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Endocrine Abstracts (2019) 65 OP6.4 | DOI: 10.1530/endoabs.65.OP6.4

1Institute of Metabolism and Systems Research, University of Birmingham, Birmingham, UK; 2Centre for Endocrinology, Diabetes and Metabolism, Birmingham Health Partners, Birmingham, UK; 3Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groni, Netherlands; 4Divison of Endocrinology, Diabetes, Metabolism and Nutrition, Mayo Clinic, Rochester, MN, USA; 5Institute of Metabolism and Systems Research, University of Birmingh, Birmingham, UK; 6Division of Endocrinology and Diabetes, Department of Internal Medicine I, University Hospital, University of Würzburg, Würzburg, Germany; 7Medizinische Klinik and Poliklinik IV, Ludwig-Maximilians-Universität München, Munich, Germany; 8University of Turin, Turin, UK; 9INCa- COMETE, Cochin Hospital, Institut Cochin, Institut National de la Sante Ì et de la Recherche Medicale Unite Ì 1016, Rene Ì Descartes University, Paris, France; 10Endocrinology in Charlottenburg, Berlin, Germany; 11Department of Experimental and Clinical Biomedical Sciences, University of Florence, Florence, Italy; 12University Hospital of Coimbra, Coimbra, Portugal; 13Serviço de Endocrinologia Diabetes e Metabolismo, Hospital de Santa Maria, Lisbon, Portugal; 14Department of Endocrinology, University Hospital Centre Zagreb, Zagreb, Croatia; 15National University of Ireland Galway (NUIG), Galway, Ireland; 16Department of Endocrinology, Beaumont Hospital, Dublin and the Royal College of Surgeons, Republic of Ireland, Dublin, Ireland; 17Department of Internal Medicine and Endocrinology, Medical University of Warsaw, Warsaw, Poland; 18Evangelismos Hospital, Athens, Greece; 19Klinik für Endokrinologie, Diabetologie und Klinische Ernährung, Universitäts-Spital Zürich, Zürich, Switzerland; 20Institute of Applied Health Research, University of Birmingham, Birmingham, UK; 21NIHR Birmingham Biomedical Research Centre, University Birmingham NHS Hospital Trusts and University of Birmingham, Birmingham, UK; 22Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, University of Groningen, Groningen, Netherlands

Objective: Urine steroid metabolomics, combining mass spectrometry-based steroid profiling and machine learning, has been described as a novel diagnostic tool for detection of adrenocortical carcinoma (ACC). This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for post-operative recurrence detection after microscopically complete (R0) resection of ACC.

Methods: 135 ACC patients from 14 clinical centres provided post-operative urine samples, which were analysed by gas chromatography-mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by three expert clinicians, or when analysed by Random Forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard.

Results: Imaging detected recurrences in 32 patients who provided pre- and post-recurrence urines, while 39 of 135 patients remained disease-free for >3 years. The urine ‘steroid fingerprint’ at recurrence resembled that observed in the urine before R0 resection of ACC in the majority of cases. Expert review of longitudinally collected urine steroid profiles detected recurrence by the time of radiological diagnosis in 50–72% of cases, improving to 69–92%, if a urine steroid result pre-excision of the primary tumour was available. Mitotane use did not affect diagnostic success. Recurrence detection by steroid profiling preceded diagnosis by imaging by more than 2 months in 22–39% of successful detections. Specificities varied considerably between the experts (61%–97%). The computational classifier detected ACC recurrence with superior accuracy (sensitivity=specificity=81%). The deoxycortisol metabolite tetrahydro-11-deoxycortisol (THS) was the single most important steroid metabolite in differentiating post-recurrence urine samples from samples provided by non-recurred patients, followed by the mineralocorticoid precursor metabolite tetrahydrocorticosterone (THDOC) and the pregnenolone metabolite pregnenediol (5-PD).

Conclusion: Urine steroid metabolomics is a promising non-invasive, radiation-free tool for post-operative recurrence detection in ACC; availability of a pre-operative urine considerably improves the ability to detect ACC recurrence.

Volume 65

Society for Endocrinology BES 2019

Brighton, United Kingdom
11 Nov 2019 - 13 Nov 2019

Society for Endocrinology 

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