Analyzing the Diagnosis and Treatment of Astrocytoma, Oligodendroglioma, and Glioblastoma: A Systematic Review
- Title
- Analyzing the Diagnosis and Treatment of Astrocytoma, Oligodendroglioma, and Glioblastoma: A Systematic Review
- Creator
- Singh, Yuvika; Shanbhog, Manjula; Singh, Kuljeet
- Description
- Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has had a substantial impact on a variety of fields, including healthcare, neuro-oncology, and precision medicine. In recent years, the availability of large-scale labeled datasets has allowed AI-driven advances in glioma detection, classification, and prognosis prediction. However, issues remain in assuring model generalizability, interpretability, and real-world clinical application. One of the most significant disadvantages is the underrepresentation of rare glioma subtypes, which prevents appropriate classification and therapy optimization. This study thoroughly assesses AI-based approaches for glioma classification, survival prediction, and biomarker discovery. A comprehensive survey of ML and DL models published between 2015 and 2024 has been conducted, evaluating radiomics-based tumor detection, multi-omics data integration, and AI-assisted decision-making frameworks. The review investigates the usefulness of convolutional neural networks CNNs, support vector machines SVMs, ensemble learning, and hybrid AI architectures, focusing on classification accuracy, sensitivity, and clinical applicability. Despite these advances, AI-driven glioma research faces challenges such as dataset consistency, clinical validation gaps, and a scarcity of explainable AI (XAI) frameworks. This paper offers a comparative analysis of artificial intelligence approaches assessing their strengths, constraints, and clinical relevance in glioma diagnosis and prognosis prediction in order to solve these challenges. The results underscore artificial intelligences revolutionary capacity in redefining glioma diagnosis, enhancing accuracy, and shaping the future of personalized treatment, thereby integrating computational progress with clinical neuro-oncology. Glioma diagnosis, deep learning, astrocytoma, oligodendroglioma, glioblastoma. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
- Source
- Lecture Notes in Networks and Systems;Volume;1580 LNNS;pp.467-481
- Date
- 01-01-2026
- Publisher
- Springer Science and Business Media Deutschland GmbH
- Subject
- Astrocytoma; Brain tumors; Deep learning; Glioblastoma; Glioma diagnosis; Oligodendroglioma
- Coverage
- Singh Y., Department of Computer Science, School of Sciences, Christ University, Delhi, India; Shanbhog M., Department of Computer Science, School of Sciences, Christ University, Delhi, India; Singh K., Department of Computer Science, School of Sciences, Christ University, Delhi, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISSN: 23673370; ISBN: 978-981950628-6;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Singh, Yuvika; Shanbhog, Manjula; Singh, Kuljeet, “Analyzing the Diagnosis and Treatment of Astrocytoma, Oligodendroglioma, and Glioblastoma: A Systematic Review,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25422.