Post-operative endocrine outcomes in patients with non-functioning
pituitary adenoma (NFPA) are variable. The aim of this study was to use machine learning (ML) models to better predict medium- and long-term post-operative
hypopituitarism in patients with NFPAs. We included data from 383 patients who underwent surgery with or without
radiotherapy for NFPAs, with a follow-up period between 6 months and 15 years. ML models, including k-nearest neighbour (KNN), support vector machine (SVM), and decision tree models, showed a superior ability to predict
panhypopituitarism compared with non-parametric statistical modelling (mean accuracy: 0.89; mean AUC-ROC: 0.79), with SVM achieving the highest performance (mean accuracy: 0.94; mean AUC-ROC: 0.88). Pre-operative endocrine function was the strongest feature for predicting
panhypopituitarism within 1 year post-operatively, while endocrine outcomes at 1 year post-operatively supported strong predictions of
panhypopituitarism at 5 and 10 years post-operatively. Other features found to contribute to
panhypopituitarism prediction were age, volume of tumour, and the use of
radiotherapy. In conclusion, our study demonstrates that ML models show potential in predicting post-operative
panhypopituitarism in the medium and long term in patients with NFPM. Future work will include incorporating additional, more granular data, including imaging and operative video data, across multiple centres.