterior fossa tumors are the commonest, with pituitary tumors and meningiomas being more common between 30-50years.

PADV-01 CHALLENGES OF NEURO-ONCOLOGY CARE IN ZIMBABWE

Abstract Due to the advent of covid 19 coupled with the socio-economic crisis in Zimbabwe, this has resulted in numerous challenges in terms of health care access and resultant diagnostic and treatment barriers for neuro-oncology cases. A retrospective analysis of the burden of neuro-oncology cases which have been seen at a single center in Zimbabwe over the past 5 years and the resultant diagnostic and treatment barriers that have incurred. METHODS An audit of brain tumor cases was done through the analysis of Outpatient department records, Neurosurgery Admission records, and Neurosurgery theatre records at Sally Mugabe Central hospital. RESULTS Total number of cases recorded for the period were 71. Meningiomas 11 (15,5%), Posterior fossa tumors 12 (17%), Gliomas 6 (0.08%), Craniopharyngiomas 6(0.08%), Metastasis 5(0.07%), Pituitary tumors 4(0.06%), choroid plexus papilloma 3(0.04%), Lymphoma 2(0.03%), Pineal region tumor 1(0.01%). 21(30%) tumors were unclassified. Posterior fossa tumors were most common in the pediatric population less than 10years of age. Meningiomas had a female to male ratio of 1.75: 1. 9 (12%) patients were operated on during the same period mostly pediatric tumors. CONCLUSION There is a great deal of institutional as well as patient related factors affecting the diagnosing and treatment of neuro-oncology care in Zimbabwe. This includes lack of operating time, inadequate intensive care facilities, a general unavailability of medical imaging services, delay in pathological reporting, unavailability and costly adjuvant treatment. This is leading to late diagnosis and suboptimal treatment of the various tumors. Although brain tumors are rare, they are not uncommon and there is thus need to improve service delivery to ensure early diagnosis and treatment to have good outcomes.

Airborne ultrafine particle concentrations and brain cancer incidence in Canada’s two largest cities

BackgroundMalignant brain tumours are rare, but are important to study because survival rates are low and few modifiable risk factors have been identified. Existing evidence suggests that outdoor ultrafine particles (UFPs) (particulate matter < 100 nm, sometimes referred to as nanoparticles) can deposit in the brain and could encourage initiation and progression of cancerous tumours, but epidemiological data are limited. MethodsHigh-resolution estimates of outdoor UFP concentrations and size were linked to residential locations of approximately 1.5 million people in Montreal and Toronto, Canada from 2001 to 2015. Cox proportional hazards models were used to estimate associations between annual average outdoor UFPs and malignant brain tumour incidence while adjusting for potential confounding factors including other outdoor air pollutants. FindingsIn total, 1400 incident brain tumour cases occurred during follow-up. Consistent positive associations were observed between long-term exposures to outdoor UFPs and brain tumour incidence with increased risk ranging from 10.5% (95% CI: −1.4, 24.0%) to 15.3% (95% CI: 0.4, 32.5%) per 10,000 particle/cm3 increase. Long-term exposures to oxidant gases, black carbon, or fine particulate matter (PM2.5) were not associated with increased brain tumour incidence. InterpretationOur results suggest that long-term exposures to outdoor UFPs are associated with an increased risk of developing malignant brain tumours. On an absolute scale, the magnitude of this risk translates into approximately 24 additional cases per year per 10,000 particle/cm3 increase in annual average outdoor UFPs in a hypothetical city of 3-million people. FundingCanadian Institutes of Health Research (CIHR) Foundation Grant and The United States Health Effects Institute (HEI).

Comparative analysis of GPT-4-based ChatGPT's diagnostic performance with radiologists using real-world radiology reports of brain tumors.

