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