Development of machine learning models for predicting postoperative delayed remission of patients with Cushing's disease.

CONTEXT Postoperative hypercortisolemia mandates further therapy in patients with Cushing's disease (CD). Delayed remission (DR) is defined as not achieving postoperative immediate remission (IR), but having spontaneous remission during long-term follow-up. OBJECTIVE We aimed to develop and validate machine learning (ML) models for predicting DR in non-IR patients with CD. METHODS We enrolled 201 CD patients, and randomly divided them into training and test datasets. We then used the recursive feature elimination (RFE) algorithm to select features, and applied five ML algorithms to construct DR prediction models. We used permutation importance and local interpretable model-agnostic explanation (LIME) algorithms to determine the importance of the selected features and interpret the ML models. RESULTS Eighty-eight (43.8 %) of the 201 CD patients met the criteria for DR. Overall, patients who were younger, had low body-mass index, Knosp grade III-IV and a tumor not found by pathological examination tended to achieve a lower rate of DR. After RFE feature selection, the Adaboost model, which comprised 18 features, had the greatest discriminatory ability, and its predictive ability was significantly better than using Knosp grade and postoperative immediate morning serum cortisol (PoC). The results obtained from permutation importance and LIME algorithms showed that preoperative 24-hour urine free cortisol, PoC and age were the most important features, and showed the reliability and clinical practicability of Adaboost model in DC prediction. CONCLUSIONS ML-based models could serve as an effective non-invasive approach to predicting DR, and could aid in determining individual treatment and follow-up strategies for CD patients.