Distinguishing Intramedullary Spinal Cord Neoplasms from Non-Neoplastic Conditions by Analyzing the Classic Signs on MRI in the Era of AI

Abstract

: Intramedullary lesions can be challenging to diagnose given the wide range of possible pathologies. Each lesion has unique clinical and imaging features, which are best evaluated on magnetic resonance imaging. Radiological imaging is unique with rich, descriptive patterns and classic signs—which are often metaphorical. In this review, we present a collection of classic MRI signs, ranging from neoplastic to non-neoplastic lesions, within the spinal cord. The differential diagnosis (DD) of intramedullary lesions can be narrowed down by careful analysis of the classic signs and pattern of involvement in the spinal cord. Furthermore, the signs are illustrated memorably with emphasis on the pathophysiology, mimics and pitfalls. Artificial intelligence (AI) algorithms, particularly deep learning, have made remarkable progress in image recognition tasks. The classic signs and related illustrations can enhance a pattern recognition approach in diagnostic radiology. Deep learning can potentially be designed to distinguish neoplastic from non-neoplastic processes by pattern recognition of the classic MRI signs.