Eventually, Siegel envisions every patient's clinical course and data being saved and incorporated by decision-supporting algorithms to help physicians make better choices when treating patients. "Every patient's medical care would become a clinical trial," he said.

Perhaps ironically, providing personalized, individual care to patients requires that doctors collect data from as many people as possible to gain information on similar types of patients. That way, they can have a better sense of how to treat patients based on factors like their age, sex, geographical location and medical history.


Mining your own business

But there are some challenges to overcome before informatics-based imaging can become a reality.

For one, patient records are not currently set up in a way to allow for a seamless exchange of information, since the majority of patient data is taken down in an unstructured free text format. It's also not possible to search for terms within records, and information is often entered with nonstandard abbreviations and misspellings.

Restricted access to research data from big clinical trials is another obstacle to informatics-based diagnostic imaging, Siegel said.

Making use of data from the National Lung Screening Trial could potentially be a huge boon to practitioners, allowing other factors such as geographical location, age, or nodule shape to help refine current Fleischner Society diagnostic guidelines, which are based on nodule size.

On the whole, however, research data from these trials remain inaccessible. Even when this data is technically accessible, researchers must jump through cumbersome bureaucratic hoops to request permission for access with long periods of wait, often with no guarantee of success.

Playing tag

To see how much further diagnostic imaging's role in medicine could be expanded, Siegel is currently spearheading the Vasari Rembrandt Project, which explores correlations between patient genetic expression and tissue features in radiological images.

Researchers took MRI scans of brain tumors and categorized and tagged them according to over 30 different parameters. They were able to cross correlate imaging features with specific genes to predict genetic data. Siegel sees this as the way of the future in imaging.

"We need to create a mechanism to tag content of images and correlate it with genetic data," he said. Ideally, features in imaging exams and their changes over time would be rigorously quantified, tagged and made searchable, allowing algorithms to integrate this information alongside other data sets to help predict things like patient survival or disease expression.

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