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 5072% of cases, improving to 6992%, 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 2239% 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.