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著者: Jacqueline Dinnes, Irina Bancos, Lavinia Ferrante di Ruffano, Vasileios Chortis, Clare Davenport, Susan Bayliss, Anju Sahdev, Peter Guest, Martin Fassnacht, Jonathan J Deeks, Wiebke Arlt
雑誌名: Eur J Endocrinol. 2016 Aug;175(2):R51-64. doi: 10.1530/EJE-16-0461. Epub 2016 Jun 2.
Abstract/Text
OBJECTIVE: Adrenal masses are incidentally discovered in 5% of CT scans. In 2013/2014, 81 million CT examinations were undertaken in the USA and 5 million in the UK. However, uncertainty remains around the optimal imaging approach for diagnosing malignancy. We aimed to review the evidence on the accuracy of imaging tests for differentiating malignant from benign adrenal masses.
DESIGN: A systematic review and meta-analysis was conducted.
METHODS: We searched MEDLINE, EMBASE, Cochrane CENTRAL Register of Controlled Trials, Science Citation Index, Conference Proceedings Citation Index, and ZETOC (January 1990 to August 2015). We included studies evaluating the accuracy of CT, MRI, or (18)F-fluoro-deoxyglucose (FDG)-PET compared with an adequate histological or imaging-based follow-up reference standard.
RESULTS: We identified 37 studies suitable for inclusion, after screening 5469 references and 525 full-text articles. Studies evaluated the accuracy of CT (n=16), MRI (n=15), and FDG-PET (n=9) and were generally small and at high or unclear risk of bias. Only 19 studies were eligible for meta-analysis. Limited data suggest that CT density >10HU has high sensitivity for detection of adrenal malignancy in participants with no prior indication for adrenal imaging, that is, masses with ≤10HU are unlikely to be malignant. All other estimates of test performance are based on too small numbers.
CONCLUSIONS: Despite their widespread use in routine assessment, there is insufficient evidence for the diagnostic value of individual imaging tests in distinguishing benign from malignant adrenal masses. Future research is urgently needed and should include prospective test validation studies for imaging and novel diagnostic approaches alongside detailed health economics analysis.
© 2016 The authors.
PMID 27257145 Eur J Endocrinol. 2016 Aug;175(2):R51-64. doi: 10.1530/EJE-16-0461. Epub 2016 Jun 2.
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