Automated calculation of point A coordinates for CT-based high-dose-rate brachytherapy of cervical cancer

Purpose: The goal is to develop a stand-alone application, which automatically and consistently computes the coordinates of the dose calculation point recommended by the American Brachytherapy Society (i.e., point A) based solely on the implanted applicator geometry for cervical cancer brachytherapy.

Material and methods: The application calculates point A coordinates from the source dwell geometries in the computed tomography (CT) scans, and outputs the 3D coordinates in the left and right directions. The algorithm was tested on 34 CT scans of 7 patients treated with high-dose-rate (HDR) brachytherapy using tandem and ovoid applicators. A single experienced user retrospectively and manually inserted point A into each CT scan, whose coordinates were used as the “gold standard” for all comparisons. The gold standard was subtracted from the automatically calculated points, a second manual placement by the same experienced user, and the clinically used point coordinates inserted by multiple planners. Coordinate differences and corresponding variances were compared using nonparametric tests.

Results: Automatically calculated, manually placed, and clinically used points agree with the gold standard to < 1 mm, 1 mm, 2 mm, respectively. When compared to the gold standard, the average and standard deviation of the 3D coordinate differences were 0.35 ± 0.14 mm from automatically calculated points, 0.38 ± 0.21 mm from the second manual placement, and 0.71 ± 0.44 mm from the clinically used point coordinates. Both the mean and standard deviations of the 3D coordinate differences were statistically significantly different from the gold standard, when point A was placed by multiple users (p < 0.05) but not when placed repeatedly by a single user or when calculated automatically. There were no statistical differences in doses, which agree to within 1-2% on average for all three groups.

Conclusions: The study demonstrates that the automated algorithm calculates point A coordinates consistently, while reducing inter-user variability. Point placement using the algorithm expedites the planning process and minimizes associated potential human errors.