Large language models like GPT-4 have demonstrated potential for diagnosis in radiology. Previous studies investigating this potential primarily utilized quizzes from academic journals. This study aimed to assess the diagnostic capabilities of GPT-4-based Chat Generative Pre-trained Transformer (ChatGPT) using actual clinical radiology reports of brain tumors and compare its performance with that of neuroradiologists and general radiologists. We collected brain MRI reports written in Japanese from preoperative brain tumor patients at two institutions from January 2017 to December 2021. The MRI reports were translated into English by radiologists. GPT-4 and five radiologists were presented with the same textual findings from the reports and asked to suggest differential and final diagnoses. The pathological diagnosis of the excised tumor served as the ground truth. McNemar's test and Fisher's exact test were used for statistical analysis. In a study analyzing 150 radiological reports, GPT-4 achieved a final diagnostic accuracy of 73%, while radiologists' accuracy ranged from 65 to 79%. GPT-4's final diagnostic accuracy using reports from neuroradiologists was higher at 80%, compared to 60% using those from general radiologists. In the realm of differential diagnoses, GPT-4's accuracy was 94%, while radiologists' fell between 73 and 89%. Notably, for these differential diagnoses, GPT-4's accuracy remained consistent whether reports were from neuroradiologists or general radiologists. GPT-4 exhibited good diagnostic capability, comparable to neuroradiologists in differentiating brain tumors from MRI reports. GPT-4 can be a second opinion for neuroradiologists on final diagnoses and a guidance tool for general radiologists and residents. This study evaluated GPT-4-based ChatGPT's diagnostic capabilities using real-world clinical MRI reports from brain tumor cases, revealing that its accuracy in interpreting brain tumors from MRI findings is competitive with radiologists. We investigated the diagnostic accuracy of GPT-4 using real-world clinical MRI reports of brain tumors. GPT-4 achieved final and differential diagnostic accuracy that is comparable with neuroradiologists. GPT-4 has the potential to improve the diagnostic process in clinical radiology.

AB094. Association of body mass index with grading and survival of glioma patients.

Glioma is the second most common type of brain tumor, representing 24% of all brain tumor cases. The role of body mass index (BMI) on glioma remains unclear, with conflicting findings regarding the association between higher BMI and the risk of developing certain brain tumors. Glioblastoma, an aggressive and malignant form of glioma with limited treatment options and a poor prognosis, has been linked to BMI in some studies, suggesting that individuals with higher BMIs may have an elevated risk of glioblastoma development. However, a comprehensive understanding of the mechanisms underlying this relationship and its extent is still needed. The study aimed to investigate the correlation between BMI and the grading and survival of glioma patients. A retrospective cross-sectional analysis was conducted on 117 histologically confirmed glioma patients at Dr. Sardjito General Hospital in Yogyakarta, Indonesia. Clinical data were collected from medical records. BMI was calculated by measuring weights (kg) and dividing it by squared heights (m2). The statistical analysis focused on assessing the association between BMI, tumor grade, and patient survival. Among 117 glioma patients, glioblastoma was the most prevalent tumor type (48.7%; n=57/117), followed by diffuse astrocytoma (22%; n=26/117). The remaining cases included anaplastic ependymoma, anaplastic oligodendroglioma, and pilocytic astrocytoma. Most patients were male (61%), with an average age of 47.5 years, age ranges between 20 and 79 years. The majority had grade IV of World Health Organization (WHO) classification (58%, n=68/117), while only two patients were classified as grade I. The average BMI was 23.5 kg/m2, indicating overweight status for the Asian population, with more than half of the patients being overweight or obese (54%, n=63/117). Additionally, ten patients were underweight. There was a trend of higher BMI being associated with higher grading and survival. However, no significant association between BMI and tumor grade (P=0.23) or survival (P=0.26) was found. Although no significant associations were found between BMI, tumor grade, and survival in glioma patients, further studies are warranted. The high prevalence of overweight and obesity among patients should be further investigated to provide valuable insights for patient management and care.

