Lung cancer is one of the world’s most prevalent, deadly, and financially costly cancers. It accounts for 2.21 million deaths per year worldwide and is the leading cause of cancer mortality. Just within the United States, there there are hundreds of thousands of cases, of which a significant number die. In 2018, for example, there were 218,000 lung cancer diagnoses of which 142,080 cases were fatal (65% mortality) despite significant efforts and expenditures for detection and treatment. In 2020 alone, $23.8 billion USD in claims were made for lung cancer treatment accounting for nearly 14% of the total ($174 billion) spent on cancer treatment that year.
Early detection, diagnosis, and treatment are critical to reducing mortality and cost. While significant progress has been made in reducing the death rate of other cancers, the five-year survival rate of lung cancer (18.6%) is much lower than colorectal (64.5%), breast (89.6%), and prostate (98.2%). Yet, if detected early, the five-year survival rate rises to 56%. Unfortunately, only 16% of lung cancer cases are diagnosed at an early stage. Worse, nearly two-thirds of lung cancer patients are diagnosed during advanced stages of cancer when curative treatment is no longer possible. If lung cancer could be detected earlier, while the disease is still located within the lungs and has not spread to other parts of the body, there is hope that we could significantly reduce the death rate.
Why is early detection such a challenge and what are the technical issues which must be overcome? There are many reasons:
These points lead to a number of questions. Can AI help to provide a "second pair of eyes" for detecting tumors more accurately and earlier in their progression? What type of impact might a successful AI system realistically have on lung cancer treatment? Is this the type of problem that AI is capable of tackling?
In this series, we'll attempt to answer these points. First, we'll look at the technical challenges of early detection and survey some of the progress being made. In Part 2, we'll try to place how these challenges in the context of how lung cancer is currently treated. In Part 3, we'll take the conclusions of that analysis and use them to describe the shape of an AI solution.