  Lung cancer is the leading cause of cancer deaths. Early detection through
low-dose computed tomography (CT) screening has been shown to significantly
reduce mortality but suffers from a high false positive rate that leads to
unnecessary diagnostic procedures. Quantitative image analysis coupled to deep
learning techniques has the potential to reduce this false positive rate. We
conducted a computational analysis of 1449 low-dose CT studies drawn from the
National Lung Screening Trial (NLST) cohort. We applied to this cohort our
newly developed algorithm, DeepScreener, which is based on a novel deep
learning approach. The algorithm, after the training process using about 3000
CT studies, does not require lung nodule annotations to conduct cancer
prediction. The algorithm uses consecutive slices and multi-task features to
determine whether a nodule is likely to be cancer, and a spatial pyramid to
detect nodules at different scales. We find that the algorithm can predict a
patient's cancer status from a volumetric lung CT image with high accuracy
(78.2%, with area under the Receiver Operating Characteristic curve (AUC) of
0.858). Our preliminary framework ranked 16th of 1972 teams (top 1%) in the
Data Science Bowl 2017 (DSB2017) competition, based on the challenge datasets.
We report here the application of DeepScreener on an independent NLST test set.
This study indicates that the deep learning approach has the potential to
significantly reduce the false positive rate in lung cancer screening with
low-dose CT scans.
