Potential roles of transformers in brain tumor diagnosis and treatment

Brain tumor (BT) is one of many malignancies that have substantially enhanced global human morbidity and mortality rates. Early detection and characterization of glioma are essential for effective preventive strategies. Currently, the use of Transformers, a deep learning model for BT diagnosis and treatment, is attracting significant attention. The transformer self-attention mechanism automatically learns the associations between input data for efficient processing and analysis. Research indicates that Transformers could play an essential role in the BT segmentation of magnetic resonance imaging (MRI) images, the MRI and histopathology-based grading of brain cancer, BT molecular expression prediction, the classification of primary brain metastasis sites, voxel-level dose and BT radiotherapy outcome prediction, synergistic prediction, and the pathway deconvolution of drug combinations. In this review, the feasibility, accuracy, and applicability of various algorithms are systematically analyzed and their prospects are discussed. Overall, this review aimed to discuss and provide an overview of the increasing applications of Transformers in real-time BT detection and therapy, indicating their broad prospects and potential. In the future, Transformers are expected to be increasingly used for the diagnosis and subsequent treatment of BT because of the continuous development and improvement of Transformer-based deep learning technology. However, more work is required to investigate their properties for anomaly detection, medical image classification, network design development, and application to other medical data.