FREQUENCY AND MORPHOLOGICAL PATTERNS OF CNS TUMORS IN A TERTIARY CARE HOSPITAL OF SOUTH PUNJAB

Background: The incidence of CNS tumours is increasing rapidly especially in young population. CNS tumours include a set of neoplasms which originate form brain parenchyma, pituitary gland, pineal gland, spinal cord and meninges. The aim of this study was to observe the frequency and morphological patterns of CNS tumours in a tertiary care hospital of South Punjab. Methods: A cross-sectional descriptive study of 100 cases of brain tumours was conducted at the Pathology Department of DG Khan Medical College, Dera Ghazi Khan. One-hundred cases of CNS tumour biopsies were included with the relevant clinical and radiological findings. Results: A total of 100 cases of CNS tumours were studied. The morphological distribution of various CNS tumours was as follows: Astrocytoma (49%), Meningioma (19%), Schwannoma (8%), Oligodandroglioma (6%), Metastatic tumour (4%), Ependymoma (4%), Pituitary Adenoma (2%), Medulloblastoma (2%), Mixed Gliomas (2%), Germ Cell Tumours (1%), Craniopharyngioma (1%), Lymphoma (1%), Vascular tumours (1%). Conclusion: Among the CNS tumours Astrocytomas were the commonest tumours followed by Meningiomas, Schwannoma and Oligodendroglioma. Pak J Pathol 2024;20(2):24-6

Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture

According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. The National Cancer Institute reports that there are approximately 4.4 new cases of brain tumors per 100,000 men and women per year. Brain tumors can be detected using magnetic resonance imaging (MRI), a scanning tool that uses a magnetic field and a computer to record brain images and is able to provide clear visualization of differences in soft tissue such as white matter and gray matter. However, this cannot be done optimally because it still relies on manual analysis, so it cannot classify brain tumor types on larger datasets with the potential for error and a low level of accuracy. To accurately determine the type of brain tumor, a better classification method is needed. The aim of this study is to determine the accuracy of brain tumor calcification using the deep learning model. In this study, the classification of brain tumor types was carried out using the ResNet152V2 convolutional neural network (CNN) model which has a depth of 152 layers. The dataset used in this study was 7,023 MRI images of brain tumors consisting of 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary and 2,000 normal. Research results show an accuracy value of 94.44%, so it can be concluded that the ResNet152V2 model performs well in classifying brain tumor images and can be used as a medium for physicians to more accurately diagnose brain tumor patients more accurately.

ATRT-20. ATYPICAL TERATOID RHABDOID TUMOR: ASSOCIATION OF CLINICAL, HISTOLOGICAL, RADIOLOGICAL, MOLECULAR, AND THERAPEUTIC DATA WITH THE SURVIVAL OF PATIENTS ATTENDED AT IOP/GRAACC/UNIFESP

Abstract BACKGROUND In childhood, Atypical Rhabdoid Teratoid Tumor accounts for 2% of cases of brain tumors. There is no consensus for the treatment, resulting in multiple protocols. METHODS Retrospective study at the Institute of Pediatric Oncology-IOP/GRAACC/UNIFESP, from January 2000 to December 2022. There were 31 patients admitted during this period. The classification of the histological type into rhabdoid/classic, small round and blue cells, epithelial, and mesenchymal was carried out by the same pathologist, and a 50% cutoff characterized the predominant pattern. A neuro-radiologist reviewed the radiological data. Molecular analysis was performed using NGS. RESULTS In patients &amp;lt;3yo, 74.9% of patients died. Overall survival (OS) at 3 years was 24.5%, and progression-free survival (PFS) at 3 years was 13.8%. In patients &amp;lt;3yo, the absence of metastases was associated with a higher OS and PFS (3y-OS 35.6% and 3y-PFS 23.7%, vs 3y-OS 10% and 3y-PFS 0% in patients with M+). The OS was higher in patients who received radiotherapy at the first treatment (3y-OS of 66.6% vs 7.8% in the non-irradiated group). The classic histological pattern had a lower PFS than the epithelial and small cell groups (p=0.021). In patients&amp;gt;3 yo, 57.1% of patients died. The 3y-OS was 38.1% and the 3y-PFS was 53.5%. In this age group, there was no difference in OS and PFS considering metastases, location, histological pattern, and tumor volume. For post-surgical status, only PFS was significantly higher in patients with complete resection (3y-PFS of 75%, p=0.008). The mean survival time in patients &amp;lt;3 yo was 1.9 years, and the mean survival time in patients &amp;gt;3 yo was 2.5 years. The genetic alterations found were SMARCB1, NOTCH1, and KMT2D. CONCLUSION Patients &amp;gt;3yo seem to have a higher OS and PFS than those &amp;lt;3 yo. Early diagnosis of ATRT may corroborate to a better outcome. The radiotherapy in the first treatment can improve the outcome of patients under 3 years of age. The classic histological pattern was associated with worse PFS.

