  We address the problem of supporting radiologists in the longitudinal
management of lung cancer. Therefore, we proposed a deep learning pipeline,
composed of four stages that completely automatized from the detection of
nodules to the classification of cancer, through the detection of growth in the
nodules. In addition, the pipeline integrated a novel approach for nodule
growth detection, which relied on a recent hierarchical probabilistic U-Net
adapted to report uncertainty estimates. Also, a second novel method was
introduced for lung cancer nodule classification, integrating into a two stream
3D-CNN network the estimated nodule malignancy probabilities derived from a
pretrained nodule malignancy network. The pipeline was evaluated in a
longitudinal cohort and reported comparable performances to the state of art.
