Aims: A normal tissue complication probability (NTCP) model of severe acute mucositis would be highly useful to guide clinical decision making and inform radiotherapy planning. We aimed to improve upon our previous model by using a novel oral mucosal surface organ at risk (OAR) in place of an oral cavity OAR. Materials and methods: Predictive models of severe acute mucositis were generated using radiotherapy dose to the oral cavity OAR or mucosal surface OAR and clinical data. Penalised logistic regression and random forest classification (RFC) models were generated for both OARs and compared. Internal validation was carried out with 100-iteration stratified shuffle split cross-validation, using multiple metrics to assess different aspects of model performance. Associations between treatment covariates and severe mucositis were explored using RFC feature importance. Results: Penalised logistic regression and RFC models using the oral cavity OAR performed at least as well as the models using mucosal surface OAR. Associations between dose metrics and severe mucositis were similar between the mucosal surface and oral cavity models. The volumes of oral cavity or mucosal surface receiving intermediate and high doses were most strongly associated with severe mucositis. Conclusions: The simpler oral cavity OAR should be preferred over the mucosal surface OAR for NTCP modelling of severe mucositis. We recommend minimising the volume of mucosa receiving intermediate and high doses, where possible. (C) 2016 The Royal College of Radiologists. Published by Elsevier Ltd.