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Query: UMLS:C0011168 (
dysphagia
)
15,644
document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)
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 (PLR
standard
) 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
.
...
PMID:Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy. 2939 42
