Development and validation of a knowledge-based model for robotic radiosurgery planning for brain lesions

One of the main challenges in stereotactic planning is achieving a steep dose gradient to spare nearby structures. This study aimed to develop and validate a knowledge-based (KB) model for automated planning of single brain lesions using a robotic stereotactic system, achieving plan quality comparable or superior to manual plans.

Sixty retrospective plans were used to train the model. A relationship was established between the Planning Target Volume (PTV) radius and the effective radii of multiple isodose volumes. These regressions were used to generate patient-specific dose shell structures and automated optimization templates. Model performance was assessed through internal (15 cases) and external (13 cases) validation. Conformity Index (CI), Dose Gradient Index (DGI), healthy brain dose, and delivery parameters were compared between KB-generated and clinical plans.

The predictive accuracy of the model was ≥0.98 for all isodose levels. For both validation cohorts, KB plans achieved a significantly steeper dose fall-off. A median DGI of 87.3 vs 81.5 (p = 0.001) and of 88.5 vs 79.2 (p < 0.0001) was found, respectively. A significantly (p = 0.01) lower CI was found for external validation and a similar CI (p = 0.08) for the internal cohort. Automatic plans reduced irradiated healthy brain volume, particularly at 50% and 30% isodose levels. Significantly fewer beams were obtained: -58 (p = 0.004) and - 34 (p = 0.03), respectively for both cohorts.

The proposed KB model enables automated stereotactic planning with high quality, efficiency, and standardization.