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Medical-grid researchers are not short on vision. Comparing images is just the first step. In cases where the scans match, doctors hope to be able to bore deeper into the histories of similar cases and learn which drugs or surgeries worked best. And Buetow says his trials could actually hasten the day when some cancer diagnoses are automated. A doctor could input images (and as the grid expands, blood test results, descriptions of genetic markers, and other patient data) and learn how frequently near-identical test results from patients around the world correlate with specific malignancies such as lymphomas, melanomas, or sarcomas.

And in the future, as gene-sequencing costs come down, the NCI’s grid could even include patients’ genomic information. “The power of the grid is in its capability to aggregate and correlate more and more public-health data from around the world,” said Mary Kratz of the University of Michigan Medical School, a technical advisor to the grid research community. “The more data you have, the more knowledge you generate.”

Meanwhile, mundane technical problems need solving.

Since the data that accompany images vary in type and format from hospital to hospital, researchers are developing standard formats that can harmonize them all. “We’re asking researchers at many competitive institutions to tear down barriers to sharing vast amounts of data,” says Howard Bilofsky, senior fellow at the Center for Bioinformatics at the University of Pennsylvania, which participates in NCI’s project. “Being able to share information in grids across the world in the arena of life science research is not something that is easily done.”

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