Which of the laboratory, genetic, or imaging biomarkers is better for a clinical endpoint in a brain tumor? Bioinformatics welcomes you to the smart world

Gliomas arise from glial cells and account for about ۳۸.۷% of nervous system cancers and ۸۰% of malignant brain tumors as well as worldwide killing ۲۲۵,۰۰۰ people annually. The diagnosis of gliomas is based on the tumor's location and type, which allows medical professionals to determine treatment options, including surgery, radiation therapy, and chemotherapy, and to estimate the patient's chances of survival. MRI images (T۱-weighted, T۲-weighted, and T۲-weighted Fluid Attenuated Inversion Recovery (FLAIR)) are among the advanced imaging techniques most commonly used in the modern diagnosis of gliomas, which can be analyzed, segmented, and classified using machine learning techniques, particularly Deep Learning (DL), a hybrid of artificial intelligence and machine learning. A variety of advanced MRI techniques, including diffusion-weighted imaging (DWI), high angular resolution diffusion imaging (HARDI), and arterial spin labeling (ASL) perfusion imaging, have been used for presurgical planning. Neurobioinformatics, a fast-developing field, is dedicated to combining neuronal data with the development of modern computing tools to improve our understanding of how the nervous system functions. MicroRNAs (miRs) are a class of small non-coding RNAs that are responsible for many vital biological processes. The miRs have been found to function as therapeutic targets and agents in addition to being diagnostic and prognostic markers. A prompt and accurate diagnosis to establish the endpoint in gliomas treatment is, in our opinion, a promising first step toward understanding the molecular mechanisms of tumor growth. In this paper, we summarize the current state of the art in the field of brain tumor neuroimaging, microRNAs in the pathogenesis, and biomarker discovery.