Using multi-centre data to train and validate a knowledge-based model for planning radiotherapy of the head and neck

Abstract

Background and purpose: Knowledge-based radiotherapy planning models have been shown to reduce healthy tissue dose and optimisation times, with larger training databases delivering greater robustness. We propose a method of combining knowledge-based models from multiple centres to create a ‘super-model’ using their collective patient libraries, thereby increasing the breadth of training knowledge. Materials and methods: A head and neck super-model containing 207 patient datasets was created by merging the data libraries of three centres. Validation was performed on 30 independent datasets during which optimiser parameters were tuned to deliver the optimal set of model template objectives. The super-model was tested on a further 40 unseen patients from four radiotherapy centres, including one centre external to the training process. The generated plans were assessed using established plan evaluation criteria. Results: The super-model generated plans that surpassed the dose objectives for all patients with single optimisations in an average time of 10 min. Healthy tissue sparing was significantly improved over manual planning, with dose reductions to parotid of 4.7 ± 2.1 Gy, spinal cord of 3.3 ± 0.9 Gy and brainstem of 2.9 ± 1.7 Gy. Target coverage met the established constraints but was marginally reduced compared with clinical plans. Conclusions: Three centres successfully merged patient libraries to create a super-model capable of generating plans that met plan evaluation criteria for head and neck patients with improvements in healthy tissue sparing. The findings indicate that the super-model could improve head and neck planning quality, efficiency and consistency across radiotherapy centres.

Keywords

• Radiotherapy
• RapidPlan
• Head and neck
• Knowledge-based planning
• Super-Model