Grading Of Brain Tumors Research Articles

Diffusion-weighted MR image analysis based on gamma distribution model for differentiating benign and malignant brain tumors.

Considering the invasiveness of the biopsy method, we attempted to evaluate the ability of the gamma distribution model using magnetic resonance imaging images to stage and grade benign and malignant brain tumors. A total of 42 patients with malignant brain tumors (including glioma, lymphoma, and choroid plexus papilloma) and 24 patients with benign brain tumors (meningioma) underwent diffusion-weighted imaging using five b-values ranging from 0 to 2000 s/mm2 with a 1.5 T scanner. The gamma distribution model is expected to demonstrate the probability of water molecule distribution based on the apparent diffusion coefficient. For all tumors, the apparent diffusion coefficient, shape parameter (κ), and scale parameter (θ) were calculated for each b-value. In the staging step, the fractions (ƒ1, ƒ2, ƒ3) expected to reflect the intracellular, and extracellular diffusion and perfusion were investigated. Diffusion <1 × 10-4 mm2/s (ƒ1), 1 × 10-4 mm2/s < Diffusion > 3 × 10-4 mm2/s (ƒ2), and Diffusion >3 × 10-4 mm2/s (ƒ3); in the grading step, fractions were determined to check heavily restricted diffusion. Diffusion lower than 0.3 × 10-4 mm2/s (ƒ11). Diffusion lower than 0.5 × 10-4 mm2/s (ƒ12). Diffusion lower than 0.8 × 10-4 mm2/s (ƒ13). The findings were analyzed using nonparametric statistics and receiver operating characteristic curve diagnostic performance. Gamma model parameters (κ, ƒ1, ƒ2, ƒ3) showed a satisfactory difference in differentiating meningioma from glioma. For b value = 2000 s/mm2, ƒ1 had a better diagnostic performance than κ and apparent diffusion coefficient (sensitivity, 88%; specificity, 68%; P < .001). The best diagnostic performance was related to ƒ3 in b = 2000 s/mm2 (area under the curve = 0.891, sensitivity = 83%, specificity = 80%, P < .001). In the grading step, ƒ12 (area under the curve = 0.870, sensitivity = 92%, specificity = 72%, P < .001) had the best diagnostic performance in differentiating high-grade from low-grade gliomas with b = 2000 s/mm2. The findings of our study highlight the potential of using a gamma distribution model with diffusion-weighted imaging based on multiple b-values for grading and staging brain tumors. Its potential integration into routine clinical practice could advance neurooncology and improve patient outcomes through more accurate diagnosis and treatment planning.

Nakagami-fuzzy imaging for grading brain tumors by analyzing fractal complexity

Gliomas are the brain tumors in glial cells, which are categorized into four numerical grades, I-II-III-IV, to quantize the aggressiveness and severity of the tumors; while divided into two major groups, high-grade (HG) and low-grade (LG), in general. Among many differences between these groups, one of the most distinct and characteristic features could be seen in the shape of the tumor boundaries by magnetic resonance imaging (MRI). Due to aggressive nature of the HG tumors in proliferation phase, the boundaries of HG tumors become more shape-wise complex compared to the LG tumors, which could be differentiated by analyzing the fractal complexity of the cell membranes. However, the complexity cannot be either manually calculated or estimated by eye inspection without a reference point with one single image or sometimes even with an image set. Therefore, we present an automated glioma grading framework to provide an insight on the grades with a novel contouring and fractal dimension analysis system. The primary component of the proposed system is an automated Nakagami imaging module with a specialized fuzzy c-means algorithm to contour the boundaries of the whole tumors. The contoured images, afterwards, are analyzed by the Minkowski–Bouligand and Hausdorff methods for two panning options to generate the fractal dimensions and to estimate the fractal complexities for classifying the gliomas The results are greatly encouraging that the overall classification accuracy is computed as 88.31 % using the basic support vector machines (SVM) classifier; while as 91.96 % with the arbitrary thresholding appended. The outcomes of this paper with implementable mathematical infrastructure would be very useful and beneficial as an expert system in intelligent and automatic glioma grading, for researchers and medical experts.

Correlation of ADC values of adult brain tumors with the diagnosis and pathological grade: A cross-sectional multicenter study.

