  In this study, we present a hybrid CNN-RNN approach to investigate long-term
survival of subjects in a lung cancer screening study. Subjects who died of
cardiovascular and respiratory causes were identified whereby the CNN model was
used to capture imaging features in the CT scans and the RNN model was used to
investigate time series and thus global information. The models were trained on
subjects who underwent cardiovascular and respiratory deaths and a control
cohort matched to participant age, gender, and smoking history. The combined
model can achieve an AUC of 0.76 which outperforms humans at cardiovascular
mortality prediction. The corresponding F1 and Matthews Correlation Coefficient
are 0.63 and 0.42 respectively. The generalisability of the model is further
validated on an 'external' cohort. The same models were applied to survival
analysis with the Cox Proportional Hazard model. It was demonstrated that
incorporating the follow-up history can lead to improvement in survival
prediction. The Cox neural network can achieve an IPCW C-index of 0.75 on the
internal dataset and 0.69 on an external dataset. Delineating imaging features
associated with long-term survival can help focus preventative interventions
appropriately, particularly for under-recognised pathologies thereby
potentially reducing patient morbidity.

Machine Learning