Normal tissue complication probability (NTCP) modelling using spatial dose metrics and machine learning methods for severe acute oral mucositis resulting from head and neck radiotherapy.
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Embargo End Date
ICR Authors
Authors
Dean, JA
Wong, KH
Welsh, LC
Jones, A-B
Schick, U
Newbold, KL
Bhide, SA
Harrington, KJ
Nutting, CM
Gulliford, SL
Wong, KH
Welsh, LC
Jones, A-B
Schick, U
Newbold, KL
Bhide, SA
Harrington, KJ
Nutting, CM
Gulliford, SL
Document Type
Journal Article
Date
2016-07-01
Date Accepted
2016-05-12
Abstract
BACKGROUND AND PURPOSE: Severe acute mucositis commonly results from head and neck (chemo)radiotherapy. A predictive model of mucositis could guide clinical decision-making and inform treatment planning. We aimed to generate such a model using spatial dose metrics and machine learning. MATERIALS AND METHODS: Predictive models of severe acute mucositis were generated using radiotherapy dose (dose-volume and spatial dose metrics) and clinical data. Penalised logistic regression, support vector classification and random forest classification (RFC) models were generated and compared. Internal validation was performed (with 100-iteration cross-validation), using multiple metrics, including area under the receiver operating characteristic curve (AUC) and calibration slope, to assess performance. Associations between covariates and severe mucositis were explored using the models. RESULTS: The dose-volume-based models (standard) performed equally to those incorporating spatial information. Discrimination was similar between models, but the RFCstandard had the best calibration. The mean AUC and calibration slope for this model were 0.71 (s.d.=0.09) and 3.9 (s.d.=2.2), respectively. The volumes of oral cavity receiving intermediate and high doses were associated with severe mucositis. CONCLUSIONS: The RFCstandard model performance is modest-to-good, but should be improved, and requires external validation. Reducing the volumes of oral cavity receiving intermediate and high doses may reduce mucositis incidence.
Citation
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology, 2016, 120 (1), pp. 21 - 27
Rights
Source Title
Publisher
ELSEVIER IRELAND LTD
ISSN
0167-8140
eISSN
1879-0887
Research Team
Clinical Academic Radiotherapy (Horwich)
Radiotherapy Physics Modelling
Targeted Therapy
Radiotherapy Physics Modelling
Targeted Therapy