Application of a genetic algorithm to optimizing radiation therapy treatment plans for pancreatic carcinoma

The performance of an automated treatment planning algorithm was tested using cases of patients with pancreatic carcinoma; the system implements optimization tools that suggest high-quality plans for consideration by the planner and physician, making best use of the capabilities of a conventional linear accelerator: isocentric setup, shaped fields, and wedges. Ten consecutive patients presenting with pancreatic cancer were first planned using a conventional 3-field protocol to provide a basis for comparison. Each was then planned using an automated optimization technique using a genetic algorithm and a dose-based score function subject to volume-dose constraints. Two sets of optimized plans were created, 1 using only axial beams and the other permitting non-axial beams. The improvement afforded by the optimization was assessed by comparing the score function results and by computing the combined normal tissue complication probability (NTCP) for a constant isocenter dose. In all 10 cases, optimization improved the dose-based score function. In 9 cases, the non-axial plan scored higher than the axial plan. Optimization driven by the dose-based score function improved or equaled the predicted NTCP in 8 axial and 9 nonaxial plans. This study demonstrates progress toward the goal of developing an automated planning tool that can robustly suggest high-quality plans.