  Lung cancer is the leading cause of cancer related mortality by a significant
margin. While new technologies, such as image segmentation, have been paramount
to improved detection and earlier diagnoses, there are still significant
challenges in treating the disease. In particular, despite an increased number
of curative resections, many postoperative patients still develop recurrent
lesions. Consequently, there is a significant need for prognostic tools that
can more accurately predict a patient's risk for recurrence.
  In this paper, we explore the use of convolutional neural networks (CNNs) for
the segmentation and recurrence risk prediction of lung tumors that are present
in preoperative computed tomography (CT) images. First, expanding upon recent
progress in medical image segmentation, a residual U-Net is used to localize
and characterize each nodule. Then, the identified tumors are passed to a
second CNN for recurrence risk prediction. The system's final results are
produced with a random forest classifier that synthesizes the predictions of
the second network with clinical attributes. The segmentation stage uses the
LIDC-IDRI dataset and achieves a dice score of 70.3%. The recurrence risk stage
uses the NLST dataset from the National Cancer institute and achieves an AUC of
73.0%. Our proposed framework demonstrates that first, automated nodule
segmentation methods can generalize to enable pipelines for a wide range of
multitask systems and second, that deep learning and image processing have the
potential to improve current prognostic tools. To the best of our knowledge, it
is the first fully automated segmentation and recurrence risk prediction
system.
