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Updated: Aug 25, 2025
A Novel Method: Super-selective Adrenal Venous Sampling
Published on: September 15, 2017
Distinguishing between metastatic and benign adrenal masses in patients with extra-adrenal malignancies
Jinchao Chen1, Yedie He1, Xiaowei Zeng1
1Department of Urologic Surgery, The Cancer Hospital of the University of Chinese Academy of Sciences, Zhejiang Cancer Hospital, Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China.
Background And Objectives:
The adrenal gland is a common organ involved in metastasis. This study aimed to compare adrenal metastases (AMs) and adrenal benign masses (ABMs) of patients with extra-adrenal malignancies during the staging or follow-up.
Methods:
We retrospectively collected data from 120 patients with AMs and 87 patients with ABMs. The clinical characteristics, imaging features, pathology, and treatment regimes were analyzed.
Results:
The most common types of extra-adrenal malignancies in patients with ABMs included thyroid, kidney, and gynecological cancers. On the other hand, lung and kidney cancers and lymphoma were the most frequent primary cancers of AMs. The age and incidence of symptoms were significantly higher in patients with AM. Radiological analysis showed that AMs tended to have larger tumor sizes and higher attenuation values than ABMs on pre-contrast computed tomography (CT). The diagnostic accuracy of positron emission tomography-CT for AM was 94.1%. An adrenal biopsy had a diagnostic accuracy of 92.5%. A multivariate logistic regression model demonstrated that the origins of extra-adrenal malignancies, the enhancement pattern, and attenuation values in pre-contrast CT were independent predictors of AMs. The sensitivity and specificity of this predictive model of combination was 92.5% and 74.1%, respectively.
Conclusions:
The differential diagnosis between AMs and ABMs is extremely important. The combination of origin of first malignancy, enhancement pattern and CT value in non-enhanced phase is a valuable model for predicting AMs.
