Background: Stereotactic body radiation therapy (SBRT) is increasingly used to treat early-stage non-small cell lung cancer (NSCLC). A previous report introduced the term size-adjusted biologically effective dose (sBED), which accounts for tumor diameter and biologically effective dose (BED) and may be used to predict the likelihood of local control following SBRT. Here we seek to replicate those findings using a separate dataset.
Methods and materials: We queried the RSSearch Patient Registry for patients treated with SBRT for stage I NSCLC. Kaplan-Meier survival curves, log-rank testing, and Cox proportional hazards modeling were used to evaluate tumor diameter, BED, and treatment planning algorithm as predictors of local control. sBED was defined as BED minus 10 times the tumor diameter (in centimeters). Tumor control probability (TCP) modeling was performed to characterize the relationship between sBED and the likelihood of local control 2 years after SBRT.
Results: A total of 928 patients met inclusion criteria. Median BED was 115.5 Gy, and 59% of patients had T1 tumors. Local control rates following treatments planned using a pencil beam algorithm were inferior to those observed following treatments planned using a Monte Carlo algorithm (89% vs 96% at 2 years, log-rank P = .022). In a multivariable Cox model adjusted for tumor diameter and BED, the use of a pencil beam planning algorithm was associated with increased risk of local failure (hazard ratio, 2.39; 95% confidence interval, 1.08-5.29; P = .032). TCP modeling, restricted to patients treated using a Monte Carlo algorithm, demonstrated that sBED values of 60, 80, and 100 Gy yield predicted TCP rates of 91%, 95%, and 97%, respectively.
Conclusions: Using a large, multi-institutional database, we found a strong association between treatment planning algorithm and local control rates following SBRT for early-stage NSCLC. sBED is a useful tool for predicting the likelihood of local control following SBRT in this setting.
Copyright © 2017 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.