Modeling nodule growth via spatial transformation for follow-up prediction and diagnosis

Lung cancer is the leading cause of cancer deaths worldwide with its mortality rate higher than that of other leading cancers. Nodules in the lungs can grow quite large without any obvious symptoms until the condition has reached a certain stage. Early detection and diagnosis of growing nodules can lay a good foundation for further treatment and potentially improve lung cancer survival rate. In this paper a unified framework is proposed for visual prediction and diagnosis of follow-up lung nodules. Future nodule growth is predicted by modeling the nodule growth between consecutive Computed Tomography (CT) scans via spatial transformation using convolutional network. Nodule classification is made based on the predicted nodule growth and previous diagnosis. Experiments are conducted on a longitudinal follow-up dataset of 615 LDCT scans of 153 lung nodules in early stages from 125 patients. Quantitative and qualitative results demonstrate the effectiveness of the proposed method.