Normal Tissue Complication Probability (NTCP) Modelling of Severe Acute Mucositis using a Novel Oral Mucosal Surface Organ at Risk. Issue 4 (April 2017)

• Record Type: Journal Article
• Authors: Dean, J.A.; Welsh, L.C.; Wong, K.H.; Aleksic, A.; Dunne, E.; Islam, M.R.; Patel, A.; Patel, P.; Petkar, I.; Phillips, I.; Sham, J.; Schick, U.; Newbold, K.L.; Bhide, S.A.; Harrington, K.J.; Nutting, C.M.; Gulliford, S.L.
• Is Part Of: Clinical oncology. Volume 29:Issue 4(2017)
• Page Start: 263
• Page End: 273
• Publication Date: 2017-04
• Subjects: Head and neck radiotherapy -- machine learning -- mucositis -- NTCP modelling -- OAR delineation -- oral mucosa; Oncology -- Periodicals; Tumors -- Periodicals; Cancer -- Treatment -- Periodicals; Radiotherapy -- Periodicals; Neoplasms -- Periodicals; Cancer -- Radiotherapy; Cancer -- Treatment; Oncology; Medical radiology; Radiotherapy; Tumors; Electronic journals; Periodicals; 616.994
• DOI: 10.1016/j.clon.2016.12.001
• Languages: English
• ISSNs: 0936-6555

Abstract

Abstract: 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. Highlights: A novel mucosal surface organ at risk (OAR) was applied to normal tissue complication probability modelling of mucositis. Comparison was made with models using the previously used oral cavity OAR. The predictive performance of models using the different OARs was similar. High and intermediate doses were associated with severe mucositis for both OARs. These results could be used to inform radiotherapy planning.