Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy.

ABSTRACT: Severe acute dysphagia commonly results from head and neck radiotherapy (RT). A model enabling prediction of severity of acute dysphagia for individual patients could guide clinical decision-making. Statistical associations between RT dose distributions and dysphagia could inform RT planning protocols aiming to reduce the incidence of severe dysphagia. We aimed to establish such a model and associations incorporating spatial dose metrics. Models of severe acute dysphagia were developed using pharyngeal mucosa (PM) RT dose (dose-volume and spatial dose metrics) and clinical data. Penalized logistic regression (PLR), support vector classification and random forest classification (RFC) models were generated and internally (173 patients) and externally (90 patients) validated. These were compa

SUBMITTER: Dean J 

PROVIDER: S-EPMC5796681 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature