  Lung cancer is the leading cause of cancer-related mortality worldwide. Lung
cancer screening (LCS) using annual low-dose computed tomography (CT) scanning
has been proven to significantly reduce lung cancer mortality by detecting
cancerous lung nodules at an earlier stage. Improving risk stratification of
malignancy risk in lung nodules can be enhanced using machine/deep learning
algorithms. However most existing algorithms: a) have primarily assessed single
time-point CT data alone thereby failing to utilize the inherent advantages
contained within longitudinal imaging datasets; b) have not integrated into
computer models pertinent clinical data that might inform risk prediction; c)
have not assessed algorithm performance on the spectrum of nodules that are
most challenging for radiologists to interpret and where assistance from
analytic tools would be most beneficial.
  Here we show the performance of our time-series deep learning model
(DeepCAD-NLM-L) which integrates multi-model information across three
longitudinal data domains: nodule-specific, lung-specific, and clinical
demographic data. We compared our time-series deep learning model to a)
radiologist performance on CTs from the National Lung Screening Trial enriched
with the most challenging nodules for diagnosis; b) a nodule management
algorithm from a North London LCS study (SUMMIT). Our model demonstrated
comparable and complementary performance to radiologists when interpreting
challenging lung nodules and showed improved performance (AUC=88\%) against
models utilizing single time-point data only. The results emphasise the
importance of time-series, multi-modal analysis when interpreting malignancy
risk in LCS.
