Current Status of Imaging Diagnosis of Brain Tumors and the Utilization of Artificial Intelligence in Their Diagnosis and Detection

Abstract

Brain tumor imaging is crucial for lesion detection, qualitative and pathological diagnosis, identifying lesion extent, treatment planning, and prognosis estimation. The importance of molecular genetic information, highlighted in WHO classifications of 2016 and 2021, necessitates advanced MRI techniques, such as functional and quantitative MRI, to improve diagnostic accuracy. Recent advancements in artificial intelligence (AI) have significantly impacted medical imaging, enhancing image quality and aiding in brain tumor detection and diagnosis. Gliomas, including astrocytomas and oligodendrogliomas, are classified based on pathological and genetic characteristics. Conventional MRI sequences, such as T1- and T2-weighted images, FLAIR, and post-contrast T1 images, are essential. Functional MRI sequences provide additional insights into tumor pathology. The T2-FLAIR mismatch sign is specific to IDH-mutant astrocytomas, aiding differential diagnosis. AI models like DeepMedic, using multi-resolution feature extraction and 3D convolutional layers, achieve high-accuracy segmentation and diagnosis. For metastatic brain tumors, post-contrast T1-weighted images using Black-blood MRI techniques enhance detection sensitivity. AI models trained on these sequences outperform radiologists in detecting micro-metastases. Integrating AI with advanced imaging techniques promises improved diagnostic accuracy and efficiency in brain tumor imaging.