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Endocrine Abstracts (2015) 38 OC3.4 | DOI: 10.1530/endoabs.38.OC3.4

1Institute of Metabolism and Systems Research, Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, University of Birmingham, Birmingham, UK; 2Department of Medicine I, University Hospital, University of Wurzburg, Wurzburg, Germany; 3Medizinische Klinik und Poliklinik IV, Klinikum der Universitat Munchen, Munich, Germany; 4Istituto ToscanoTumori, University of Florence, Florence, Italy; 5Charite Campus Mitte, Charite University Medicine Berlin, Berlin, Germany; 6Medical School, National and Kapodistrian University of Athens, Athens, Greece; 7Institut National de la Santé et de la Recherche Médicale Unité 1016, Rene Descartes University, Paris, France; 8National University of Ireland, Galway, Ireland; 9University of Gronigen, Gronigen, The Netherlands.


Introduction: Adrenocortical carcinoma (ACC) is an aggressive malignancy with a high rate of recurrence. Regular post-operative follow-up imaging is necessary, but associated with high radiation exposure and frequent diagnostic ambiguity. Urine steroid metabolomics has recently been introduced as a novel diagnostic tool for the detection of adrenocortical malignancy in patients with adrenal incidentalomas. Here we present the first clinical study assessing the performance of this innovative approach in the context of follow-up after complete (R0) ACC resection.

Patients and methods: We included 166 patients from 13 centres registered with the European Network for the Study of Adrenal Tumours (ENSAT). We selected all patients recorded between 2008 and 2015 fulfilling the following criteria: i) recorded on the ENSAT registry as confirmed adrenocortical carcinoma with R0 primary tumour resection and ii) availability of at least two postoperative 24-h urines, one whilst disease-free and the other after recurrence. Twenty-four-hour urines were analysed by gas chromatography–mass spectrometry, with quantification of 38 distinct steroid metabolites. A machine learning-based computational algorithm was employed to detect ACC recurrence.

Results: Twenty-one patients developed 22 ACC recurrences during the study period as documented by serial cross-sectional imaging and biopsy where appropriate. Steroid metabolomics predicted disease recurrence at the time of first abnormal imaging with a sensitivity of 84% and specificity of 95%. Adjuvant mitotane in 12/21 patients did not affect accuracy. In the subgroup of patients for whom a diagnostic pre-operative 24-h urine sample was available (n=7), we were able to accurately detect all cases of recurrence (sensitivity and specificity 100%). In seven cases, biochemical evidence of disease recurrence pre-dated the first radiological detection by more than 2 months (range 2–11 months).

Conclusion: Our study provides proof-of-principle evidence suggesting a role for urine steroid metabolomics as a potent diagnostic tool in the follow-up monitoring of ACC.

Volume 38

Society for Endocrinology BES 2015

Edinburgh, UK
02 Nov 2015 - 04 Nov 2015

Society for Endocrinology 

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