Space.com's Samantha Mathewson reports that advanced cancer research is calling on techniques used by NASA scientists who analyze satellite imagery to find commonalities among stars, planets, and galaxies in space.

Scientists from NASA's Jet Propulsion Laboratory (JPL) use complex machine learning algorithms to identify similarities among galaxies that may otherwise be overlooked, NASA officials said in a statement. Using similar techniques, medical professionals are able to analyze a lung sample for common cancer biomarkers.

Making medical data accessible across the globe will solve a common problem with uniformity. Previously, medical data such as patient age, cancer type or other characteristics was not labeled and stored uniformly (the same everywhere), so it could not be shared and studied by everyone, NASA officials said.

In the years to come, the NCI plans to incorporate image-recognition technology to help archive images of cancer specimens from the EDRN. Then, much like how computer algorithms comb through images of star clusters, these images could be analyzed for early signs of cancer based on a patient's age, ethnic background, and other demographics.

“As we develop more automated methods for detecting and classifying features in images, we see great opportunities for enhancing data discovery,” said Dan Crichton, the head of JPL's Center for Data Science and Technology. “We have examples where algorithms for detection of features in astronomy images have been transferred to biology and vice versa.”