1235 Using Supervised Machine Learning to Validate a Novel Scoring System for the Prediction of Disease Remission of Functional Adenomas Following Transsphenoidal Resection

Abstract

INTRODUCTION: Functional pituitary adenomas are common primary central nervous system tumors with potent systemic endocrinological effects. Postoperatively, remission rates range from 65-98%. METHODS: 392 adult patients who underwent primary resection of a functional pituitary adenoma at the three Mayo Clinic between 2006-2022 were retrospectively reviewed. Multivariate logistic regression was performed to identify variables predictive of disease remission. Variables found statistically significant on multivariate analysis were incorporated into our proposed pituitary-SCHEME score. Machine learning models were implemented to compare the accuracy of the pituitary-SCHEME score against the multivariate models. After training (314 patients) and cross-validation of the machine learning models, an independent testing set of 78 patients was performed. SPSS V27 and R4.2.1 were used for statistical analyses. RESULTS: 261 (66.6%) patients achieved post-operative biochemical remission. On multivariate analysis gross-total resection and Knosp-Grade 0,I,II adenomas were predictive of disease remission following resection, while macroadenomas (adenoma > 10mm), male sex, mammosomatotroph and Mixed Growth-Hormone + Prolactin adenomas were predictive of continued disease. In machine learning models, without Pituitary-SCHEME score, the K-Nearest Neighbors (KNN) model achieved the highest accuracy at 75.6% followed by Naive Bayes model. An increase in model sensitivity was achieved with inclusion of Pituitary-SCHEME score with the Linear-Discriminant-Analysis (LDA) model achieving the highest accuracy at 86.9%, followed by the Classification and Regression Trees (CART) model. The random forest model had the largest AUC-ROC among models with and without pituitary-SCHEME score. Model prediction accuracy (with vs. without pituitary-SCHEME score) were found to be statistically different based on Wilcoxon rank sum testing (p < 0.0001). CONCLUSIONS: The novel pituitary-SCHEME score, which incorporates perioperative and endocrinological measures, has promise as a clinical tool to predict patient outcomes following surgical resection of functional adenomas.