Within the ADHD population, features involved in the default mode network, which is a network of brain regions active when an individual is not engaged in a specific task, and the insular cortex, an area with diverse functions related to emotion, significantly contributed to discriminating the ADHD inattentive subtype from the combined subtype.
Overall, the radiomics signatures allowed discrimination of ADHD patients and healthy control children with 74 percent accuracy and discrimination of ADHD inattentive and ADHD combined subtypes with 80 percent accuracy.
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"This imaging-based classification model could be an objective adjunct to facilitate better clinical decision making," Dr. Gong said. "Additionally, the present study adds to the developing field of psychoradiology, which seems primed to play a major clinical role in guiding diagnostic and treatment planning decisions in patients with psychiatric disorders."
The researchers plan to recruit more newly diagnosed ADHD patients to validate the results and learn more about imaging-based classification. They also intend to apply the analytic approach to other mental or neurological disorders and test its feasibility in a clinical environment, where the fully automatic analytic framework can be readily deployed, Dr. Gong said.
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"Psychoradiology utility of MR Imaging for Diagnosis of Attention Deficit Hyperactivity Disorder: A Radiomics Analysis." Collaborating with Drs. Gong, Sun and Chen were Qiang Huang, Ph.D., Su Lui, Ph.D., Xiaoqi Huang, Ph.D., Yan Shi, M.D., Xin Xu, M.D., and John A. Sweeney, Ph.D.
Radiology is edited by Herbert Y. Kressel, M.D., Harvard Medical School, Boston, Mass., and owned and published by the Radiological Society of North America, Inc.
RSNA is an association of over 54,000 radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Ill.
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