SURG-03. VIRTUAL REALITY ASSISTANCE IN THE MANAGEMENT OF PEDIATRIC BRAIN TUMORS

Abstract BACKGROUND Virtual reality (VR) solutions are emerging as a useful adjunct to the armamentarium of the neurosurgeon. Surprisingly, little data is available in the literature regarding its utility in pediatric neurosurgical oncology. We hereby report our preliminary Institutional experience on the application of VR to surgical rehearsal in a series of pediatric brain tumors. METHODS Virtual models were obtained by the Surgical Planner (Surgical Theatre, Inc) in a series of 50 consecutive brain tumor cases treated at Bambino Gesù Children’s Hospital. Utility of VR was scored based on operators’ perception. Explored items included: planning, execution, resection, sparing of eloquent tissue, and overall surgical management. Scoring was performed using a customized Likert scale. RESULTS We prospectively collected 100 consecutive questionnaires from surgeons participating to brain tumor resection procedures. In a multivariate analysis VR was found to be significantly more beneficial in specific surgical settings. Leading surgeons (LS) judged VR significantly more useful in supratentorial tumors, in children younger than 10 years, and in planned partial resections. Assistant surgeons (AS) found VR more useful in supratentorial lesions, particularly in deep seated tumors. Both LS and AS perceived VR to be less useful for in embryonal tumors and epilepsy-associated gliomas (LEAT). CONCLUSIONS Our data suggest that VR technology is useful in the surgical management of children with brain tumors. There might be specific clinical scenarios in which the advantage of VR availability might have a significant clinical impact in planning and performing surgical resection.

IMG-28. AUTOMATIC BRAIN TUMOR VOLUMETRIC ANALYSIS IN MAGNETIC RESONANCE IMAGING GENERALIZABLE TO PEDIATRIC NEURO-ONCOLOGY

Abstract BACKGROUND The prognosis of brain tumors is variable in clinical practice if it only relies on human interpretation of magnetic resonance imaging (MRI). The automatic segmentation of brain tumors in MRI enables quantitative analysis in support of clinical trials and personalized patient care. We developed benchmarked deep learning-based tools that are generalizable to the volumetric quantification of various tumor types across diverse populations. METHODS We participated in the well-established international brain tumor segmentation challenge (BraTS 2023) and benchmarking competition. The challenge made available 4,500 multi-national brain tumor cases with multi-sequence MRIs, including pediatric high-grade gliomas (PED), i.e., high-grade astrocytoma and diffuse midline glioma, and adult gliomas, brain metastases (MET) and intracranial meningiomas (MEN). Each case comprises four MRI volumes: T1, contrast-enhanced T1, T2, and T2-FLAIR. Manual segmentations were provided to establish ground truth for enhancing tumor (ET), tumor core (TC), and whole tumor (WT). Our framework used a model ensemble strategy based on two state-of-the-art deep learning models: a convolutional neural network (nnU-Net) and a vision transformer (Swin UNETR) and was tested for broader applicability across multiple tumor types. The framework was trained on 99, 1,000, and 165 cases and validated on 45, 141, and 31 unseen cases for PED, MEN, and MET, respectively. Automatic segmentations were evaluated by lesion-wise volume overlap (Dice similarity score, DSC) and Hausdorff distance (HD). RESULTS In the evaluation on independent unseen test datasets, our automatic tool was ranked first for PED, third for MEN, and fourth for MET volumetric analysis. Our method resulted in PED lesion-wise DSC of 0.733, 0.782, 0.817 and HD (mm) of 75.93, 25.54, 24.18 for ET, TC, and WT, respectively. CONCLUSIONS These brain tumor volumetric analysis tools are readily available to be efficiently tested on diverse datasets. Automatic MRI analysis provides consistent quantitative data for multi-institutional protocols and clinical trials.

