AI could spot lung cancer before radiologists

The new study from Google and Northwestern Medicine compared the deep-learning system against radiologists on LDCTs for patients, some of whom had biopsy confirmed cancer within a year. In most comparisons, the model performed at or better than radiologists.

The system provides an automated image evaluation system to enhance the accuracy of early lung cancer diagnosis that could lead to earlier treatment, and is a technique that teaches computers to learn by example.

The deep-learning system also interestingly produced fewer false positives and fewer false negatives, meaning it was more effective and could lead to fewer unnecessary follow-up procedures and fewer missed tumours, if it were used in a clinical setting.

"Radiologists generally examine hundreds of two-dimensional images or ‘slices’ in a single CT scan but this new machine learning system views the lungs in a huge, single three-dimensional image," said study co-author Dr Mozziyar Etemadi, a research assistant professor of anesthesiology at Northwestern University Feinberg School of Medicine.

"AI in 3D can be much more sensitive in its ability to detect early lung cancer than the human eye looking at 2-D images. This is technically ‘4D' because it is not only looking at one CT scan, but two (the current and prior scan) over time.

"In order to build the AI to view the CTs in this way, you require an enormous computer system of Google-scale. The concept is novel but the actual engineering of it is also novel because of the scale."

Lung cancer is the most common cause of cancer-related death in the United States, resulting in an estimated 160,000 deaths in 2018. Chest screening can identify the cancer and reduce death rates, however, high error rates and the limited access to these screenings mean that many lung cancers are usually detected at advanced stages, when they are hard to treat.

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