Primary glioblastoma of the spinal cord is a rare and aggressive tumor, comprising less than 1.5% of spinal neoplasms. It typically affects young adult males and arises in the cervical or thoracic regions. We report an unusual case of intradural extramedullary spinal glioblastoma in a 62-year-old man with prior lymphoma in remission. The patient presented with a 7-month history of progressive lower limb weakness, numbness, and radiating pain. MRI revealed a contrast-enhancing mass at the T6–7 level, initially suspected as lymphoma. Surgical resection via total laminectomy was performed, and en-bloc tumor removal achieved. Histopathological analysis confirmed WHO grade IV glioblastoma, IDH-wildtype, without Histone H3 mutation. This case highlights an atypical radiologic and anatomical presentation, complicating preoperative diagnosis. Histopathologic and molecular studies were essential for confirmation. Postoperative treatment included adjuvant radiotherapy and temozolomide, though their efficacy remains uncertain in spinal glioblastoma due to limited evidence and spinal cord radiosensitivity. Early biopsy and a multimodal diagnostic approach are critical for managing rare spinal tumors presenting with nonspecific clinical and imaging features.

Advanced imaging technologies have revolutionized the diagnosis and management of spinal pathologies by providing superior precision and efficiency. Modalities such as PET-CT, SPECT, diffusion tensor imaging (DTI), and magnetic resonance spectroscopy (MRS) offer unique insights into the metabolic, structural, and functional aspects of spinal diseases, enabling better differentiation of lesions, improved surgical planning, and early detection of pathological changes. Furthermore, the integration of artificial intelligence (AI) has enhanced imaging workflows by enabling automated analysis, prediction of clinical outcomes, and segmentation of spinal structures. Despite these advancements, challenges such as technical limitations, high costs, and ethical concerns, including issues of data privacy and AI-generated inaccuracies, hinder widespread adoption. This review explores the clinical applications, limitations, and future directions of these emerging technologies, highlighting the need for multidisciplinary collaboration and large-scale research to standardize protocols and optimize patient outcomes. The seamless integration of advanced imaging and AI represents a transformative potential for improving diagnostic accuracy and treatment efficacy in spinal care.