RADIOLOGIC EVALUATION AND DIAGNOSTIC METHODS IN METASTATIC SPINE TUMORS

Metastatic spinal tumors represent the most common malignant lesions of the spine, accounting for approximately 90% of all spinal neoplasms. They may involve the vertebral body, epidural space, intradural extramedullary compartment, or, rarely, the spinal cord parenchyma. Breast, lung, prostate, and kidney cancers are among the most frequent primary sources. Classification based on anatomi-cal location facilitates diagnosis, treatment planning, and prognostic assessment. Vertebral metasta-ses constitute the majority (~94%), followed by intradural extramedullary (~5%) and intramedullary (~1%) lesions. Radiologic imaging plays a pivotal role in diagnosis, staging, treatment planning, and follow-up. Conventional radiography is limited in sensitivity but can demonstrate advanced osseous destruction and deformities. Computed tomography (CT) provides detailed evaluation of cortical and trabecular bone, facilitates surgical and interventional planning, and enables image-guided biopsy. Magnetic resonance imaging (MRI), with its superior soft tissue resolution, is the gold standard for detecting bone marrow involvement, epidural extension, and neural compression, with high sensitivity and specificity. Nuclear medicine techniques, including bone scintigraphy, PET-CT, and SPECT-CT, are valuable for systemic staging and treatment response evaluation. Hybrid imaging modalities, such as PET-MRI, offer combined functional and anatomic assessment. Spinal metastases spread via hema-togenous, lymphatic, or direct invasion routes. Radiologic features vary according to tumor biology; osteolytic lesions show cortical destruction and high T2 signal, whereas osteoblastic lesions display dense sclerosis and low signal on both T1and T2-weighted sequences. Differentiation from mim-icking conditions such as infection, trauma, or degenerative disease requires careful correlation with clinical and laboratory findings. Emerging technologies, including artificial intelligence (AI), machine learning (ML), and advanced MRI techniques (DWI, DCE-MRI, quantitative MRI), enhance diagnos-tic accuracy, treatment monitoring, and personalized therapy planning. Integrated decision-support systems combining imaging with clinical, genetic, and laboratory data hold promise for improved patient-specific management. In conclusion, optimal diagnosis and management of metastatic spinal tumors require a multimodality imaging approach, integration of radiologic findings with clinical con-text, and multidisciplinary collaboration. Continuous advances in imaging technologies are expected to further improve early detection, prognostic stratification, and therapeutic outcomes.