A preoperative risk classifier that predicts tumor progression in patients with cranial base chondrosarcomas.

Abstract

OBJECTIVE:

The authors conducted a study to identify clinical features of cranial base chondrosarcomas that will predict tumor progression after resection.

METHODS:

The authors performed a retrospective study at a tertiary referral cranial base center. Patients who underwent resection of cranial base chondrosarcomas between January 2004 and December 2018 were followed longitudinally. The main outcome measure was progression-free survival (PFS).

RESULTS:

A total of 41 patients were treated for histopathologically proven "conventional" cranial base chondrosarcomas during the study period, and the median PFS was 123 months for the cohort. Univariate analysis was performed on clinical, anatomical, and radiographic parameters collected for each patient. Features that were statistically significant were fed into a multivariate regression model, which revealed two independent predictors of PFS: patient age and encasement of 3-4 major arteries (> 25% of the vessel wall surrounded by tumor). Using these two variables of age and multiple arterial vessel encasement, the authors generated a risk stratification model using a simple point system to predict PFS in patients with cranial base chondrosarcomas. Based on these two factors known preoperatively, this model could stratify patients into high-risk (10% of patients), intermediate-risk (68% of patients), and low-risk (22% of patients) subgroups corresponding to dramatically distinct median PFS (1.8 years, 10.2 years, and no progression, respectively).

CONCLUSIONS:

In patients with cranial base chondrosarcomas, age and artery encasement are variables known preoperatively that can powerfully predict tumor progression, define operative goals, and aid in selecting postoperative adjuvant therapy.

• Authors: Andrew S Venteicher, Michael M McDowell, Ezequiel Goldschmidt, Eric W Wang, Carl H Snyderman, Paul A Gardner
• Journal: Journal of neurosurgery (J Neurosurg) Pg. 1-9 (Jan 10 2020) ISSN: 1933-0693 [Electronic] United States
• PMID: 31923886 (Publication Type: Journal Article)