Brain tumors are common, requiring physicians to have a precise understanding of them for accurate diagnosis and treatment. Considering that various histological tumor types present different cellularity, we conducted this research to examine the role of apparent diffusion coefficient (ADC) values in the differential diagnosis and pathologic grading of brain tumor types. In this cross-sectional study, we gathered pathology reports of histological samples of adult brain tumors. The tissue sample of brain tumors were examined histologically by a pathologist. The magnetic resonance imaging data of these patients were interpreted by a neuroradiologist. The measured ADC values and ADC ratios were calculated. Standard mean ADC values were expressed as 10- 6 mm2/s. The findings were compared according to the histological diagnosis of each tumor. Sixty-eight patients were included in the study: 34 (50%) were male, and 34 (50%) were female. The average age of the patients was 51.69 + 16.40 years. In the examination of tumor type, 16 (23.5%) were astrocytoma, 9 (13.2%) were oligodendroglioma, 20 (29.4%) were glioblastoma, 4 (5.9%) were medulloblastoma, and 19 (27.9%) were metastatic tumors. the average value of ADC was statistically significantly different according to the pathological type of tumor (p < 0.001). The two-by-two comparison of average ADC among tumor types revealed significant differences, except for oligodendroglioma and glioblastoma (p-value = 0.87) and glioblastoma and medulloblastoma (p-value = 0.347). The average value of ADC and ADC ratio was statistically significantly different according to the pathological grade of the tumor (p < 0.001). In the two-by-two comparison of average ADC between all pathological grades of the tumor showed a significance difference except for Grade I and Grade II (p-value = 0.355). The mean value of ADC and ADC ratio for glioblastoma and metastatic tumors showed no significant difference. The assessment of brain tumor grade through ADC examination will help to estimate prognosis and devising suitable therapeutic strategies.

Brain Tumors

Brain tumors are a result of uncontrolled growth of cells in the brain. This growth can be cancerous or noncancerous and cause damage to brain tissue which leads to loss of certain functions. Brain cancer is the 10th leading cause of death. There are 4 grades of brain tumors classified by the World Health Organization based on specific components of each type of tumor. Several factors seem to affect whether an individual is diagnosed with brain tumors: age, exposure to radiation, ethnicity and race, family history, weak immune system, and gender. There are various symptoms of brain tumors that are general as well as specific to the location of the tumor. Many types of treatments are available for brain tumors today such as surgery, radiation therapy, chemotherapy, and targeted therapy. This article works to provide an overview of brain tumors discussing the types, risk factors, symptoms, diagnosis, treatments, and preventions.

An effective ensemble learning approach for classification of glioma grades based on novel MRI features

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors’ prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

Comparison of Diffusion MRI Findings of High-Graded Primary Brain Tumors and Metastatic Brain Tumors

Aim: Glioblastomas are the highest grade and most mortal primary brain tumors. Cerebral masses that occur with the metastasis of cancers of tissues other than brain are included in the differential diagnosis of glioblastomas. This study aimed to compare the diffusion-weighted imaging signal characteristics of primary and metastatic brain masses and to describe the findings that may be useful in the differential diagnosis. Material and Methods: Patients with pathologically diagnosed glioblastoma and patients with pathologically diagnosed metastases or radiologically diagnosed brain metastases were included in the study. Diffusion-weighted imaging signal properties in magnetic resonance imaging examinations obtained with a 1.5 Tesla scanner were retrospectively analyzed. The signal features and short and long diameters of the lesions were measured and compared in both patient groups. Results: A total of 54 patients, 24 glioblastomas, and 30 brain metastases were included in the study. The most common signal feature of diffusion-weighted imaging in the glioblastoma group was heterogeneous hyper- and hypointense areas observed in 20 (83.3%) patients. The most common signal feature in the metastasis group was the peripheral hyperintense ring and central hypointense signal in 16 (53.3%) patients. There was no significant relation found between the number of lesions and the primary brain tumor and metastases. Conclusion: Although only signal characteristics are used without quantitative assessment in diffusion-weighted imaging, it may be helpful in the differential diagnosis of primary and metastatic brain masses. It is important to remember that the masses in the two groups can have comparable signal properties.

An Illustrated Scoping Review of the Magnetic Resonance Imaging Characteristics of Canine and Feline Brain Tumors.

