Utility of deformable image registration for adaptive prostate cancer treatment. Analysis and comparison of two commercially available algorithms

INTRODUCTION: This study aimed to assess and compare the capabilities of two commercially available deformable image registration algorithms implemented in Raystation 9A (A(1)) and Velocity AI (A(2)) for possible usage in adaptive prostate radiotherapy based on the propagation of anatomical contours from computed tomography (CT) images to cone-beam CT (CBCT) images. MATERIAL AND METHODS: Ten patients were retrospectively selected from a group treated for localized prostate cancer. The propagation of rectum contours was analyzed in a set of CT-CBCT pairs. Two independent observers carried out qualitative analysis using the two-level descriptive scale (meet/fail). Quantitative analysis was done using landmark points distances based on implanted markers as navigation points and differently obtained contours (manually and automatically using DIR algorithms). Quantitative analysis was taken on sets preselected by qualitative analysis. RESULTS: Qualitative analysis shows that 83.7% of the rectum contours were scored identically (meet or fail) for both algorithms, from which 53.5% and 55.8% are failed results for A(1) and A(2), respectively. For the rectum size (RWD parameter), differences between referenced and deformation-based values were 5.5 and 5.8 mm, and for the rectum wall, the prostate marker distance (WMD parameter) was 4.5 and 5.5 mm for A(1) and A(2), respectively. The differences between the WMD parameters were statistically significant (p = 0.007). CONCLUSIONS: In both tested algorithms, neither effectiveness nor measured uncertainties in the propagation of rectum contour process in prostate patient cases were satisfactory. Careful selection of input images followed by case/set-based verification of every deformable registration is a substantial step to avoid inappropriate results.