  Accurate prognosis for an individual patient is a key component of precision
oncology. Recent advances in machine learning have enabled the development of
models using a wider range of data, including imaging. Radiomics aims to
extract quantitative predictive and prognostic biomarkers from routine medical
imaging, but evidence for computed tomography radiomics for prognosis remains
inconclusive. We have conducted an institutional machine learning challenge to
develop an accurate model for overall survival prediction in head and neck
cancer using clinical data etxracted from electronic medical records and
pre-treatment radiological images, as well as to evaluate the true added
benefit of radiomics for head and neck cancer prognosis. Using a large,
retrospective dataset of 2,552 patients and a rigorous evaluation framework, we
compared 12 different submissions using imaging and clinical data, separately
or in combination. The winning approach used non-linear, multitask learning on
clinical data and tumour volume, achieving high prognostic accuracy for 2-year
and lifetime survival prediction and outperforming models relying on clinical
data only, engineered radiomics and deep learning. Combining all submissions in
an ensemble model resulted in improved accuracy, with the highest gain from a
image-based deep learning model. Our results show the potential of machine
learning and simple, informative prognostic factors in combination with large
datasets as a tool to guide personalized cancer care.
