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 post-operative recurrence detection after microscopically complete (R0) resection of ACC.
PATIENTS AND METHODS 135 patients from 14 clinical centers provided post-operative 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 three blinded experts detected recurrence by the time of radiological diagnosis in 50-72% of cases, improving to 69-92%, if a pre-operative urine steroid result was available. Recurrence detection by steroid profiling preceded detection by imaging by more than 2 months in 22-39% 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 post-operative recurrence detection in ACC; availability of a pre-operative urine considerably improves the ability to detect ACC recurrence.