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Updated: Jul 17, 2026

Stereotactic Radiosurgery for Gynecologic Cancer
Published on: April 17, 2012
Development of a rapid planning technique based on heuristic target shaping for stereotactic radiosurgery
Seungjong Oh1, Tae-Suk Suh, Joo-Young Song
1Department of Biomedical Engineering, The Catholic University of Korea, 505 Banpo-dong, Seocho-gu, Seoul 137-701, Republic of Korea.
Abstract:
Stereotactic radiosurgery (SRS) is a technique to delivering a high dose to a target region and a low dose to a critical organ by using only one or a few irradiations. Traditionally, SRS is performed using a Gamma knife with using 201 cobalt 60 sources or a linear accelerator with equally spaced noncoplanar arcs. Finding a specific condition that includes the target in the prescription dose while sparing the critical organ is tedious, because there are many combinations of positions and collimator sizes for each isocenter. Many methods of identifying suitable planning condition automatically have been proposed. However, there are some limitations using these methods. These include a long calculation time to obtain the final plan, and difficulties finding a unique solution due to different tumor shapes. This study uses three steps to solve these problems. (1) The dose distribution of one isocenter is modeled as a sphere. This makes it possible to reduce the time needed to obtain the result due to the absence of a dose calculation. (2) The target was constructed by piling up cylinders along a virtual axis, which was the longest line in a given target. (3) Spheres were then packed in each cylinder according to the position and diameter of each cylinder in order to cover each target divided by the height of the cylinder. The results of applying three imaginary targets were found to be satisfactory in terms of: target coverage-more than 50%, the reproducibility of the result and the calculation time-several tens of seconds. The PITV ratio was less than 2.0. However, the dose applied to normal tissue around the target must be reduced slightly. Planner or conventional optimization algorithms might easily solve this limitation.

