Survival of Patients With Multiple Intracranial Metastases Treated With Stereotactic Radiosurgery

Abstract

Defining prognostic factors is a crucial initial step for determining the management of patients with brain metastases. Randomized trials assessing radiosurgery have commonly limited inclusion criteria to 1 to 4 brain metastases, in part due to multiple retrospective studies reporting on the number of brain metastases as a prognostic indicator. The present study reports on the survival of patients with 1 to 4 versus ≥5 brain metastases treated with radiosurgery. We evaluated a retrospective multi-institutional database of 1523 brain metastases in 507 patients who were treated with radiosurgery (Gamma Knife or Cyberknife) between 2001 and 2014. A total of 243 patients were included in the analysis. Patients with 1 to 4 brain metastases were compared with patients with ≥5 brain metastases using a standard statistical analysis. Cox hazard regression was used to construct a multivariable model of overall survival (OS). To find covariates that best separate the data at each split, a machine learning technique Chi-squared Automated Interaction Detection tree was utilized. On Pearson correlation, systemic disease status, number of intracranial metastases, and overall burden of disease (number of major involved organ systems) were found to be highly correlated (P<0.001). Patients with 1 to 4 metastases had a median OS of 10.8 months (95% confidence interval, 6.1-15.6 mo), compared with a median OS of 8.5 months (95% confidence interval, 4.4-12.6 mo) for patients with ≥5 metastases (P=0.143). The actuarial 6 month local failure rate was 5% for patients with 1 to 4 metastases versus 3.2% for patients with ≥5 metastases (P=0.404). There was a significant difference in systemic disease status between the 2 groups; 30% of patients had controlled systemic disease in the <5 lesions group, versus 8% controlled systemic disease in the ≥5 lesions group (P=0.005). Patients with 1 to 4 metastases did not have significantly improved OS in a multivariable model adjusting for systemic disease status, systemic extracranial metastases, and other key variables. The Chi-squared Automated Interaction Detection tree (machine learning technique) algorithm consistently identified performance status and systemic disease status as key to disease classification, but not intracranial metastases. Although the number of brain metastases has previously been accepted as an independent prognostic indicator, our multicenter analysis demonstrates that the number of intracranial metastases is highly correlated with overall disease burden and clinical status. Proper matching and controlling for these other determinants of survival demonstrates that the number of intracranial metastases alone is not an independent predictive factor, but rather a surrogate for other clinical factors.