Advances in Imaging for Orbital Tumors

Abstract

In recent years, significant strides have been made in the field of ocular neoplasm imaging. This dynamic area of research has witnessed the convergence of various advanced imaging modalities and computational techniques, revolutionizing the diagnosis, characterization, and management of ocular malignancies. The intricate anatomy and delicate structures of the eye and orbit pose unique challenges in imaging, necessitating the development of high-resolution, non-invasive imaging techniques capable of delineating subtle changes in tissue architecture and function. Novel imaging technologies such as multi-sequence magnetic resonance imaging (MRI) have provided unprecedented insights into tumour morphology, vascularity, and cellular composition. Moreover, the advent of artificial intelligence (AI) and machine learning algorithms has augmented the diagnostic capabilities of imaging modalities, enabling rapid and accurate interpretation of complex imaging datasets. This review aims to critically examine the latest advances in orbital neoplasm imaging, encompassing the latest developments in imaging instrumentation, image processing algorithms, and clinical applications, with a focus on improving early detection, treatment planning, and prognostication for patients with orbital tumours.