  In the management of lung nodules, we are desirable to predict nodule
evolution in terms of its diameter variation on Computed Tomography (CT) scans
and then provide a follow-up recommendation according to the predicted result
of the growing trend of the nodule. In order to improve the performance of
growth trend prediction for lung nodules, it is vital to compare the changes of
the same nodule in consecutive CT scans. Motivated by this, we screened out
4,666 subjects with more than two consecutive CT scans from the National Lung
Screening Trial (NLST) dataset to organize a temporal dataset called NLSTt. In
specific, we first detect and pair regions of interest (ROIs) covering the same
nodule based on registered CT scans. After that, we predict the texture
category and diameter size of the nodules through models. Last, we annotate the
evolution class of each nodule according to its changes in diameter. Based on
the built NLSTt dataset, we propose a siamese encoder to simultaneously exploit
the discriminative features of 3D ROIs detected from consecutive CT scans. Then
we novelly design a spatial-temporal mixer (STM) to leverage the interval
changes of the same nodule in sequential 3D ROIs and capture spatial
dependencies of nodule regions and the current 3D ROI. According to the
clinical diagnosis routine, we employ hierarchical loss to pay more attention
to growing nodules. The extensive experiments on our organized dataset
demonstrate the advantage of our proposed method. We also conduct experiments
on an in-house dataset to evaluate the clinical utility of our method by
comparing it against skilled clinicians.
