In the U.S., cancer will strike two in every five people in their lifetimes. But it affects all of us. That’s why, in 2016, the office of the Vice President announced the Cancer Moonshot. It’s an audacious effort to make a decade’s worth of progress in cancer prevention, diagnosis, and treatment in just five years.
The 2017 Data Science Bowl® will pursue one of the Cancer Moonshot’s key goals: unleashing the power of data against this deadly disease. Presented by Booz Allen and Kaggle, the competition will convene the data science and medical communities to develop cancer detection algorithms, and help end the disease as we know it.
The Lung Cancer Detection Challenge
Lung cancer is one of the most common types of cancer, with nearly 225,000 new cases of the disease expected in the U.S. in 2016.
Early detection is critical, as it opens a range of treatment options not available when cancer is detected at later, more advanced stages. Low-dose computed tomography (CT) is a potential breakthrough technology for early detection, with the ability to reduce deaths by 20%.1 Often, suspicious lesions identified in screening are initially assessed as high risk of cancer, but after additional follow-up tests, they turn out to be non-cancerous (false positives from the initial screening).2 Can machine learning reduce the number of radiology exams flagged for potentially unnecessary follow up and avoid patient anxiety?