  With an estimated 160,000 deaths in 2018, lung cancer is the most common
cause of cancer death in the United States. Lung cancer CT screening has been
shown to reduce mortality by up to 40% and is now included in US screening
guidelines. Reducing the high error rates in lung cancer screening is
imperative because of the high clinical and financial costs caused by diagnosis
mistakes. Despite the use of standards for radiological diagnosis, persistent
inter-grader variability and incomplete characterization of comprehensive
imaging findings remain as limitations of current methods. These limitations
suggest opportunities for more sophisticated systems to improve performance and
inter-reader consistency. In this report, we reproduce a state-of-the-art deep
learning algorithm for lung cancer risk prediction. Our model predicts
malignancy probability and risk bucket classification from lung CT studies.
This allows for risk categorization of patients being screened and suggests the
most appropriate surveillance and management. Combining our solution high
accuracy, consistency and fully automated nature, our approach may enable
highly efficient screening procedures and accelerate the adoption of lung
cancer screening.
