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

Area of Science:

• Medical Physics
• Radiation Oncology
• Computational Imaging

Background:

• Online adaptive radiotherapy (ART) addresses inter-fraction geometric variations in patient setup.
• Clinical implementation requires understanding ART's benefits and optimal usage frequencies.
• Efficient methods are needed for large-scale retrospective studies of ART.

Purpose of the Study:

• To establish and examine an efficient procedure for large-scale retrospective clinical studies of online ART.
• To utilize a GPU-based re-planning platform for this purpose.

Main Methods:

• Developed a GPU-based re-planning software (SCORE) for online ART analysis.
• Employed deformable registration (CT to CBCT) and CBCT artifact correction.
• Handled limited CBCT field-of-view by using deformed CT for re-planning and dose calculation.
• Calculated optimized and delivered fractional doses and compared them using deformation vector fields.

Main Results:

• The procedure was tested on prostate cancer intensity-modulated radiotherapy (IMRT) cases.
• Re-optimized and delivered dose-volume histograms (DVHs) and dose distributions were generated rapidly (within minutes).

Conclusions:

• A novel procedure was developed for efficient, large-scale retrospective studies of online ART.
• The GPU-based software facilitates rapid analysis of clinical gains from online ART.