Introduction: Glioblastoma is a highly malignant central nervous system tumor, World Health Organization Ⅳ, glioblastoma is the most common primary malignancy, due to its own specificity and complexity, different patients often benefit from the current conventional treatment regimen because of different molecular subtypes, in the context of precision medicine, the application of deep learning to identify the salient features of tumors on brain imaging, prognostic predictive assessment combined with clinical data to maximize the benefits of each patient from the treatment regimen is a non-invasive and feasible regimen. Methods: We conducted a comprehensive review of the existing literature on the role of deep learning in glioblastomas, covering molecular classification and diagnosis, prognosis assessment. Results: Data based on a variety of magnetic resonance imaging sequences, genetic information, and clinical combinations enable noninvasive predictive tumor diagnosis of glioblastoma and assess overall survival and treatment response accuracy. For images, standardized image acquisition and data extraction techniques can be effectively translated into learning models for clinical practice. However, it must be recognized that interventions in the treatment of glioblastoma using deep learning are still in their infancy, and the robustness of the model is challenged, as the current total number of glioblastoma samples is insufficient for large-scale experimental methods, which is directly related to the difficulty of application of the model. Conclusion: Compared to radiomics and shallow machine learning, deep learning can be a more robust, non-invasive, and effective approach, providing more valuable information as clinicians develop personalized medical protocols for glioblastoma patients.
Keywords: Glioblastoma; clinical; deep learning; prognosis; radiogenomics.