Magnetic resonance imaging (MRI) is used pervasively in veterinary practice for the antemortem diagnosis of intracranial tumors. Here, we provide an illustrated summary of the published MRI features of primary and secondary intracranial tumors of dogs and cats, following PRISMA scoping review guidelines. The PubMed and Web of Science databases were searched for relevant records, and input from stakeholders was solicited to select data for extraction. Sixty-seven studies of moderate to low-level evidence quality describing the MRI features of pathologically confirmed canine and feline brain tumors met inclusion criteria. Considerable variability in data inclusion and reporting, as well as low case numbers, prohibited comparative data analyses. Available data support a holistic MRI approach incorporating lesion number, location within the brain, shape, intrinsic signal appearances on multiparametric sequences, patterns of contrast enhancement, and associated secondary changes in the brain to prioritize differential imaging diagnoses, and often allows for accurate presumptive diagnosis of common intracranial tumors. Quantitative MRI techniques show promise for improving discrimination of neoplastic from non-neoplastic brain lesions, as well as differentiating brain tumor types and grades, but sample size limitations will likely remain a significant practical obstacle to the design of robustly powered radiomic studies. For many brain tumor variants, particularly in cats, there remains a need for standardized studies that correlate clinicopathologic and neuroimaging data.

Design and Development of Hypertuned Deep learning Frameworks for Detection and Severity Grading of Brain Tumor using Medical Brain MR images.

Brain tumor is a grave illness causing worldwide fatalities. The current detection methods for brain tumors are manual, invasive, and rely on histopathological analysis. Determining the type of brain tumor after its detection relies on biopsy measures and involves human subjectivity. The use of automated CAD techniques for brain tumor detection and classification can overcome these drawbacks. The paper aims to create two deep learning-based CAD frameworks for automatic detection and severity grading of brain tumors - the first model for brain tumor detection in brain MR images and model 2 for the classification of tumors into three types: Glioma, Meningioma, and Pituitary based on severity grading. The novelty of the research work includes the architectural design of deep learning frameworks for detection and classification of brain tumor using brain MR images. The hyperparameter tuning of the proposed models is done to achieve the optimal parameters that result in maximizing the models' performance and minimizing losses. The proposed CNN models outperform the existing state of the art models in terms of accuracy and complexity of the models. The proposed model developed for detection of brain tumors achieved an accuracy of 98.56% and CNN Model developed for severity grading of brain tumor achieved an accuracy of 92.36% on BraTs dataset. The proposed models have an edge over the existing CNN models in terms of less complexity of the structure and appreciable accuracy with low training and test errors. The proposed CNN Models can be employed for clinical diagnostic purposes to aid the medical fraternity in validating their initial screening for brain tumor detection and its multi-classification.

Brain tumor grading diagnosis using transfer learning based on optical coherence tomography.

In neurosurgery, accurately identifying brain tumor tissue is vital for reducing recurrence. Current imaging techniques have limitations, prompting the exploration of alternative methods. This study validated a binary hierarchical classification of brain tissues: normal tissue, primary central nervous system lymphoma (PCNSL), high-grade glioma (HGG), and low-grade glioma (LGG) using transfer learning. Tumor specimens were measured with optical coherence tomography (OCT), and a MobileNetV2 pre-trained model was employed for classification. Surgeons could optimize predictions based on experience. The model showed robust classification and promising clinical value. A dynamic t-SNE visualized its performance, offering a new approach to neurosurgical decision-making regarding brain tumors.

Diffusion Weighted Imaging and Grading of Brain Tumours

Background: Most prevalent primary cerebral tumours are meningiomas. The other frequent intracranial tumours are pituitary adenomas, which are benign, and gliomas, which are intra-axial brain tumours. The objective of this study is to understand the importance of DW MRI imaging with standard b-value in differentiating presurgical grading of the brain tumour. Design: A total of 24 DWI patients, including 12 meningiomas, 8 gliomas, and 4 pituitary adenomas, were included in this retrospective analysis. Method: The Stejskal-Tanner equation is used to analyse the ADCmean, ADCmin, and ADCmax values from the healthy and tumour core that are obtained out from area of interest (ROI). Result: The ADCmean value of Gliomas ranges from 0.09 x 10-3 mm2s-1 to 0.99 x 10-3 mm2s-1 with a median value of 0.25 x 10-3 mm2s-1. ADCmean value 1.82 x 10-3 mm2/s (sensitivity: 67%. Specificity: 81.8%) and 0.94 x 10-3 mm2/s (sensitivity: 75%. specificity: 81.3%) can discriminate grade II –IV meningioma from grade II-IV glioma. Conclusion: The ADC and its threshold levels offer crucial details on the grades, consistency, and characterization of tumour, aiding accurate diagnosis and therapy. KEYWORDS: Brain tumour, Imaging, MRI, DWI, ADC, b-value.

Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features.

Gliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain tumor grades, we implemented a protocol for learning, discovering, and quantifying tumor microenvironment elements on our glioma dataset. Using only single-stained biopsies we derived characteristic differentiating tumor microenvironment phenotypic neighborhoods. The study was complicated by the small size of the available human leukocyte antigen stained on glioma tissue microarray dataset - 206 images of 5 classes - as well as imbalanced data distribution. This challenge was addressed by image augmentation for underrepresented classes. In practice, we considered two scenarios, a whole slide supervised learning classification, and an unsupervised cell-to-cell analysis looking for patterns of the microenvironment. In the supervised learning investigation, we evaluated 6 distinct model architectures. Experiments revealed that a DenseNet121 architecture surpasses the baseline's accuracy by a significant margin of 9% for the test set, achieving a score of 69%, increasing accuracy in discerning challenging WHO grade 2 and 3 cases. All experiments have been carried out in a cross-validation manner. The tumor microenvironment analysis suggested an important role for myeloid cells and their accumulation in the context of characterizing glioma grades. Those promising approaches can be used as an additional diagnostic tool to improve assessment during intraoperative examination or subtyping tissues for treatment selection, potentially easing the workflow of pathologists and oncologists.

A multi-class brain tumor grading system based on histopathological images using a hybrid YOLO and RESNET networks

Gliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tumors. In this paper, we developed a technique for visualizing a predictive tumor grading model on histopathology pictures to help guide doctors by emphasizing characteristics and heterogeneity in forecasts. The proposed technique is a hybrid model based on YOLOv5 and ResNet50. The function of YOLOv5 is to localize and classify the tumor in large histopathological whole slide images (WSIs). The suggested technique incorporates ResNet into the feature extraction of the YOLOv5 framework, and the detection results show that our hybrid network is effective for identifying brain tumors from histopathological images. Next, we estimate the glioma grades using the extreme gradient boosting classifier. The high-dimensional characteristics and nonlinear interactions present in histopathology images are well-handled by this classifier. DL techniques have been used in previous computer-aided diagnosis systems for brain tumor diagnosis. However, by combining the YOLOv5 and ResNet50 architectures into a hybrid model specifically designed for accurate tumor localization and predictive grading within histopathological WSIs, our study presents a new approach that advances the field. By utilizing the advantages of both models, this creative integration goes beyond traditional techniques to produce improved tumor localization accuracy and thorough feature extraction. Additionally, our method ensures stable training dynamics and strong model performance by integrating ResNet50 into the YOLOv5 framework, addressing concerns about gradient explosion. The proposed technique is tested using the cancer genome atlas dataset. During the experiments, our model outperforms the other standard ways on the same dataset. Our results indicate that the proposed hybrid model substantially impacts tumor subtype discrimination between low-grade glioma (LGG) II and LGG III. With 97.2% of accuracy, 97.8% of precision, 98.6% of sensitivity, and the Dice similarity coefficient of 97%, the proposed model performs well in classifying four grades. These results outperform current approaches for identifying LGG from high-grade glioma and provide competitive performance in classifying four categories of glioma in the literature.

An open relaxation-diffusion MRI dataset in neurosurgical studies

Diffusion MRI (dMRI) is a safe and noninvasive technique that provides insight into the microarchitecture of brain tissue. Relaxation-diffusion MRI (rdMRI) is an extension of traditional dMRI that captures diffusion imaging data at multiple TEs to detect tissue heterogeneity between relaxation and diffusivity. rdMRI has great potential in neurosurgical research including brain tumor grading and treatment response evaluation. However, the lack of available data has limited the exploration of rdMRI in clinical settings. To address this, we are sharing a high-quality rdMRI dataset from 18 neurosurgical patients with different types of lesions, as well as two healthy individuals as controls. The rdMRI data was acquired using 7 TEs, where at each TE multi-shell dMRI with high spatial and angular resolutions is obtained at each TE. Each rdMRI scan underwent thorough artifact and distortion corrections using a specially designed processing pipeline. The dataset’s quality was assessed using standard practices, including quality control and assurance. This resource is a valuable addition to neurosurgical studies, and all data are openly accessible.

