  Importance: Lung cancer is the leading cause of cancer mortality in the US,
responsible for more deaths than breast, prostate, colon and pancreas cancer
combined and it has been recently demonstrated that low-dose computed
tomography (CT) screening of the chest can significantly reduce this death
rate.
  Objective: To compare the performance of a deep learning model to
state-of-the-art automated algorithms and radiologists as well as assessing the
robustness of the algorithm in heterogeneous datasets.
  Design, Setting, and Participants: Three low-dose CT lung cancer screening
datasets from heterogeneous sources were used, including National Lung
Screening Trial (NLST, n=3410), Lahey Hospital and Medical Center (LHMC,
n=3174) data, Kaggle competition data (from both stages, n=1595+505) and the
University of Chicago data (UCM, a subset of NLST, annotated by radiologists,
n=197). Relevant works on automated methods for Lung Cancer malignancy
estimation have used significantly less data in size and diversity. At the
first stage, our framework employs a nodule detector; while in the second
stage, we use both the image area around the nodules and nodule features as
inputs to a neural network that estimates the malignancy risk for the entire CT
scan. We trained our two-stage algorithm on a part of the NLST dataset, and
validated it on the other datasets.
  Results, Conclusions, and Relevance: The proposed deep learning model: (a)
generalizes well across all three data sets, achieving AUC between 86% to 94%;
(b) has better performance than the widely accepted PanCan Risk Model,
achieving 11% better AUC score; (c) has improved performance compared to the
state-of-the-art represented by the winners of the Kaggle Data Science Bowl
2017 competition on lung cancer screening; (d) has comparable performance to
radiologists in estimating cancer risk at a patient level.
