Project background

The problem

Lung cancer causes more deaths each year than any other cancer. As with all cancers, the earlier it can be detected the better the patient outcomes. When the disease is still localized in the lungs, the five-year survival rate is 55%. For tumors that have spread to other organs, the five-year survival rate drops to just 4%.

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Built on machine learning

The primary algorithm adapted in this project comes from the top-performing model in the 2017 Data Science Bowl, a competition where thousands of machine learning models vied to accurately classify cancer risk from lung CT scans. This solution was able to detect nodules with greater than 85% recall and reduce the rate of false positive results compared with previous clinical studies. The algorithm is built using a deep learning segmentation architecture called UNET, which uses a 3D region proposal network to detect potential nodules, and then a similar network engineered for diagnosis to assign cancer probabilities to the proposed nodules. When combined, these two UNET based modules outperformed all other models entered in the competition.

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Project partners

From before the project started, we had an amazing group of of technical, clinical, and domain experts guiding the structure of the challenge and the design of the technical approach. This was an ambitious project that relied on a wide range of expertise and experience. A technical panel provided input on code contributions, while clinicians and researchers provided feedback on the design and implementation of those changes, and context for why this matters in a clinical setting.

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