Brain tumor grade classification using the ConvNext architecture.

Brain tumor grade is an important aspect of brain tumor diagnosis and helps to plan for treatment. Traditional methods of diagnosis, including biopsy and manual examination of medical images, are either invasive or may result in inaccurate diagnoses. This study proposes a brain tumor grade classification technique using a modern convolutional neural network (CNN) architecture called ConvNext that inputs magnetic resonance imaging (MRI) data. Deep learning-based techniques are replacing invasive procedures for consistent, accurate, and non-invasive diagnosis of brain tumors. A well-known challenge of using deep learning architectures in medical imaging is data scarcity. Modern-day architectures have huge trainable parameters and require massive datasets to achieve the desired accuracy and avoid overfitting. Therefore, transfer learning is popular among researchers using medical imaging data. Recently, transformer-based architectures have surpassed CNNs for image data. However, recently proposed CNNs have achieved superior accuracy by introducing some tweaks inspired by vision transformers. This study proposed a technique to extract features from the ConvNext architecture and feed these features to a fully connected neural network for final classification. The proposed study achieved state-of-the-art performance on the BraTS 2019 dataset using pre-trained ConvNext. The best accuracy of 99.5% was achieved when three MRI sequences were input as three channels of the pre-trained CNN. The study demonstrated the efficacy of the representations learned by a modern CNN architecture, which has a higher inductive bias for the image data than vision transformers for brain tumor grade classification.

GLIMMERS: glioma molecular markers exploration using long-read sequencing.

The revised WHO guidelines for classifying and grading brain tumors include several copy number variation (CNV) markers. The turnaround time for detecting CNVs and alterations throughout the entire genome is drastically reduced with the customized read incremental approach on the nanopore platform. However, this approach is challenging for non-bioinformaticians due to the need to use multiple software tools, extract CNV markers and interpret results, which creates barriers due to the time and specialized resources that are necessary. To address this problem and help clinicians classify and grade brain tumors, we developed GLIMMERS: glioma molecular markers exploration using long-read sequencing, an open-access tool that automatically analyzes nanopore-based CNV data and generates simplified reports. GLIMMERS is available at https://gitlab.com/silol_public/glimmers under the terms of the MIT license.

Ensemble coupled convolution network for three-class brain tumor grade classification

Ensemble coupled convolution network for three-class brain tumor grade classification

Brain tumor grade classification using multi‐step pre‐training

AbstractMedical images offer a non‐invasive method to diagnose different diseases, but using them manually produces unreliable results. Modern deep learning architectures and techniques are computed and data‐intensive, making them difficult to use for relatively smaller datasets of medical images. Transfer learning has been used as a remedy for the problem mentioned above. However, the domain difference between the datasets used for pre‐training (e.g., ImageNet) and the target datasets, like medical images, negatively impacts the transfer learning results. Recently, many researchers have used additional pre‐training called domain‐adaptive pre‐training (DAPT) using the data from the target domain (e.g., medical images) before using the model on the target tasks to achieve superior performance. This study proposes a variant of DAPT by performing it on a subset of the architecture. It has achieved state‐of‐the‐art performance for brain tumor grading on the BraTS 2019 while being computationally efficient.

