Clinical application of the "sellar barrier's concept" for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning analysis

Abstract

Background: Recently, it was defined that the sellar barrier entity could be identified as a predictor of cerebrospinal fluid (CSF) intraoperative leakage. The aim of this study is to validate the application of the sellar barrier concept for predicting intraoperative CSF leak in endoscopic endonasal surgery for pituitary adenomas with a machine learning approach.

Methods: We conducted a prospective cohort study, from June 2019 to September 2020: data from 155 patients with pituitary subdiaphragmatic adenoma operated through endoscopic approach at the Division of Neurosurgery, Università degli Studi di Napoli "Federico II," were included. Preoperative magnetic resonance images (MRI) and intraoperative findings were analyzed. After processing patient data, the experiment was conducted as a novelty detection problem, splitting outliers (i.e., patients with intraoperative fistula, n = 11/155) and inliers into separate datasets, the latter further separated into training (n = 115/144) and inlier test (n = 29/144) datasets. The machine learning analysis was performed using different novelty detection algorithms [isolation forest, local outlier factor, one-class support vector machine (oSVM)], whose performance was assessed separately and as an ensemble on the inlier and outlier test sets.

Results: According to the type of sellar barrier, patients were classified into two groups, i.e., strong and weak barrier; a third category of mixed barrier was defined when a case was neither weak nor strong. Significant differences between the three datasets were found for Knosp classification score (p = 0.0015), MRI barrier: strong (p = 1.405 × 10^-6), MRI barrier: weak (p = 4.487 × 10^-8), intraoperative barrier: strong (p = 2.788 × 10^-7), and intraoperative barrier: weak (p = 2.191 × 10^-10). We recorded 11 cases of intraoperative leakage that occurred in the majority of patients presenting a weak sellar barrier (p = 4.487 × 10^-8) at preoperative MRI. Accuracy, sensitivity, and specificity for outlier detection were 0.70, 0.64, and 0.72 for IF; 0.85, 0.45, and 1.00 for LOF; 0.83, 0.64, and 0.90 for oSVM; and 0.83, 0.55, and 0.93 for the ensemble, respectively.

Conclusions: There is a true correlation between the type of sellar barrier at MRI and its in vivo features as observed during endoscopic endonasal surgery. The novelty detection models highlighted differences between patients who developed an intraoperative CSF leak and those who did not.

Keywords: CSF leak; machine learning; pituitary adenoma; sellar barrier; skull base surgery.

Figure 1

Strong sellar barrier. A 42-year-old male patient, with GH produce macroadenoma. (A,B) Preoperative MRI: the yellow arrows indicate the barrier that captures contrast with a thickness greater than 1 mm. (C–F) Intraoperative images: the barrier constituted by the gland can be seen.

Figure 2

Mixed sellar barrier. A 59-year-old female patient, with a PRL produced macroadenoma. (A,B) Preoperative MRI: the yellow arrows indicate the barrier that captures contrast with a thickness greater than 1 mm and the red arrows indicate the barrier that captures contrast with a thickness less than 1 mm. (C–F) Intraoperative images: the barrier constituted by duramater and arachnoid can be seen.

Figure 3

Weak sellar barrier. A 31-year-old female patient, with an ACTH produced macroadenoma. (A,B) Preoperative MRI: the red arrows indicate the barrier that captures contrast with a thickness less than 1 mm. (C–F) Intraoperative images: the barrier constituted only with arachnoid can be seen. The green arrow marks the CSF leak.

Figure 4

Model's decision function in relation to the distribution of inlier and outlier test set patients.