Tumor Treating Fields : Additional Mechanisms and Additional Applications

Novel therapeutic strategies in glioma targeting glutamatergic neurotransmission

High grade gliomas carry a poor prognosis despite aggressive surgical and adjuvant approaches including chemoradiotherapy. Recent studies have demonstrated a mitogenic association between neuronal electrical activity and glioma growth involving the PI3K-mTOR pathway. As the predominant excitatory neurotransmitter of the brain, glutamate signalling in particular has been shown to promote glioma invasion and growth. The concept of the neurogliomal synapse has been established whereby glutamatergic receptors on glioma cells have been shown to promote tumour propagation. Targeting glutamatergic signalling is therefore a potential treatment option in glioma. Antiepileptic medications decrease excess neuronal electrical activity and some may possess anti-glutamate effects. Although antiepileptic medications continue to be investigated for an anti-glioma effect, good quality randomised trial evidence is lacking. Other pharmacological strategies that downregulate glutamatergic signalling include riluzole, memantine and anaesthetic agents. Neuromodulatory interventions possessing potential anti-glutamate activity include deep brain stimulation and vagus nerve stimulation - this contributes to the anti-seizure efficacy of the latter and the possible neuroprotective effect of the former. A possible role of neuromodulation as a novel anti-glioma modality has previously been proposed and that hypothesis is extended to include these modalities. Similarly, the significant survival benefit in glioblastoma attributable to alternating electrical fields (Tumour Treating Fields) may be a result of disruption to neurogliomal signalling. Further studies exploring excitatory neurotransmission and glutamatergic signalling and their role in glioma origin, growth and propagation are therefore warranted.

Medical Device Advances in the Treatment of Glioblastoma

Despite decades of research and the growing emergence of new treatment modalities, Glioblastoma (GBM) frustratingly remains an incurable brain cancer with largely stagnant 5-year survival outcomes of around 5%. Historically, a significant challenge has been the effective delivery of anti-cancer treatment. This review aims to summarize key innovations in the field of medical devices, developed either to improve the delivery of existing treatments, for example that of chemo-radiotherapy, or provide novel treatments using devices, such as sonodynamic therapy, thermotherapy and electric field therapy. It will highlight current as well as emerging device technologies, non-invasive versus invasive approaches, and by doing so provide a detailed summary of evidence from clinical studies and trials undertaken to date. Potential limitations and current challenges are discussed whilst also highlighting the exciting potential of this developing field. It is hoped that this review will serve as a useful primer for clinicians, scientists, and engineers in the field, united by a shared goal to translate medical device innovations to help improve treatment outcomes for patients with this devastating disease.

Study and analysis of different segmentation methods for brain tumor MRI application

Medical Resonance Imaging (MRI) is one of the preferred imaging methods for brain tumor diagnosis and getting detailed information on tumor type, location, size, identification, and detection. Segmentation divides an image into multiple segments and describes the separation of the suspicious region from pre-processed MRI images to make the simpler image that is more meaningful and easier to examine. There are many segmentation methods, embedded with detection devices, and the response of each method is different. The study article focuses on comparing the performance of several image segmentation algorithms for brain tumor diagnosis, such as Otsu's, watershed, level set, K-means, HAAR Discrete Wavelet Transform (DWT), and Convolutional Neural Network (CNN). All of the techniques are simulated in MATLAB using online images from the Brain Tumor Image Segmentation Benchmark (BRATS) dataset-2018. The performance of these methods is analyzed based on response time and measures such as recall, precision, F-measures, and accuracy. The measured accuracy of Otsu's, watershed, level set, K-means, DWT, and CNN methods is 71.42%, 78.26%, 80.45%, 84.34%, 86.95%, and 91.39 respectively. The response time of CNN is 2.519 s in the MATLAB simulation environment for the designed algorithm. The novelty of the work is that CNN has been proven the best algorithm in comparison to all other methods for brain tumor image segmentation. The simulated and estimated parameters provide the direction to researchers to choose the specific algorithm for embedded hardware solutions and develop the optimal machine-learning models, as the industries are looking for the optimal solutions of CNN and deep learning-based hardware models for the brain tumor.