Acute Spine Tumor Pain: Development of a Description and Classification System Through an International Delphi Consensus Study
Ori Barzilai, Marie-Laure Vial, Raphaële Charest-Morin, Hanbo Chen, Dean Chou, Michelle J Clarke, Nicolas Dea, John E O'Toole, Lisa V Doan, S Mohammed Karim, Daniel Lubelski, Sheng-fu Larry Lo, Cordula Netzer, Lisa Ruppert, Joost Rutges, Jorrit-Jan Verlaan, Michael H Weber, Riccardo Cecchinato, Amitabh Gulati, Michael G Fehlings, Aron Lazary, Antonio Martín-Benlloch, Jacques D Müller-Broich, Shalin S Patel, Laurence D Rhines, John H Shin, Jonathan T Yang, Scott L Zuckerman, Charles G Fisher, Ilya LauferAbstract
Background
Pain is a dominant and often debilitating symptom in patients with spinal tumors, yet its assessment and reporting remain inconsistent across disciplines. The absence of a standardized, mechanism-based classification limits clinical communication, treatment selection, and the interpretation of outcomes in spine oncology research
Methods
We conducted a modified Delphi study through the AO Spine Knowledge Forum Tumor, engaging an international panel of 44 multidisciplinary experts, including neurosurgeons, orthopedic surgeons, radiation oncologists, and pain specialists. Across four iterative rounds, participants evaluated candidate domains and definitions for spine tumor pain using structured surveys, consensus thresholds, and a final consensus meeting. A mechanism-based classification framework was developed based on agreement across predefined criteria.
Results
Consensus identified three core domains—pain characteristics, clinical examination, and imaging findings—as essential for characterizing spine tumor–related pain. These elements informed a simplified, mechanism-based classification comprising six mutually exclusive categories: mechanical-axial, mechanical-radicular, inflammatory-axial, inflammatory-radicular, neuropathic-central, and neuropathic-peripheral. The framework integrates symptom quality, anatomical distribution, and radiographic correlates to infer underlying pain mechanisms. Nociplastic and chronic pain states were incorporated as modifiers. The resulting system was considered clinically intuitive, reproducible, and applicable across specialties.
Conclusion
This international consensus establishes the first multidisciplinary, mechanism-based classification of acute spine tumor pain. The framework provides a standardized approach to pain assessment in neuro-oncology, with potential to improve clinical decision-making, enhance cross-disciplinary communication, and enable consistent pain endpoint reporting in clinical trials. Prospective validation is warranted to confirm its reliability and utility in diverse clinical settings.