A Supervised Machine learning-powered tool: intraoperative CSF leak predictor in endoscopic transsphenoidal surgery for pituitary adenomas.

Background: Despite advances in endoscopic transnasal transsphenoidal surgery (E-TNS) for pituitary adenomas (PAs), cerebrospinal fluid (CSF) leakage remains a life-threatening complication as it predisposes to meningitis and tension pneumocephalus. The purpose of the current study is to develop an accurate supervised ML model able to predict the risk of intraoperative CSF leakage by comparing different machine learning (ML) methods. Methods: A cohort of patients consecutively treated via E-TNS for PAs was selected. Clinical, radiological and endocrinological preoperative data were reviewed and elaborated through a features selecting algorithm. A customized pipeline of several ML models was programmed and trained in parallel; the best five models were included for further analyses. Selected risk factors were then used for training and hyperparameters optimization. Results: Intraoperative CSF leak occurred in 54 (22,6%) of 238 patients. Best risk’s predictors were: non secreting status, older age, x-, y- and z-axes diameters, ICD and R ratio. The random forest (RF) classifier outperformed other models, with an AUC of 0,84, high sensitivity (87%) and specificity (82%). Positive predictive value and negative predictive value were 69% and 93% respectively. F1 score was 0,87. Conclusion: A supervised machine learning prediction model able to identify patients at higher risk of intraoperative CSF leakage was trained and internally validated. The random forest classifier showed the best performance across all models selected by the authors. RF models might predict surgical outcomes in heterogeneous multimorbid and fragile populations outperforming classical statistical analyses and other machine learning models.