Google creates AI to detect when breast cancer spreads

Google's new artificial intelligence algorithm, dubbed LYNA, accurately detects the spread of breast cancer — but the company is taking pains to highlight that the tool is meant to assist, not replace, human pathologists.

LYNA, which stands for "LYmph Node Assistant," uses a type of AI modeled on how the human brain processes information, called "deep learning." A deep learning algorithm learns over time by extracting patterns from a data set. For Google's project, that means researchers "trained" it on pathology slides from patients with metastatic breast cancer, or the stage of breast cancer where the disease has spread from the primary tumor site to nearby lymph nodes.

In an Oct. 12 post on the company's research blog, Google outlined findings from two recent studies related to LYNA, which it calls a "proof-of-concept pathologist assistance tool."

The first study, published in the Archives of Pathology and Laboratory Medicine, applied the AI algorithm to two new sets of pathology slides to assess LYNA's accuracy. Google reported LYNA correctly distinguished slides with metastatic cancer from slides without the disease 99 percent of the time in both data sets. The algorithm could also pinpoint the location of cancers and other "suspicious regions" within each slide, some of which were too small to be consistently detected by the human eye.

Today, a pathologist's examination of a tumor using a microscope is considered the "gold standard" for cancer diagnosis, according to Google's blog post, despite the reality that small metastases, or micrometastases, are difficult to detect. Based on the findings from its first study, Google's research team suggested LYNA might prove a successful tool to highlight areas of concern for human pathologists to review before delivering their final diagnosis.

The second study, published in The American Journal of Surgical Pathology, explored the potential clinical or workflow benefits the AI algorithm provides. Six board-certified pathologists completed a simulated clinical assessment during the study, in which they reviewed images of lymph nodes for metastatic breast cancer both with and without assistance from LYNA. Using the AI algorithm improved diagnostic accuracy for the physicians, reducing the rate of missed micrometastases by a factor of two.

The pathologists also reported that diagnosing micrometastases was "easier" with LYNA, according to the study results. With LYNA, the average time for a pathologist to review a slide was only one minute, compared to two minutes without the tool.

"Encouragingly, pathologists with LYNA assistance were more accurate than either unassisted pathologists or the LYNA algorithm itself," according to Google's blog post. "This suggests the intriguing potential for assistive technologies such as LYNA to reduce the burden of repetitive identification tasks and to allow more time and energy for pathologists to focus on other, more challenging clinical and diagnostic tasks."

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