Though metastasis-directed therapy (MDT) has the potential to improve overall survival (OS), appropriate patient selection remains challenging. We aimed to develop a model predictive of OS to refine patient selection for clinical trials and MDT.
We assembled a multi-institutional cohort of patients treated with MDT (stereotactic body radiation therapy, radiosurgery, and whole brain radiation therapy). Candidate variables for recursive partitioning analysis were selected per prior studies: ECOG performance status, time from primary diagnosis, number of additional non-target organ systems involved (NOS), and intracranial metastases.
A database of 1,362 patients was assembled with 424 intracranial, 352 lung, and 607 spinal treatments (n=1,383). Treatments were split into training (TC) (70%, n=968) and internal validation (IVC) (30%, n=415) cohorts. The TC had median ECOG of 0 (interquartile range [IQR]: 0-1), NOS of 1 (IQR: 0-1), and OS of 18 months (IQR: 7-35). The resulting model components and weights were: ECOG = 0, 1, and > 1 (0, 1, and 2); 0, 1, and > 1 NOS (0, 1, and 2); and intracranial target (2), with lower scores indicating more favorable OS. The model demonstrated high concordance in the TC (0.72) and IVC (0.72). The score also demonstrated high concordance for each target site (spine, brain, and lung).
This pre-treatment decision tool represents a unifying model for both intracranial and extracranial disease and identifies patients with the longest survival after MDT who may benefit most from aggressive local therapy. Carefully selected patients may benefit from MDT even in the presence of intracranial disease, and this model may help guide patient selection for MDT.