  We propose new methods for the prediction of 5-year mortality in elderly
individuals using chest computed tomography (CT). The methods consist of a
classifier that performs this prediction using a set of features extracted from
the CT image and segmentation maps of multiple anatomic structures. We explore
two approaches: 1) a unified framework based on deep learning, where features
and classifier are automatically learned in a single optimisation process; and
2) a multi-stage framework based on the design and selection/extraction of
hand-crafted radiomics features, followed by the classifier learning process.
Experimental results, based on a dataset of 48 annotated chest CTs, show that
the deep learning model produces a mean 5-year mortality prediction accuracy of
68.5%, while radiomics produces a mean accuracy that varies between 56% to 66%
(depending on the feature selection/extraction method and classifier). The
successful development of the proposed models has the potential to make a
profound impact in preventive and personalised healthcare.

Computer Vision and Pattern Recognition