P.094 Incidence of pathologically confirmed primary malignant brain tumours in Newfoundland and Labrador: an eight-year review spanning 2015-2022

Background: Considering regional and temporal trends, we sought to explore the incidence of primary malignant brain tumours in Newfoundland and Labrador. Methods: We reviewed all primary, malignant brain tumour cases from 2015-2022 confirmed by St. John’s Health Sciences Centre pathology reports. Incidence rates were standardized using the 2011 Canadian standard population. Results: We included 362 cases. The average annual age-standardized incidence rate of primary, malignant brain tumours per 100,000 was 7.0 (95% CI: 6.3-7.7), lower than the national average (7.93; 95% CI: 7.78-8.08). The incidence of glioblastoma (5.1; 95% CI: 4.5-5.7) was significantly higher than the national average (4.05; 95% CI: 3.95-4.16). Temporal trends revealed that oligodendroglioma incidence spiked from 0.5 (95% CI: 0.2-0.7) in 2015-2019 to 1.5 (95% CI: 0.4-2.6) in 2020 before returning to baseline in 2022. Regional trends indicated a lower incidence of malignant tumours in Labrador-Grenfell (5.1; 95% CI: 2.5-7.6), compared to 6.9 (95% CI: 6.2-7.6) averaged elsewhere. Conclusions: Higher rates of glioblastoma in Newfoundland and Labrador could have a genetic or multi-factorial cause. The increased occurrence of oligodendroglioma during the COVID-19 pandemic necessitates broader investigation, potentially linked to delays in patient care during this period. Regional trends could suggest less access to care in rural populations and underestimated incidence.

Artificial intelligence model for tumoral clinical decision support systems

Background and ObjectiveComparative diagnostic in brain tumor evaluation makes possible to use the available information of a medical center to compare similar cases when a new patient is evaluated. By leveraging Artificial Intelligence models, the proposed system is able of retrieving the most similar cases of brain tumors for a given query. The primary objective is to enhance the diagnostic process by generating more accurate representations of medical images, with a particular focus on patient-specific normal features and pathologies. A key distinction from previous models lies in its ability to produce enriched image descriptors solely from binary information, eliminating the need for costly and difficult to obtain tumor segmentation. MethodsThe proposed model uses Artificial Intelligence to detect patient features to recommend the most similar cases from a database. The system not only suggests similar cases but also balances the representation of healthy and abnormal features in its design. This not only encourages the generalization of its use but also aids clinicians in their decision-making processes. This generalization makes possible for future research in different medical diagnosis areas with almost not any change in the system. ResultsWe conducted a comparative analysis of our approach in relation to similar studies. The proposed architecture obtains a Dice coefficient of 0.474 in both tumoral and healthy regions of the patients, which outperforms previous literature. Our proposed model excels at extracting and combining anatomical and pathological features from brain Magnetic Resonances (MRs), achieving state-of-the-art results while relying on less expensive label information. This substantially reduces the overall cost of the training process. Our findings highlight the significant potential for improving the efficiency and accuracy of comparative diagnostics and the treatment of tumoral pathologies. ConclusionsThis paper provides substantial grounds for further exploration of the broader applicability and optimization of the proposed architecture to enhance clinical decision-making. The novel approach presented in this work marks a significant advancement in the field of medical diagnosis, particularly in the context of Artificial Intelligence-assisted image retrieval, and promises to reduce costs and improve the quality of patient care using Artificial Intelligence as a support tool instead of a black box system.

A review of natural products and small-molecule therapeutics acting on central nervous system malignancies: Approaches for drug development, targeting pathways, clinical trials, and challenges.

