Normal Tissue Complication Probability (NTCP) Modelling of Severe Acute Mucositis using a Novel Oral Mucosal Surface Organ at Risk.
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Embargo End Date
ICR Authors
Authors
Dean, JA
Welsh, LC
Wong, KH
Aleksic, A
Dunne, E
Islam, MR
Patel, A
Patel, P
Petkar, I
Phillips, I
Sham, J
Schick, U
Newbold, KL
Bhide, SA
Harrington, KJ
Nutting, CM
Gulliford, SL
Welsh, LC
Wong, KH
Aleksic, A
Dunne, E
Islam, MR
Patel, A
Patel, P
Petkar, I
Phillips, I
Sham, J
Schick, U
Newbold, KL
Bhide, SA
Harrington, KJ
Nutting, CM
Gulliford, SL
Document Type
Journal Article
Date
2017-04-01
Date Accepted
2016-11-01
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.
Citation
Clinical oncology (Royal College of Radiologists (Great Britain)), 2017, 29 (4), pp. 263 - 273
Source Title
Publisher
ELSEVIER SCIENCE LONDON
ISSN
0936-6555
eISSN
1433-2981
Research Team
Clinical Academic Radiotherapy (Horwich)
Radiotherapy Physics Modelling
Targeted Therapy
Radiotherapy Physics Modelling
Targeted Therapy