The relationship between serum tissue factor and primary brain tumor grades

The relationship between serum tissue factor and primary brain tumor grades

Leat-associated seizures the possible role of EAAT2, pyruvate carboxylase and glutamine synthetase

BackgroundDrug-resistant epilepsy is a common condition in patients with brain neoplasms. The pathogenesis of tumor-associated seizures is poorly understood. Among the possible pathogenetic mechanisms, the increase in glutamate concentration has been proposed. Glutamate transporters, glutamine synthetase and pyruvate carboxylase are involved in maintaining the physiological concentration of glutamate in the intersynaptic spaces. In our previous research on angiocentric gliomas, we demonstrated that all tumors lacked the expression of the main glutamate transporter EAAT2, while the expression of glutamine synthetase and pyruvate carboxylase was mostly preserved. MethodsIn the present study, we evaluated the immunohistochemical expression of EAAT2, glutamine synthetase and pyruvate carboxylase in a heterogeneous series of 25 long-term epilepsy-associated tumors (10 dysembryoplastic neuroepithelial tumors, 7 gangliogliomas, 3 subependymal giant cell astrocytomas, 3 rosette forming glioneuronal tumors, 1 diffuse astrocytoma MYB- or MYBL1-altered and 1 angiocentric glioma). In order to evaluate the incidence of variants in the SLC1A2 gene, encoding EAAT2, in a large number of central nervous system tumors we also queried the PedcBioPortal. ResultsEAAT2 protein expression was lost in 9 tumors (36 %: 3 dysembryoplastic neuroepithelial tumors, 1 ganglioglioma, 3 subependymal giant cell astrocytomas, 1 diffuse astrocytoma MYB- or MYBL1-altered and 1 angiocentric glioma). Glutamine synthetase protein expression was completely lost in 2 tumors (8 %; 1 ganglioglioma and 1 diffuse astrocytoma MYB- or MYBL1-altered). All tumors of our series but rosette forming glioneuronal tumors (in which neurocytic cells were negative) were diffusely positive for pyruvate carboxylase. Consultation of the PedcBioPortal revealed that of 2307 pediatric brain tumors of different histotype and grade, 20 (< 1%) had variants in the SLC1A2 gene. Among the SLC1A2-mutated tumors, there were no angiocentric gliomas or other LEATs ConclusionsIn conclusion, unlike angiocentric gliomas where the EAAT2 loss is typical and constant, the current study shows the loss of EAAT2 expression only in a fraction of the LEATs. In these cases, we may hypothesize some possible epileptogenic role of the EAAT2 loss. The retained expression of pyruvate carboxylase may contribute to determining a pathological glutamate excess unopposed by glutamine synthetase that resulted expressed to a variable extent in the majority of the tumors. Furthermore, we can assume that the EAAT2 loss in brain tumors in general and in LEATs in particular is more conceivably epigenetic.

BIOM-58. ZERO-WASTE MOLECULAR DIAGNOSTICS FROM BIOPSY NEEDLE WASHES

Abstract BACKGROUND Brain tumor classification has increasingly integrated molecular diagnostics into official criteria. However, biopsy tissue is limited due to the small quantity acquired in eloquent areas of the brain and is, therefore, usually earmarked for histology. We have identified that the fluid used to wash biopsy needles contains excess tumor material that can be diagnostically useful. Characterization of this non-zero-sum waste material provides an opportunity for improvement in the diagnosis and treatment for brain tumor patients. METHODS For suspected or established high grade brain tumor biopsies (n = 37 across a multi-institutional cohort at submission), neurosurgeons were instructed to save fluid used to wash biopsy needles. Samples were processed and characterized in a variety of methods to establish diagnostic utility including (1) cell-free DNA isolation and targeted panel sequencing (n = 13), (2) isothermal amplification of cell-free DNA from raw wash water for rapid diagnostics (n = 2), (3) cell-line generation (n = 24) and, (4) cell-free mutant protein capture and quantification (n = 9). RESULTS (1) 13 wash samples were considered for cf-tDNA extraction and Illumina TruSight Oncology 500 panel sequencing (with extracted DNA ranging from 20ng-40ug). In 2 returned cases (11 pending), concordance was 100% with clinical targeted panel sequencing where panels overlapped. (2) One case suspected of harboring H3K27M was subjected to targeted, loop-mediated isothermal amplification and Oxford Nanopore real-time sequencing, confirming an H3.3K27M diagnosis (11.8% allele fraction) within 1hr35min of receipt of sample from the operating room. (3) 5 cases resulted in primary cell culture generation (4) A novel single-molecule mutant protein quantification technique was performed on 9 cases. RESULTS from all analyses and concordance with outstanding sequencing will be presented. CONCLUSION We demonstrate that biopsy byproducts contain enough high-quality tumor-derived material to perform a variety of useful diagnostics with the potential to have immediate translational impact on standard of care.