Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma

Chortis, Vasileios; Bancos, Irina; Nijman, Thomas; Gilligan, Lorna C.; Taylor, Angela E.; Ronchi, Cristina L.; O'Reilly, Michael W.; Schreiner, Jochen; Asia, Miriam; Riester, Anna; Perotti, Paola; Libe, Rosella; Quinkler, Marcus; Canu, Letizia; Paiva, Isabel; Bugalho, Maria J.; Kastelan, Darko; Dennedy, M. Conall; Sherlock, Mark; Ambroziak, Urszula; Vassiliadi, Dimitra; Bertherat, Jerome; Beuschlein, Felix; Fassnacht, Martin; Deeks, Jonathan J.; Biehl, Michael und Arlt, Wiebke (2020): Urine Steroid Metabolomics as a Novel Tool for Detection of Recurrent Adrenocortical Carcinoma. In: Journal of Clinical Endocrinology & Metabolism, Bd. 105, Nr. 3, E307-E318

Abstract

Context: 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). Objective, Design, Setting: This proof-of-concept study evaluated the performance of urine steroid metabolomics as a tool for postoperative recurrence detection after microscopically complete (R0) resection of ACC. Patients and Methods: 135 patients from 14 clinical centers provided postoperative urine samples, which were analyzed by gas chromatography-mass spectrometry. We assessed the utility of these urine steroid profiles in detecting ACC recurrence, either when interpreted by expert clinicians or when analyzed by random forest, a machine learning-based classifier. Radiological recurrence detection served as the reference standard. Results: Imaging detected recurrent disease in 42 of 135 patients;32 had provided pre- and post-recurrence urine samples. 39 patients remained disease-free for >= 3 years. The urine "steroid fingerprint" at recurrence resembled that observed before R0 resection in the majority of cases. Review of longitudinally collected urine steroid profiles by 3 blinded experts detected recurrence by the time of radiological diagnosis in 50% to 72% of cases, improving to 69% to 92%, if a preoperative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22% to 39% of patients. Specificities varied considerably, ranging from 61% to 97%. The computational classifier detected ACC recurrence with superior accuracy (sensitivity = specificity = 81%). Conclusion: Urine steroid metabolomics is a promising tool for postoperative recurrence detection in ACC;availability of a preoperative urine considerably improves the ability to detect ACC recurrence.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Medizin
• Themengebiete: 600 Technik, Medizin, angewandte Wissenschaften > 610 Medizin und Gesundheit
• ISSN: 0021-972X
• Sprache: Englisch