Artificial intelligence applications in the screening and classification of glioblastoma

Glioblastoma is the most aggressive primary brain tumor, with poor prognosis following initial identification. Current diagnostic methods, including neuroimaging and molecular pathology, face several limitations in tumor delineation, differentiation of progression from treatment effects, and classification of tumor grade. Artificial intelligence (AI) and machine learning (ML) have been increasingly investigated for its potential in addressing such challenges. This narrative review examines existing AI applications in glioblastoma screening and classification, as well as their associated methodological shortcomings. A comprehensive literature search was conducted in MEDLINE for studies published in the past five years applying ML methods to glioblastoma screening and classification. Studies which were not peer reviewed, did not discuss screening or classification, or lacked a clearly defined ML methodology were excluded. Study designs, training dataset type, and model efficacies were reviewed for narrative evidence synthesis. AI-based omics models frequently applied genomic, transcriptomic, methylation status, and Raman spectroscopy data to glioblastoma classification. Non-omics AI applications frequently involved imaging-based methods, in addition to histopathologic and clinical studies. Accuracies exceeding 90% were observed in several studies for the identification and classification of glioblastoma. Despite this, challenges remain in clinical implementation due to dataset heterogeneity, inconsistent model validation, and lack of standardized reporting methodology. While large language models are an emerging area of interest, few studies investigated their uses in the screening or classification of GBM. AI offers the potential for significant advancements in GBM screening and classification, but widespread clinical adoption requires improved application of existing reporting guidelines. Future research should focus on model interpretability, further development of high-quality datasets, and implementation.