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DOI 10.1097/rct.0000000000000269

Медицинская библиотека · Journal of computer assisted tomography · 2015

Prediction of Adrenal Adenomas With Hypercortisolism by Using Adrenal Computed Tomography: Emphasis on Contralateral Adrenal Thinning.

Park Sung Yoon, Oh Young Taik, Jung Dae Chul, Rhee Yumie

СтатьяПоддержка исследования: вне правительства СШАПеревод пока не подготовлен

Аннотация

PURPOSE: To analyze computed tomography (CT) characteristics of adrenal adenomas with hypercortisolism on adrenal CT compared to adenomas with hyperaldosteronism.

MATERIALS AND

METHODS

Thirty-two adrenal adenomas, consisting of group A (hypercortisolism, n = 16) and group B (hyperaldosteronism, n = 16), were evaluated with adrenal CT. Thirty-four patients without adrenal nodule on CT and hormonal abnormality (group C) were selected for comparison. In adenomas, lesion size, attenuation value, and absolute and relative washout were compared between groups A and B. The mean adrenal thickness was compared quantitatively and qualitatively between groups.

RESULTS

All of adenomas satisfied absolute (≥60%) or relative (≥40%) washout criteria of adrenal CT. The mean adrenal thickness was 1.6 ± 0.3 mm in group A, 2.9 ± 0.5 mm in group B, and 3.0 ± 0.4 mm in group C (group A versus B or C, P < 0.001; group B versus C, P = 0.775). For differentiating group A from B, the area under the curve of quantitative and qualitative analyses for contralateral adrenal thickness was 0.982 and 0.934, respectively. Both sensitivity and specificity with cutoff of 2.0 mm or less in thickness and probably thin or more in visual score were 93.7% and 93.7%, and 87.5% and 93.7%, respectively, for predicting group A.

CONCLUSIONS

Contralateral adrenal thinning on CT helps predict adrenal adenomas with hypercortisolism.

Для цитирования: Park Sung Yoon, Oh Young Taik, Jung Dae Chul, Rhee Yumie. Prediction of Adrenal Adenomas With Hypercortisolism by Using Adrenal Computed Tomography: Emphasis on Contralateral Adrenal Thinning.. Journal of computer assisted tomography. 2015. DOI: 10.1097/rct.0000000000000269.

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