STUDY DESIGN: Systematic review and modified Delphi technique. OBJECTIVE: To use an evidence-based medicine process using the best available literature and expert opinion consensus to develop a comprehensive classification system to diagnose neoplastic spinal instability. SUMMARY OF BACKGROUND DATA: Spinal instability is poorly defined in the literature and presently there is a lack of guidelines available to aid in defining the degree of spinal instability in the setting of neoplastic spinal disease. The concept of spinal instability remains important in the clinical decision-making process for patients with spine tumors. METHODS: We have integrated the evidence provided by systematic reviews through a modified Delphi technique to generate a consensus of best evidence and expert opinion to develop a classification system to define neoplastic spinal instability. RESULTS: A comprehensive classification system based on patient symptoms and radiographic criteria of the spine was developed to aid in predicting spine stability of neoplastic lesions. The classification system includes global spinal location of the tumor, type and presence of pain, bone lesion quality, spinal alignment, extent of vertebral body collapse, and posterolateral spinal element involvement. Qualitative scores were assigned based on relative importance of particular factors gleaned from the literature and refined by expert consensus. CONCLUSION: The Spine Instability Neoplastic Score is a comprehensive classification system with content validity that can guide clinicians in identifying when patients with neoplastic disease of the spine may benefit from surgical consultation. It can also aid surgeons in assessing the key components of spinal instability due to neoplasia and may become a prognostic tool for surgical decision-making when put in context with other key elements such as neurologic symptoms, extent of disease, prognosis, patient health factors, oncologic subtype, and radiosensitivity of the tumor.
STUDY DESIGN: Systematic review and modified Delphi technique. OBJECTIVE: To use an evidence-based medicine process using the best available literature and expert opinion consensus to develop a comprehensive classification system to diagnose neoplastic spinal instability. SUMMARY OF BACKGROUND DATA: Spinal instability is poorly defined in the literature and presently there is a lack of guidelines available to aid in defining the degree of spinal instability in the setting of neoplastic spinal disease. The concept of spinal instability remains important in the clinical decision-making process for patients with spine tumors. METHODS: We have integrated the evidence provided by systematic reviews through a modified Delphi technique to generate a consensus of best evidence and expert opinion to develop a classification system to define neoplastic spinal instability. RESULTS: A comprehensive classification system based on patient symptoms and radiographic criteria of the spine was developed to aid in predicting spine stability of neoplastic lesions. The classification system includes global spinal location of the tumor, type and presence of pain, bone lesion quality, spinal alignment, extent of vertebral body collapse, and posterolateral spinal element involvement. Qualitative scores were assigned based on relative importance of particular factors gleaned from the literature and refined by expert consensus. CONCLUSION: The Spine Instability Neoplastic Score is a comprehensive classification system with content validity that can guide clinicians in identifying when patients with neoplastic disease of the spine may benefit from surgical consultation. It can also aid surgeons in assessing the key components of spinal instability due to neoplasia and may become a prognostic tool for surgical decision-making when put in context with other key elements such as neurologic symptoms, extent of disease, prognosis, patient health factors, oncologic subtype, and radiosensitivity of the tumor.
Authors: Patricia L Zadnik; Lee Hwang; Derek G Ju; Mari L Groves; Jackson Sui; Alp Yurter; Timothy F Witham; Ali Bydon; Jean-Paul Wolinsky; Ziya L Gokaslan; Daniel M Sciubba Journal: Clin Exp Metastasis Date: 2013-09-03 Impact factor: 5.150
Authors: Ori Barzilai; Lily McLaughlin; Eric Lis; Anne S Reiner; Mark H Bilsky; Ilya Laufer Journal: Oper Neurosurg (Hagerstown) Date: 2019-05-01 Impact factor: 2.703
Authors: Ori Barzilai; Anne L Versteeg; Arjun Sahgal; Laurence D Rhines; Mark H Bilsky; Daniel M Sciubba; James M Schuster; Michael H Weber; Peter Pal Varga; Stefano Boriani; Chetan Bettegowda; Michael G Fehlings; Yoshiya Yamada; Michelle J Clarke; Paul M Arnold; Ziya L Gokaslan; Charles G Fisher; Ilya Laufer Journal: Cancer Date: 2018-11-29 Impact factor: 6.860