  Nodule malignancy assessment is a complex, time-consuming and error-prone
task. Current clinical practice requires measuring changes in size and density
of the nodule at different time-points. State of the art solutions rely on 3D
convolutional neural networks built on pulmonary nodules obtained from single
CT scan per patient. In this work, we propose a two-stream 3D convolutional
neural network that predicts malignancy by jointly analyzing two pulmonary
nodule volumes from the same patient taken at different time-points. Best
results achieve 77% of F1-score in test with an increment of 9% and 12% of
F1-score with respect to the same network trained with images from a single
time-point.
