Journal of the American College of Surgeons · 2026
Predictive Model for Mild Autonomous Cortisol Secretion in Patients Evaluated in Artificial Intelligence-Driven Adrenal Incidentaloma Clinic.
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Kostek Mehmet, McLeod Chandler, Kasmirski Julia, Negrete Haleigh, Tridandapani Srini, Cochran Micah, Osborne John D, Gillis Andrea, Chen Herbert, Lindeman Brenessa
Аннотация
BACKGROUND: Mild autonomous cortisol secretion (MACS) is present in approximately 20% to 50% of adrenal incidentalomas. These patients do not exhibit the clinical manifestations of overt Cushing's syndrome, and differentiation from nonfunctional adrenal incidentalomas (NFAI) is typically made after a low-dose dexamethasone suppression test. The objective of this study was to develop predictive models to distinguish MACS from NFAI using clinical and radiological parameters.
STUDY DESIGN: This retrospective study included patients evaluated in an adrenal incidentaloma clinic between February 2022 and August 2024 who were diagnosed with either NFAI or MACS. Demographic characteristics, medical and medication history, and radiological features were collected. Patients were randomly divided into training and test cohorts in a 3:1 ratio. Predictive models for MACS were developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) algorithms.
RESULTS: A total of 397 patients were included, with 297 allocated to the training set and the remaining 100 to the test set. The mean age was 62.3 years, and 55% of participants (n = 220) were women. MACS was present in 34% of the study population (n = 136). The most influential predictive factors of MACS were BMI, Posterior Adiposity Index, and the number of antihypertensive medications. The LASSO and RF models achieved discrimination with area under the curve values of 0.686 and 0.736, respectively. At Youden Index thresholds balancing sensitivity and selectivity, the LASSO model had 58.8% sensitivity, 75.8% specificity, and 70% accuracy, whereas the RF model had 64.7% sensitivity, 75.8% specificity, and 72% accuracy.
CONCLUSIONS: Predictive models incorporating clinical and radiological characteristics offer a promising approach for distinguishing MACS from NFAI in patients with adrenal incidentalomas.
STUDY DESIGN: This retrospective study included patients evaluated in an adrenal incidentaloma clinic between February 2022 and August 2024 who were diagnosed with either NFAI or MACS. Demographic characteristics, medical and medication history, and radiological features were collected. Patients were randomly divided into training and test cohorts in a 3:1 ratio. Predictive models for MACS were developed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and random forest (RF) algorithms.
RESULTS: A total of 397 patients were included, with 297 allocated to the training set and the remaining 100 to the test set. The mean age was 62.3 years, and 55% of participants (n = 220) were women. MACS was present in 34% of the study population (n = 136). The most influential predictive factors of MACS were BMI, Posterior Adiposity Index, and the number of antihypertensive medications. The LASSO and RF models achieved discrimination with area under the curve values of 0.686 and 0.736, respectively. At Youden Index thresholds balancing sensitivity and selectivity, the LASSO model had 58.8% sensitivity, 75.8% specificity, and 70% accuracy, whereas the RF model had 64.7% sensitivity, 75.8% specificity, and 72% accuracy.
CONCLUSIONS: Predictive models incorporating clinical and radiological characteristics offer a promising approach for distinguishing MACS from NFAI in patients with adrenal incidentalomas.