  Lung cancer has a high rate of recurrence in early-stage patients. Predicting
the post-surgical recurrence in lung cancer patients has traditionally been
approached using single modality information of genomics or radiology images.
We investigate the potential of multimodal fusion for this task. By combining
computed tomography (CT) images and genomics, we demonstrate improved
prediction of recurrence using linear Cox proportional hazards models with
elastic net regularization. We work on a recent non-small cell lung cancer
(NSCLC) radiogenomics dataset of 130 patients and observe an increase in
concordance-index values of up to 10%. Employing non-linear methods from the
neural network literature, such as multi-layer perceptrons and visual-question
answering fusion modules, did not improve performance consistently. This
indicates the need for larger multimodal datasets and fusion techniques better
adapted to this biological setting.
