ANZ journal of surgery · 2017
Predicting malignancy in adrenal incidentaloma and evaluation of a novel risk stratification algorithm.
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Foo Elizabeth, Turner Robin, Wang Kuan-Chi, Aniss Adam, Gill Anthony J, Sidhu Stanley, Clifton-Bligh Roderick, Sywak Mark
Аннотация
BACKGROUND: Incidentally discovered adrenal lesions known as adrenal incidentalomas (AI) are being encountered with increasing frequency due to the widespread use of abdominal computed tomography (CT). The aim of this study was to identify the clinical predictors of malignancy in AI and to evaluate the accuracy of a recently proposed risk stratification algorithm.
METHODS: A retrospective analysis of 96 patients presenting with AI between 2004 and 2014 was undertaken; 66 patients underwent adrenalectomy, and 30 were managed non-operatively. Univariate analysis including patient demographics, CT features of tumour size, density and heterogeneity was performed. Hormonal parameters including 24-h urinary-free cortisol and serum dehydroepiandrosterone sulphate (DHEAS) were also included. A Cleveland Clinic risk stratification model utilizing adrenal size and density was evaluated.
RESULTS: The overall rate of malignancy was 8%. On univariate analysis, the following preoperative variables were predictive of malignancy - tumour size on pathology (P = 0.0031) and CT (P = 0.0016), heterogeneity on CT imaging (P = 0.0036), a relative percentage washout of less than 40% (P = 0.0178), elevated 24-h urinary-free cortisol levels (P = 0.0176), elevated DHEAs (P = 0.0061) and younger age at presentation (P < 0.0001). Evaluation of the Cleveland Clinic algorithm found an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.52-1.00).
CONCLUSION: CT characteristics of tumour size, density and heterogeneity are significantly associated with malignancy in AI and applied together reliably exclude malignancy. The risk stratification algorithm utilizing size and density alone may fail to identify some smaller adrenal cancers.
METHODS: A retrospective analysis of 96 patients presenting with AI between 2004 and 2014 was undertaken; 66 patients underwent adrenalectomy, and 30 were managed non-operatively. Univariate analysis including patient demographics, CT features of tumour size, density and heterogeneity was performed. Hormonal parameters including 24-h urinary-free cortisol and serum dehydroepiandrosterone sulphate (DHEAS) were also included. A Cleveland Clinic risk stratification model utilizing adrenal size and density was evaluated.
RESULTS: The overall rate of malignancy was 8%. On univariate analysis, the following preoperative variables were predictive of malignancy - tumour size on pathology (P = 0.0031) and CT (P = 0.0016), heterogeneity on CT imaging (P = 0.0036), a relative percentage washout of less than 40% (P = 0.0178), elevated 24-h urinary-free cortisol levels (P = 0.0176), elevated DHEAs (P = 0.0061) and younger age at presentation (P < 0.0001). Evaluation of the Cleveland Clinic algorithm found an area under the receiver operating characteristic curve of 0.81 (95% confidence interval 0.52-1.00).
CONCLUSION: CT characteristics of tumour size, density and heterogeneity are significantly associated with malignancy in AI and applied together reliably exclude malignancy. The risk stratification algorithm utilizing size and density alone may fail to identify some smaller adrenal cancers.