Deep learning-based segmentation, grading and genotype classification of glioma: a literature analysis.

Background and Objective: Glioma represents the most prevalent and lethal primary brain tumor in adults, and its high mortality poses a significant challenge to clinical decision-making. While the conventional gold standard for diagnosis relies on the examination of surgically resected tumor tissue, recent advances in artificial intelligence, particularly deep learning, have demonstrated compelling performance in medical image analysis, offering the potential for non-invasive prediction of the genetic and histological features of glioma as well as automatic tumor segmentation. This review aims to comprehensively summarize deep learning-based techniques for glioma segmentation, grading, and genotype classification. Methods: A systematic literature search was conducted across PubMed, Institute of Electrical and Electronics Engineers (IEEE) Xplore, and Scopus for articles published in English between January 2014 and December 2025, using combinations of the terms "glioma", "brain tumor", "deep learning", "convolutional neural network", "segmentation", "classification", "grading", "genotype", "isocitrate dehydrogenase (IDH)", "1p/19q", and "O6-methylguanine-DNA methyltransferase (MGMT)". Peer-reviewed studies that applied deep learning methods to glioma analysis using medical imaging data and reported quantitative performance results were included, whereas review articles without original results, studies using exclusively non-deep-learning methods, and studies focused solely on non-glioma brain tumors were excluded. Key Content and Findings: This review first summarizes the commonly used public datasets [Brain Tumor Segmentation (BraTS), The Cancer Imaging Archive (TCIA), The Cancer Genome Atlas (TCGA), synthetic, and private cohorts] and performance metrics, and then surveys deep learning-based approaches for glioma segmentation, grading, and genotype classification according to task. Recent methodological advances, including Vision Transformers, foundation models such as the segment anything model (SAM) and MedSAM, diffusion models, and self-supervised learning, are also discussed. Reported performance varies considerably across datasets and evaluation protocols, and risks of bias, including slice-level data leakage, patient overlap across public repositories, and single-center confounding, remain prevalent in the genotype prediction literature. Conclusions: Deep learning has become a transformative tool for non-invasive glioma characterization. Future research should prioritize prospective multi-center validation, standardized evaluation protocols, calibrated uncertainty estimation, and clinically deployable frameworks, thereby facilitating the translation of these techniques into routine diagnostic workflows and ultimately improving outcomes for patients with glioma.