Post-Operative Medium- and Long-Term Endocrine Outcomes in Patients with Non-Functioning Pituitary Adenomas—Machine Learning Analysis

SIMPLE SUMMARY: Non-functioning pituitary adenomas (NFPAs) may present with hypopituitarism, and patients may develop or continue to exhibit hypopituitarism following surgery or radiotherapy to treat NFPAs. Panhypopituitarism is characterised as a deficiency in most or all pituitary hormones. Prediction of post-intervention hypopituitarism is currently limited, with no accurate models existing for medium- to long-term prognosis. The aim of this study was to develop machine learning (ML) models towards improving prediction of hypopituitarism up to 15 years following surgical intervention for NFPAs. Pre-operative hormone levels were shown to be the best predictor of panhypopituitarism up to 1 year post-operatively. Endocrine tests performed up to 1 year post-operatively were shown to support strong predictive models for assessing the probability of panhypopituitarism at 5 and 10 years post-operatively. ABSTRACT: 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.