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Editor's e-Note
As AI becomes ingrained in radiology practice, it is proving useful for multiple tasks. This month’s e-News Exclusive details an exciting development in cancer classification. Researchers at the Mallinckrodt Institute have developed an algorithm that can identify brain cancer types by looking at MRI scans.

How is your organization using AI? Let us know on Twitter and/or Facebook.

Enjoy the newsletter.

— Dave Yeager, editor
e-News Exclusive
Deep Learning Classifies Brain Tumors With Single MRI

A team of researchers at Washington University School of Medicine has developed a deep learning model that is capable of classifying a brain tumor as one of six common types, using a single 3D MRI scan, according to a study published in Radiology: Artificial Intelligence.

“This is the first study to address the most common intracranial tumors and to directly determine the tumor class or the absence of tumor from a 3D MRI volume,” says Satrajit Chakrabarty, MS, a doctoral student under the direction of Aristeidis Sotiras, PhD, and Daniel Marcus, PhD, in the Mallinckrodt Institute of Radiology’s Computational Imaging Lab at Washington University School of Medicine in St. Louis.

The six most common intracranial tumor types are high-grade glioma, low-grade glioma, brain metastases, meningioma, pituitary adenoma, and acoustic neuroma. Each was documented through histopathology, which requires surgically removing tissue from the site of a suspected cancer and examining it under a microscope. According to Chakrabarty, machine and deep learning approaches using MRI data could potentially automate the detection and classification of brain tumors.

“Noninvasive MRI may be used as a complement, or in some cases, as an alternative to histopathologic examination,” Chakrabarty says.

Full story »
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In This e-Newsletter
Worth Repeating
“This statement is an important reminder that patients may receive substantial clinical benefit from imaging exams. While we want to see prudent use of radiation in medical imaging, and many of our scientific members are working on means of reducing overall patient radiation dose, we believe it is an important matter of patient safety and clinical care that decisions on the use of imaging exams be made solely on the presenting clinical need and not on prior radiation dose.”

James Dobbins, PhD, president of the American Association of Physicists in Medicine, regarding a joint statement issued in conjunction with ACR and the Health Physics Society regarding cumulative radiation dose limits for patient imaging
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