In 2021, the World Health Organization released the fifth edition of the central nervous system (CNS) tumor classification. This classification uses histopathology and molecular pathogenesis to group tumors into more biologically and molecularly defined entities. The prognosis of brain cancer, particularly malignant tumors, has remained poor worldwide, approximately 308,102 new cases of brain and other CNS tumors were diagnosed in the year 2020, with an estimated 251,329 deaths. The cost and time-consuming nature of studies to find new anticancer agents makes it necessary to have well-designed studies. In the present study, the pathways that can be targeted for drug development are discussed in detail. Some of the important cellular origins, signaling, and pathways involved in the efficacy of bioactive molecules againstCNS tumorigenesis or progression, as well as prognosis and common approaches for treatment of different types of brain tumors, are reviewed. Moreover, different study tools, including cell lines, in vitro, in vivo, and clinical trial challenges, are discussed. In addition, in this article, natural products as one of the most important sources for finding new chemotherapeutics were reviewed and over 700 reported molecules with efficacy against CNS cancer cells are gathered and classified according to their structure. Based on the clinical trials that have been registered, very few of these natural or semi-synthetic derivatives have been studied in humans. The review can help researchers understand the involved mechanisms and design new goal-oriented studies for drug development against CNS malignancies.

Intraoperative seizures during neuro-oncological supratentorial surgery: the role of prophylaxis with levetiracetam and intraoperative monitoring in a consecutive series of 353 patients.

The aim of this paper was to understand the role of prophylaxis with levetiracetam at skin incision in preventing convulsive intraoperative seizures (IOS) during neurosurgical procedures with and without intraoperative neuromonitoring (IONM). Authors retrospectively reviewed the Institutional database for cases of supratentorial brain tumors undergoing surgical resection performed from January 2021 to October 2022. Patients were operated on both under general anesthesia and awake, using motor-evoked potentials (MEP) and direct cortical stimulation for cortical mapping. 1000 mg ev of Levetiracetam before skin incision in case of a history of seizures was administrated. We excluded all infratentorial cases. Three hundred fisty three consecutive cases were retrieved. IOS occurred in 22 patients (6.2%). Prophylaxis with Levetiracetam was administered in 149 patients, and IOS occurred in 16 cases (10.7%) in this group of patients. The IOS rate in the case of no Levetiracetam prophylaxis administration (3.5%) was significantly lower (P<0.001, OR=3.38 [1.35-8.45], RR=3.12 [1.32-7.41]). The Penfield technique stimulation evoked seven of all 22 IOS reported (31.8%) (P=0.006, RR 5.4 [1.44 -20.58], OR 21 [2.3-183.9]), and the train-of-five technique stimulation caused two of all registered IOS (8.7%) (P=0.2, RR 2.3 [0.99-5.67], OR 6.5 [0.55-76.17]). Transcranial MEPs evoked no IOS. Under levetiracetam prophylaxis, the IOS rate was not significantly lower than in the group of patients without Levetiracetam prophylaxis, regardless of the histology of the tumor and IONM. Neither the transcranial stimulation (MEP) nor train-of-five technique stimulation increases the risk of convulsive IOS, as Penfield technique stimulation does.

PDGFRA K385 mutants in myxoid glioneuronal tumors promote receptor dimerization and oncogenic signaling

Myxoid glioneuronal tumors (MGNT) are low-grade glioneuronal neoplasms composed of oligodendrocyte-like cells in a mucin-rich stroma. These tumors feature a unique dinucleotide change at codon 385 in the platelet-derived growth factor receptor α (encoded by the PDGFRA gene), resulting in the substitution of lysine 385 into leucine or isoleucine. The functional consequences of these mutations remain largely unexplored. Here, we demonstrated their oncogenic potential in fibroblast and Ba/F3 transformation assays. We showed that the K385I and K385L mutants activate STAT and AKT signaling in the absence of ligand. Co-immunoprecipitations and BRET experiments suggested that the mutations stabilized the active dimeric conformation of the receptor, pointing to a new mechanism of oncogenic PDGF receptor activation. Furthermore, we evaluated the sensitivity of these mutants to three FDA-approved tyrosine kinase inhibitors: imatinib, dasatinib, and avapritinib, which effectively suppressed the constitutive activity of the mutant receptors. Finally, K385 substitution into another hydrophobic amino acid also activated the receptor. Interestingly, K385M was reported in a few cases of brain tumors but not in MGNT. Our results provide valuable insights into the molecular mechanism underlying the activation of PDGFRα by the K385I/L mutations, highlighting their potential as actionable targets in the treatment of myxoid glioneuronal tumors.