Machine Learning May Be An Alternative To BIPSS In The Differential Diagnosis Of ACTH-Dependent Cushing's Syndrome.

OBJECTIVE:This study aimed to develop machine learning (ML) algorithms for the differential diagnosis of adrenocorticotropic hormone (ACTH)-dependent Cushing's syndrome (CS) based on biochemical and radiological features. METHODS:Logistic regression algorithms were used for ML, and the area under the receiver operating characteristics curve (AUROC) was used to measure performance. We used Shapley Contributed Comments (SHAP) values, which help explain the results of the ML models to identify the meaning of each feature and facilitate interpretation. RESULTS:A total of 106 patients, 80 with Cushing's disease (CD) and 26 with ectopic ACTH syndrome (EAS), were enrolled in the study. The ML task was created to classify patients with ACTH-dependent CS into CD and EAS. The average AUROC value obtained in the cross-validation of the logistic regression model created for the classification task was 0.850. The diagnostic accuracy of the algorithm was 86%. The SHAP values indicated that the most important determinants for the model were the 2-day 2-mg dexamethasone suppression test, the > 50% suppression in the 8-mg high-dose dexamethasone test, late-night salivary cortisol, and the diameter of the pituitary adenoma. We have also made our algorithm available to all clinicians via a user-friendly interface. CONCLUSION:ML algorithms have the potential to serve as an alternative decision support tool to invasive procedures in the differential diagnosis of ACTH-dependent CS.