Work

Baseline biochemical evaluation for etiologic differentiation in ACTH-dependent Cushing's syndrome: machine learning and composite score analysis in a multicenter cohort of 566 patients

Year: 2026

Type: article

Abstract: BACKGROUND: Limited availability of corticotropin-releasing hormone (CRH) currently complicates the differentiation of adrenocorticotropin (ACTH)-dependent Cushing's syndrome (CS). The diagnostic value of common screening tests in distinguishing Cushing's disease (CD) from ectopic CS (ECS) remains unclear. OBJECTIVE: To assess the diagnostic performance of screening tests, alone and in combination, in differentiating CD from ECS. METHODS: Retrospective multicenter study enrolling patients with confirmed ACTH-dependent CS and available screening tests at diagnosis. Data are expressed as multiples of upper limit of normal (×ULN). Optimal cut-offs were determined using Youden's Index. Combination with composite score models and machine learning algorithm were performed. RESULTS: A total of 566 patients were included (509 [90%] with CD). The optimal morning ACTH cut-off was 1.8×ULN (sensitivity 74%, specificity 77%, AUC=0.776 [CI-95% .688-.853]). 24h-urinary free cortisol (24h-UFC) showed the best performance (cut-off 5.9×ULN, sensitivity 72%, specificity 83%, AUC=0.854 [.816-.923]), followed by the 1 mg dexamethasone suppression test (12.8×ULN, sensitivity 70%, specificity 86%, AUC=.828 [.740-.906]). Using a composite score, a cut-off of 1.5 yielded 78% sensitivity and 92% specificity (AUC=0.865 [.798-.933]). Combining this score with findings derived from pituitary magnetic resonance imaging (MRI), sensitivity and specificity were 88% and 85% (AUC=0.931 [.887-.974]). Machine learning algorithm (balanced random forest) including screening tests-results yielded a sensitivity of 71% and specificity of 84% (AUC=0.853 [.741-.966]), while including the MRI-findings variant achieved a sensitivity and specificity of 76% and 91% (AUC=0.902 [.805-.999]). CONCLUSION: Combining screening tests in composite scoring and machine learning differentiates subtypes of ACTH-dependent CS and offers a potential diagnostic alternative to the CRH stimulation test.

Source: European Journal of Endocrinology

DOI: 10.1093/ejendo/lvag141