Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy. (January 2018)

• Record Type: Journal Article
• Authors: Dean, Jamie; Wong, Kee; Gay, Hiram; Welsh, Liam; Jones, Ann-Britt; Schick, Ulricke; Oh, Jung Hun; Apte, Aditya; Newbold, Kate; Bhide, Shreerang; Harrington, Kevin; Deasy, Joseph; Nutting, Christopher; Gulliford, Sarah
• Is Part Of: Clinical and translational radiation oncology. Volume 8(2018)
• Page Start: 27
• Page End: 39
• Publication Date: 2018-01
• Subjects: PM pharyngeal mucosa -- PLR penalized logistic regression -- SVC support vector classification -- RFC random forest classification -- AUC area under the receiver operating characteristic curve -- NTCP normal tissue complication probability -- RT radiotherapy -- IMRT intensity modulated radiotherapy -- CTCAE Common Terminology Criteria for Adverse Events -- PEG percutaneous endoscopic gastrostomy -- DVH dose-volume histogram -- DLH dose-length histogram -- DCH dose-circumference histogram; Cancer -- Radiotherapy -- Periodicals; Oncology -- Periodicals; Cancer -- Radiotherapy; Oncology; Radiation Oncology; Neoplasms -- radiotherapy; Translational Medical Research; Periodicals; Electronic journals; Periodicals; 616.9940642
• DOI: 10.1016/j.ctro.2017.11.009
• Languages: English
• ISSNs: 2405-6308

Abstract

Highlights: Machine learning-based NTCP modelling of acute dysphagia was performed. The models generated performed well on internal and external validation. Doses of approximately 1 Gy/fraction were most strongly associated with severe dysphagia. No spatial variation in radiosensitivity was observed for the pharyngeal mucosa. These results could inform clinical decision-support and radiotherapy planning. 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 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.