Automated 5-year Mortality Prediction using Deep Learning and Radiomics Features from Chest Computed Tomography
This work addresses mortality prediction for elderly patients in healthcare, but it is incremental as it applies existing deep learning and radiomics techniques to a specific medical imaging task.
The paper tackled predicting 5-year mortality in elderly individuals using chest CT scans, achieving a mean accuracy of 68.5% with a deep learning model and 56-66% with radiomics methods.
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.