Spinal Schwannoma Classification Based on the Presumed Origin With Preoperative Magnetic Resonance Images
- Author:
Tae-Shin KIM
1
;
Jae Hee KUH
;
Junhoe KIM
;
Woon Tak YUH
;
Junghoon HAN
;
Chang-Hyun LEE
;
Chi Heon KIM
;
Chun Kee CHUNG
Author Information
- Publication Type:Original Article
- From: Neurospine 2024;21(3):890-902
- CountryRepublic of Korea
- Language:English
-
Abstract:
Objective:Classification guides the surgical approach and predicts prognosis. However, existing classifications of spinal schwannomas often result in a high ‘unclassified’ rate. Here, we aim to develop a new comprehensive classification for spinal schwannomas based on their presumed origin. We compared the new classification with the existing classifications regarding the rate of ‘unclassified’. Finally, we assessed the surgical strategies, outcomes, and complications according to each type of the new classification.
Methods:A new classification with 9 types was created by analyzing the anatomy of spinal nerves and the origin of significant tumor portions and cystic components in preoperative magnetic resonance images. A total of 482 patients with spinal schwannomas were analyzed to compare our new classification with the existing classifications. We defined ‘unclassified’ as the inability to classify a patient with spinal schwannoma using the classification criteria. Surgical approaches and outcomes were also aligned with our new classification.
Results:Our classification uniquely reported no ‘unclassified’ cases, indicating full applicability. Also, the classification has demonstrated usefulness in predicting the surgical outcome with the approach planned. Gross total removal rates reached 88.0% overall, with type 1 and type 2 tumors at 95.3% and 96.0% respectively. The approach varied with tumor type, with laminectomy predominantly used for types 1, 2, and 9, and facetectomy with posterior fixation used for type 3 tumors.
Conclusion:The new classification for spinal schwannomas based on presumed origin is applicable to all spinal schwannomas. It could help plan a surgical approach and predict its outcome, compared with existing classifications.
