Correlation of Target Volume, Surface Area, and Position with Gamma Knife SRS Plan Quality Metrics
Abstract
Purpose
To retrospectively evaluate the correlation between Gamma Knife SRS plan quality metrics and target geometric characteristics using correlation analyses.
Methods
Thirty-five clinically approved single-target Gamma Knife Icon plans were retrospectively analyzed. All plans were generated using Leksell GammaPlan® (Version 11.4.2) with Lightning inverse optimizer for Leksell Gamma Knife® Icon™. Plan quality metrics included Paddick Conformity Index (PCI), Gradient Index at 50% and 10% prescribed dose (GI50% and GI10%), and Efficiency Index at 50% and 10% prescribed dose (η50% and η10%). Geometric parameters included target volume, surface area, and normalized position (0–1, relative to midline). Pearson correlation analysis assessed relationships between individual geometric parameters and metrics. Spearman rank correlation analysis was performed for combined geometric parameters. Statistical significance was established at p < 0.05.
Results
Mean target volume was 11.56 cm³ (range: 0.45–72.12 cm³); mean surface area was 33.12 cm² (range: 3.44–111.51 cm²); mean normalized position was 0.395 (range: 0.12–0.92). PCI & high-dose metrics demonstrated no statistically significant correlation with geometric parameters. Conversely, low-dose metrics exhibited moderate to strong correlations: GI10% negatively correlated with volume (r = −0.551, p < 0.01), surface area (r = −0.637, p < 0.01), and position (r = −0.634, p < 0.01); η10% showed positive correlations with volume (r = 0.614, p < 0.01), surface area (r = 0.554, p < 0.01), and position (r = 0.491, p < 0.01). Spearman analysis confirmed similar patterns for combined parameters, with GI10% and η10% demonstrating the strongest correlations when position was included with surface area, volume, respectively (r = −0.713 and r = 0.634; p < 0.01).
Conclusion
The Leksell GammaPlan® Lightning optimizer generates treatment plans with consistent high-dose quality metrics across varying target geometric characteristics. However, low-dose volume optimization demonstrates geometry-dependent behavior, with larger peripheral targets achieving superior dose gradients and efficiency.