Abstract
BACKGROUND: Mild autonomous cortisol secretion (MACS) accounts for a significant proportion of adrenal incidentaloma. Current endocrinological screening tests for MACS are complex, particularly in areas with limited medical resources. This study aimed to develop a diagnostic tool based on leukocyte-related parameters to differentiate between MACS and non-functioning adrenal adenoma (NFA). METHODS: Inthis retrospective case-control study, propensity score-matching was used to select 567 patients from a cohort of 1108 patients (201 MACS, 907 NFA). External validation cohort included 52MACS and 48 NFA from two hospitals, which did not overlap with the modeling cohort patients. Leukocyte-related parameters were evaluated, and the diagnostic efficacy of each parameter was assessed by calculating Youden's J index (J) and the area under the curve (AUC). The study population was divided into training and testing samples using a 10-fold cross-validation method. Machine learning (ML) and classification and regression tree (CART) model were established. RESULTS: After propensity score matching, 567 patients were enrolled, including 197 MACS and 370 NFA. With the exception of basophil percentage, all other parameters differed significantly between the two groups. Lymphocyte count, lymphocyte percentage, eosinophils count, eosinophils percentage, and basophil percentage were lower in the MACS group compared to the NFA group. Eosinophils percentage demonstrated the highest AUC (0.650), with a sensitivity of 51.3% and specificity of 73.2%. The ML model, based on multiple parameters,exhibited better performance in diagnosing MACS (sensitivity 76%, specificity 77.4%, and AUC 0.818). A clinically usable CART model achieved an AUC of 0.872, with a sensitivity of 95% and a specificity of 75.7%. In the validation cohort, the prediction accuracy of the ML model and the CART model were 0.784 and 0.798, respectively. CONCLUSION: TheCART diagnostic model, constructed based on leukocyte-related parameters, could assist clinicians in distinguishing between MACS and NFA.