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

• Main Authors: Jamie Dean, Kee Wong, Hiram Gay, Liam Welsh, Ann-Britt Jones, Ulricke Schick, Jung Hun Oh, Aditya Apte, Kate Newbold, Shreerang Bhide, Kevin Harrington, Joseph Deasy, Christopher Nutting, Sarah Gulliford
• Format: Article
• Language: English
• Published: Elsevier 2018-01-01
• Series: Clinical and Translational Radiation Oncology
• Online Access: http://www.sciencedirect.com/science/article/pii/S2405630817300460

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 compared using area under the receiver operating characteristic curve (AUC) to assess performance. Associations between treatment features and dysphagia were explored using RFC models. The PLR model using dose-volume metrics (PLRstandard) performed as well as the more complex models and had very good discrimination (AUC = 0.82) on external validation. The features with the highest RFC importance values were the volume, length and circumference of PM receiving 1 Gy/fraction and higher. The volumes of PM receiving 1 Gy/fraction or higher should be minimized to reduce the incidence of severe acute dysphagia.