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Updated: Jan 27, 2026

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Published on: February 28, 2021
Clinicopathological and Prognostic Characteristics in Extra-Axial Chordomas: An Integrative Analysis of 86 Cases and
Guo-Hua Lv1, Ming-Xiang Zou1, Fu-Sheng Liu1
1Department of Spine Surgery, The Second Xiangya Hospital, Central South University, Changsha, China.
Background:
Currently, clinical characteristics and prognostic factors of extra-axial chordoma (EAC) remain poorly understood.
Objective:
To characterize clinicopathological characteristics in a large EAC cohort and investigate their correlation with survival. We also attempted to compare these outcomes with axial chordoma (AC).
Methods:
Medline and Embase searches (from inception to February 28, 2018) were conducted to identify eligible studies as per predefined criteria. The local database at our center was also retrospectively reviewed to include additional patients.
Results:
Forty-three studies from the literature and 86 patients from our local institute were identified, resulting in a total of 86 EAC patients and 75 AC patients for analysis. Overall, EAC had similar characteristics to AC, except for having higher CAM5.2 expression, common lobular growth pattern, and better prognosis. Whereas wide surgical resection was consistently associated with favorable survival in both EAC and AC cohorts on univariate analyses, most parameters showed differential prognostic implications between the 2 groups. Significant prognostic factors for local recurrence-free survival on multivariate analysis included type of surgery in both cohorts and tumor Brachyury expression and adjuvant radiotherapy in AC cohort. Multivariate analysis of overall survival demonstrated that type of surgery, tumor Brachyury expression, and duration of symptoms were significant predictors in the AC cohort, whereas none of the analyzed parameters were predictive of overall survival for the EAC group.
Conclusion:
These data suggest potentially distinct biological behaviors between EAC and AC and may provide useful information to better understand the prognostic characteristics and improve the outcome prediction of EAC patients.
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