Abstract

BackgroundStereotactic body radiation therapy (SBRT) is increasingly used to treat spine metastases. Current post-SBRT imaging surveillance strategies in this patient population may benefit from a more data-drive and personalized approach. The objective of this study was to develop risk stratified post-SBRT magnetic resonance imaging (MRI) surveillance strategies using quantitative methods. MethodsAdult patients with bony spine metastases treated with SBRT between 2008 and 2021 and who had at least two follow-up spine MRIs were retrospectively reviewed. A recursive partitioning analysis (RPA) model was developed to separate patients into different risk categories for post-SBRT progression anywhere within the spine. Imaging intervals were derived for each risk category using parametric survival regression based on multiple expected spine progression rates per scan. ResultsA total of 446 patients and 1,039 vertebral segments were included. Cumulative incidence of spine progression was 19.2% at 1 year, 26.7% at 2 years, and 35.3% at 4 years. The internally validated risk stratification model was able to divide patients into three risk categories based on epidural disease, paraspinal disease, and Spinal Instability Neoplastic Score category. The 4-year risk of spine progression was 23.4%, 39.0%, and 51.8%, respectively, for the low, intermediate, and high-risk groups. Using an expected per-scan spine progression rate of 3.75%, the low-risk group would require follow-up scans every 6.0 (95% confidence interval: 4.9-7.6) months and the intermediate-risk group would require surveillance every 3.1 (2.6-3.7) months. At an expected spine progression rate of 5%, the high-risk group would require surveillance every 1.3 (1.1-1.6) months during the first 13.2 months after SBRT, and every 5.9 (2.8-12.3) months thereafter. ConclusionData-driven follow-up MR surveillance intervals at a range of expected spine progression rates have been determined for patients at different risks of spine progression based on an internally validated, single-institution risk stratification model.

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