Comprehensive Summary
Glioblastoma (GBM) is an aggressive brain tumor with a low survival rate, inherently caused by invasive tumor cells that usually proliferate in the peritumoral edema (peri-ED) region of the brain. Due to its recurring nature, GBM has posed as a high-risk issue that makes identification and treatment a challenge. In this study, Tu et al developed a deep-learning algorithm, Glioblastoma Infiltrating Area Interactive Detection Framework (GIAIDF), to recognize microscopic glioblastoma instances at earlier stages to improve prognosis. GIAIDF incorporated diffusion tensor imaging (DTI) biomarkers with magnetic resonance imaging (MRI) scans. After being trained with 136 patients with confirmed GBM, GIAIDF proved to distinguish between tumor and non-tumor scans with roughly 0.915 precision in the internal validation set and 0.890 precision in the external validation set. The recall ability in both sets was roughly between 0.778 and 0.800. The pathology results support that GIAIDF was further able to distinguish between infiltrated and non-infiltrated regions with 0.880 accuracy. It is further discussed that GIAIDF demonstrates great potential in identifying and recalling the manifestation of GBM in high-contrast scans. Tu et al recognize the significance of using GIAIDF to improve prognosis, treatment scheduling, and early detection in GBM cases.
Outcomes and Implications
Glioblastoma’s ability to hide in plain sight due to the microscopic, migrating nature of its tumor cells exacerbates issues in detecting GBM in earlier stages. As the conventional MRI cannot provide clarity on the minuscule, infiltrating cells, it is difficult for the tumor cells to be eradicated from the brain thoroughly. By developing the GIAIDF framework, a new approach to identifying GBM at earlier stages, Tu et al provide an opportunity to enhance surgical planning and radiation targeting. GIAIDF can potentially assist in recognizing areas of the brain that require further treatment, enabling greater focus on certain areas where GBM may recur. The high accuracy of this approach provides an avenue to integrate GIAIDF into patient care, revolutionizing survival outcomes in glioblastoma.