  In radiotherapy for head and neck cancer, the radiation dose delivered to the
pharyngeal mucosa (mucosal lining of the throat) is thought to be a major
contributing factor to dysphagia (swallowing dysfunction), the most commonly
reported severe toxicity. There is a variation in the severity of dysphagia
experienced by patients. Understanding the role of the dose distribution in
dysphagia would allow improvements in the radiotherapy technique to be
explored. The 3D dose distributions delivered to the pharyngeal mucosa of 249
patients treated as part of clinical trials were reconstructed. Pydicom was
used to extract DICOM (digital imaging and communications in medicine) data
(the standard file formats for medical imaging and radiotherapy data). NumPy
and SciPy were used to manipulate the data to generate 3D maps of the dose
distribution delivered to the pharyngeal mucosa and calculate metrics
describing the dose distribution. Multivariate predictive modelling of severe
dysphagia, including descriptions of the dose distribution and relevant
clinical factors, was performed using Pandas and SciKit-Learn. Matplotlib and
Mayavi were used for 2D and 3D data visualisation. A support vector
classification model, with feature selection using randomised logistic
regression, to predict radiation-induced severe dysphagia, was trained. When
this model was independently validated, the area under the receiver operating
characteristic curve was 0.54. The model has poor predictive power and work is
ongoing to improve the model through alternative feature engineering and
statistical modelling approaches.
