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Please use this identifier to cite or link to this item: http://repository.li.mahidol.ac.th/dspace/handle/123456789/47044
Title: Normal adrenal glands in multidetector computed tomography
Authors: Voraparee Suvannarerg
Veekij Veerasomboonsin
Wanwarang Teerasamit
Faculty of Medicine, Siriraj Hospital, Mahidol University
Keywords: Medicine
Issue Date: 1-Jan-2018
Citation: Siriraj Medical Journal. Vol.70, No.6 (2018), 502-506
Abstract: © 2018 Siriraj Medical Journal. Objective: To characterize normal adrenal glands in an adult Thai population using multidetector computed tomography (MDCT). Methods: We retrospectively reviewed the scans of 175 consecutive patients who underwent renal CT angiography for potential kidney donation with attention to the location, morphology, and enhancement of both adrenal glands. Each reader measured the body and limb thickness in the axial and coronal planes, and the length in the axial plane. The interclass correlation coefficient (ICC) between the two readers was generated. We employed mean, SD, and 95 th percentile of normal adrenal gland size by using pooled data of both reviewers. Results: The 95 th percentiles of normal adrenal glands, which might be useful as cutoff values to distinguish between normal and pathological adrenal gland size, were calculated. The right adrenal gland was more often relatively cephalad to the left adrenal (58.9%). The most common morphology of both adrenal glands was the inverted Y, with inverted V and linear shapes less common. Both adrenal glands showed homogeneous enhancement (100%) in the arterial and nephrographic phases. Conclusion: The majority of normal adrenal glands were right relatively cephalad to left, inverted Y shape, and homogenous enhancement, using the 95th percentile of size normal adrenal gland as a cutoff value to differentiate between normal and thickened limbs and body.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85059525201&origin=inward
http://repository.li.mahidol.ac.th/dspace/handle/123456789/47044
ISSN: 22288082
Appears in Collections:Scopus 2018

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