Changes to pediatric brain tumors in 2021 World Health Organization classification of tumors of the central nervous system

An enhanced pattern detection and segmentation of brain tumors in MRI images using deep learning technique

Neuroscience is a swiftly progressing discipline that aims to unravel the intricate workings of the human brain and mind. Brain tumors, ranging from non-cancerous to malignant forms, pose a significant diagnostic challenge due to the presence of more than 100 distinct types. Effective treatment hinges on the precise detection and segmentation of these tumors early. We introduce a cutting-edge deep-learning approach employing a binary convolutional neural network (BCNN) to address this. This method is employed to segment the 10 most prevalent brain tumor types and is a significant improvement over current models restricted to only segmenting four types. Our methodology begins with acquiring MRI images, followed by a detailed preprocessing stage where images undergo binary conversion using an adaptive thresholding method and morphological operations. This prepares the data for the next step, which is segmentation. The segmentation identifies the tumor type and classifies it according to its grade (Grade I to Grade IV) and differentiates it from healthy brain tissue. We also curated a unique dataset comprising 6,600 brain MRI images specifically for this study. The overall performance achieved by our proposed model is 99.36%. The effectiveness of our model is underscored by its remarkable performance metrics, achieving 99.40% accuracy, 99.32% precision, 99.45% recall, and a 99.28% F-Measure in segmentation tasks.

CVG-Net: novel transfer learning based deep features for diagnosis of brain tumors using MRI scans

Brain tumors present a significant medical challenge, demanding accurate and timely diagnosis for effective treatment planning. These tumors disrupt normal brain functions in various ways, giving rise to a broad spectrum of physical, cognitive, and emotional challenges. The daily increase in mortality rates attributed to brain tumors underscores the urgency of this issue. In recent years, advanced medical imaging techniques, particularly magnetic resonance imaging (MRI), have emerged as indispensable tools for diagnosing brain tumors. Brain MRI scans provide high-resolution, non-invasive visualization of brain structures, facilitating the precise detection of abnormalities such as tumors. This study aims to propose an effective neural network approach for the timely diagnosis of brain tumors. Our experiments utilized a multi-class MRI image dataset comprising 21,672 images related to glioma tumors, meningioma tumors, and pituitary tumors. We introduced a novel neural network-based feature engineering approach, combining 2D convolutional neural network (2DCNN) and VGG16. The resulting 2DCNN-VGG16 network (CVG-Net) extracted spatial features from MRI images using 2DCNN and VGG16 without human intervention. The newly created hybrid feature set is then input into machine learning models to diagnose brain tumors. We have balanced the multi-class MRI image features data using the Synthetic Minority Over-sampling Technique (SMOTE) approach. Extensive research experiments demonstrate that utilizing the proposed CVG-Net, the k-neighbors classifier outperformed state-of-the-art studies with a k-fold accuracy performance score of 0.96. We also applied hyperparameter tuning to enhance performance for multi-class brain tumor diagnosis. Our novel proposed approach has the potential to revolutionize early brain tumor diagnosis, providing medical professionals with a cost-effective and timely diagnostic mechanism.

Positioning Transclival Tumor-Treating Fields for the Treatment of Diffuse Intrinsic Pontine Gliomas

Diffuse intrinsic pontine glioma (DIPG) carries an extremely poor prognosis, with 2-year survival rates of <10% despite the maximal radiation therapy. DIPG cells have previously been shown to be sensitive to low-intensity electric fields in vitro. Accordingly, we sought to determine if the endoscopic endonasal (EE) implantation of an electrode array in the clivus would be feasible for the application of tumor-treating fields (TTF) in DIPG. Anatomic constraints are the main limitation in pediatric EE approaches. In our Boston Children’s Hospital’s DIPG cohort, we measured the average intercarotid distance (1.68 ± 0.36 cm), clival width (1.62 ± 0.19 cm), and clival length from the base of the sella (1.43 ± 0.69 cm). Using a linear regression model, we found that only clival length and sphenoid pneumatization were significantly associated with age (R2 = 0.568, p = 0.005 *; R2 = 0.605, p = 0.0002 *). Critically, neither of these parameters represent limitations to the implantation of a device within the dimensions of those currently available. Our findings confirm that the anatomy present within this age group is amenable to the placement of a 2 × 1 cm electrode array in 94% of patients examined. Our work serves to demonstrate the feasibility of implantable transclival devices for the provision of TTFs as a novel adjunctive therapy for DIPG.