Artificial Intelligence Models as Predictors of Poor Outcomes in Transsphenoidal Pituitary Tumor Surgeries: A Diagnostic Test Accuracy Review

Pituitary tumors are the second most common type of brain tumor in adults, accounting for 17% of adult tumors. Over the years, endoscopic transsphenoidal surgery has become widely used in the treatment of pituitary tumors. With the recent advancement of Artificial Intelligence (AI) and Machine Learning (ML) technologies to generate clinical predictions that aid in diagnostic medicine, they have stood out as preoperative planning tools with the aim of predicting clinical outcomes, including postoperative ones. This article aims to evaluate the applicability of ML models in predicting postoperative complications in the postoperative period of endoscopic transsphenoidal surgeries for pituitary lesions. Following the PRISMA protocol. Articles were included if they had an observational or randomized design, reported on patients with skull base tumors (pituitary adenomas) who underwent transsphenoidal surgery, used machine learning-based prediction for major outcomes after surgery, and reported at least one relevant outcome. Eight articles were included in this analysis. The 2 outcomes analyzed were diabetes insipidus and cerebrospinal fluid (CSF) leak. The models studied were Random Forest, Neural Network, Logistic Regression, and Decision Tree, and they presented sensitivity, specificity, and an average area under the curve greater than 0.7. The best model was Random Forest with an average area under the curve of 0.832, followed by the Neural Network model, which had an average area under the curve of 0.8. In the studies we identified, the best machine learning methodology for identifying patients at risk of Diabetes Insipidus and cerebrospinal fluid leak was Random Forest.