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FDA Clearance for New Oncological Imaging Analytics Software

By MedImaging International staff writers
Posted on 21 Feb 2016
A new analytics software package intended to increase radiologists' workflow efficiencies for oncological cases, and advanced decision support has received US Food and Drug Administration (FDA) 510(k) clearance.

The software package features a quantitative imaging dashboard with time-sequenced Electronic Health Record (EHR) information, image characterization, and image tracking. Another feature is the ability to track a tumor or nodule over multiple, spatially registered imaging exams to show whether the cancer is stable, shrinking, or growing. Visualizing changes in the structure of cancers is important for monitoring and/or adjusting the treatment regime of a patient.

The imaging analytics package was developed by HealthMyne (Madison, WI, USA), a company that provides imaging informatics data mining solutions, and solutions for automating data capture, and structured reporting. EHR information on the software dashboard is provided by Epic Systems (Verona, Wisconsin, USA).

The HealthMyne software can be used for automatic identification of nodule growth in lung cancer screening programs, which provides an indication of potential malignancy of a nodule, and additional follow-up treatment that may be necessary. In addition, in view of the large increase in the number of cancer treatment options available today, the HealthMyne package can be used to provide quantitative insight and improve clinical decision-making.

Roger Chylla, CTO of HealthMyne, said, “By automatically delineating the boundaries of a lung nodule (or tumor), our software can make other sophisticated measurements within those boundaries - such as quantitatively describing important properties like size, shape, and texture. Physicians can examine these properties to make critical judgments about a patient’s prognosis, follow-up schedule, or how to best treat them.”

Related Links:

HealthMyne
Epic Systems


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Image: The diagnostic tool could improve diagnosis and treatment decisions for patients with chronic lung infections (Photo courtesy of SNMMI)

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