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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.

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