Critical Assessment of Knowledge-Based Models for Craniospinal Irradiation of Paediatric Patients

Background and purpose Knowledge-Based Planning (KBP) is increasingly used to standardize and optimize radiotherapy planning. This study aims to develop, refine, and compare multicentric KBP models for craniospinal irradiation (CSI) in pediatric patients. Materials and methods A total of 113 CSI treatments from three Italian centers were collected, comprising Computed Tomography scans, target and organ contours, and treatment plans. Treatment techniques included Helical Tomotherapy (HT) and Volumetric Modulated Arc Therapy (VMAT). Three KBP models were developed: a full model (F-model) using data from 87 patients, a reduced model (R-model) based on a subset of the same sample, and a replanned model (RP-model) using KBP re-optimized plans. Models’ quality was evaluated using goodness-of-fit and goodness-of-prediction metrics, and their performance was assessed on a validation set of 26 patients through dose-volume histogram (DVH) comparisons, prediction bias, and variance analysis. Results The F-model and R-model exhibited similar quality and predictive ability, reflecting the variability of the original dataset and resulting in broad prediction intervals in low to mid-dose ranges. The RP-model achieved the highest quality, with narrower prediction bands. The RP-model is preferable for standardizing planning across centers, while the F-model is better suited for quality assurance as it captures clinical variability. Conclusions KBP models can effectively predict DVHs despite extreme geometric variability. However, models trained on highly variable datasets cannot simultaneously achieve high precision and accuracy. Comparing KBP models is essential for establishing benchmarks to meet specific clinical goals, particularly for complex pediatric CSI treatments.