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Correlation of Normal Lung Density Changes with Dose After Stereotactic Body Radiotherapy (SBRT) for Early Stage Lung Cancer
Q Wu*, S Devpura , K Feghali , C Liu , M Ajlouni , B Movsas , I Chetty , Henry Ford Health System, Detroit, MI
Presentations
WE-AB-207B-5 (Wednesday, August 3, 2016) 7:30 AM - 9:30 AM Room: 207B
Purpose: To investigate correlation of normal lung CT density changes with dose accuracy and outcome after SBRT for patients with early stage lung cancer.
Methods: Dose distributions for patients originally planned and treated using a 1-D pencil beam-based (PB-1D) dose algorithm were retrospectively recomputed using algorithms: 3-D pencil beam (PB-3D), and model-based methods: AAA, Acuros XB (AXB), and Monte Carlo (MC). Prescription dose was 12 Gy x 4 fractions. Planning CT images were rigidly registered to the followup CT datasets at 6-9 months after treatment. Corresponding dose distributions were mapped from the planning to followup CT images. Following the method of Palma et al .(1-2), Hounsfield Unit (HU) changes in lung density in individual, 5 Gy, dose bins from 5-45 Gy were assessed in the peri-tumor region, defined as a uniform, 3 cm expansion around the ITV(1).
Results: There is a 10-15% displacement of the high dose region (40-45 Gy) with the model-based algorithms, relative to the PB method, due to the electron scattering of dose away from the tumor into normal lung tissue (Fig.1). Consequently, the high-dose lung region falls within the 40-45 Gy dose range, causing an increase in HU change in this region, as predicted by model-based algorithms (Fig.2). The patient with the highest HU change (~110) had mild radiation pneumonitis, and the patient with HU change of ~80-90 had shortness of breath. No evidence of pneumonitis was observed for the 3 patients with smaller CT density changes (<50 HU). Changes in CT densities, and dose-response correlation, as computed with model-based algorithms, are in excellent agreement with the findings of Palma et al.(1-2).
Conclusion: Dose computed with PB (1D or 3D) algorithms was poorly correlated with clinically relevant CT density changes, as opposed to model-based algorithms. A larger cohort of patients is needed to confirm these results.
Funding Support, Disclosures, and Conflict of Interest: This work was supported in part by a grant from Varian Medical Systems, Palo Alto, CA
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