  The early identification of malignant pulmonary nodules is critical for
better lung cancer prognosis and less invasive chemo or radio therapies. Nodule
malignancy assessment done by radiologists is extremely useful for planning a
preventive intervention but is, unfortunately, a complex, time-consuming and
error-prone task. This explains the lack of large datasets containing
radiologists malignancy characterization of nodules. In this article, we
propose to assess nodule malignancy through 3D convolutional neural networks
and to integrate it in an automated end-to-end existing pipeline of lung cancer
detection. For training and testing purposes we used independent subsets of the
LIDC dataset. Adding the probabilities of nodules malignity in a baseline lung
cancer pipeline improved its F1-weighted score by 14.7%, whereas integrating
the malignancy model itself using transfer learning outperformed the baseline
prediction by 11.8% of F1-weighted score. Despite the limited size of the lung
cancer datasets, integrating predictive models of nodule malignancy improves
prediction of lung cancer.
