WE-G-BRCD-01: A Procedure for Efficient Large-Scale Retrospective Clinical Studies for Online Adaptive Radiotherapy

Online adaptive radiotherapy (ART) is promising for handling inter-fraction variations of patient's geometry. Before a clinical implementation of this advanced technology, it is necessary to study its potential clinical gains and optimal frequencies to be used for various tumor sites. The goal of this work is to establish and examine a procedure for efficient large-scale retrospective clinical studies for online ART using a GPU-based re-planning platform.

The proposed procedure utilizes an in-house developed GPU-based replanning software called SCORE. SCORE starts by applying deformable registration from CT to CBCT and correcting CBCT artifacts and intensities. However, the CBCT image may not cover the whole treatment region due to the limited field of view. In that case, we use deformed CT for replanning and dose calculation. The final optimized fractional dose is calculated using the optimized fluence maps and a finite size pencil beam model. We also use the deformed CT to calculate the delivered fractional dose using the fluence maps from the original plan. The delivered fractional dose is compared to the optimized fractional dose to estimate the daily gain of replanning. To compare accumulated optimized dose and delivered dose, the delivered and optimized doses are mapped back to the original CT geometry using the deformation vector fields.

We tested this procedure using prostate cancer IMRT cases and found that the re-optimized and delivered DVHs and dose distributions can be generated in a couple of minutes.

We have developed a procedure using a GPU-based replanning software to retrospectively study the clinical gains of online ART in an efficient and large scale manner.