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Lung cancer is the leading cause of cancer related mortality by a significantmargin. While new technologies, such as image segmentation, have been paramountto improved detection and earlier diagnoses, there are still significantchallenges in treating the disease. In particular, despite an increased numberof curative resections, many postoperative patients still develop recurrentlesions. Consequently, there is a significant need for prognostic tools thatcan more accurately predict a patient's risk for recurrence.  In this paper, we explore the use of convolutional neural networks (CNNs) forthe segmentation and recurrence risk prediction of lung tumors that are presentin preoperative computed tomography (CT) images. First, expanding upon recentprogress in medical image segmentation, a residual U-Net is used to localizeand characterize each nodule. Then, the identified tumors are passed to asecond CNN for recurrence risk prediction. The system's final results areproduced with a random forest classifier that synthesizes the predictions ofthe second network with clinical attributes. The segmentation stage uses theLIDC-IDRI dataset and achieves a dice score of 70.3%. The recurrence risk stageuses the NLST dataset from the National Cancer institute and achieves an AUC of73.0%. Our proposed framework demonstrates that first, automated nodulesegmentation methods can generalize to enable pipelines for a wide range ofmultitask systems and second, that deep learning and image processing have thepotential to improve current prognostic tools. To the best of our knowledge, itis the first fully automated segmentation and recurrence risk predictionsystem.

Automated Segmentation and Recurrence Risk Prediction of Surgically Resected Lung Tumors with Adaptive Convolutional Neural Networks