J Neurol Surg B Skull Base
DOI: 10.1055/s-0041-1740621
Original Article

Interpretable Machine Learning–Based Prediction of Intraoperative Cerebrospinal Fluid Leakage in Endoscopic Transsphenoidal Pituitary Surgery: A Pilot Study

Pier Paolo Mattogno*
1   Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
,
1   Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
,
Martina Giordano
1   Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
,
Quintino G. D'Alessandris
1   Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
,
Sabrina Chiloiro
2   Department of Endocrinology, Fondazione Policlinico Universitario A. Gemelli Istituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
,
Leonardo Tariciotti
3   Unit of Neurosurgery, Fondazione Istituto di Ricovero e Cura a Carattere Scientifico Cà Granda Ospedale Maggiore Policlinico, Milan, Italy
4   University of Milan, Milan, Italy
,
Alessandro Olivi
1   Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
,
Liverana Lauretti
1   Department of Neurosurgery, Fondazione Policlinico Universitario A. Gemell iIstituto di Ricovero e Cura a Carattere Scientifico Università Cattolica del Sacro Cuore, Rome, Italy
› Author Affiliations
Funding None.

Abstract

Purpose Transsphenoidal surgery (TSS) for pituitary adenomas can be complicated by the occurrence of intraoperative cerebrospinal fluid (CSF) leakage (IOL). IOL significantly affects the course of surgery predisposing to the development of postoperative CSF leakage, a major source of morbidity and mortality in the postoperative period. The authors trained and internally validated the Random Forest (RF) prediction model to preoperatively identify patients at high risk for IOL. A locally interpretable model-agnostic explanations (LIME) algorithm is employed to elucidate the main drivers behind each machine learning (ML) model prediction.

Methods The data of 210 patients who underwent TSS were collected; first, risk factors for IOL were identified via conventional statistical methods (multivariable logistic regression). Then, the authors trained, optimized, and audited a RF prediction model.

Results IOL reported in 45 patients (21.5%). The recursive feature selection algorithm identified the following variables as the most significant determinants of IOL: Knosp's grade, sellar Hardy's grade, suprasellar Hardy's grade, tumor diameter (on X, Y, and Z axes), intercarotid distance, and secreting status (nonfunctioning and growth hormone [GH] secreting). Leveraging the predictive values of these variables, the RF prediction model achieved an area under the curve (AUC) of 0.83 (95% confidence interval [CI]: 0.78; 0.86), significantly outperforming the multivariable logistic regression model (AUC = 0.63).

Conclusion A RF model that reliably identifies patients at risk for IOL was successfully trained and internally validated. ML-based prediction models can predict events that were previously judged nearly unpredictable; their deployment in clinical practice may result in improved patient care and reduced postoperative morbidity and healthcare costs.

Ethics Approval

Ethical approval was waived by the local Ethics Committee in view of the retrospective nature of the study and all the procedures being performed were part of the routine care.


Consent to Participate

Informed consent was obtained from all individual participants included in the study.


Availability of Data and Material

The dataset that supports the findings of this study are available from the corresponding author, M.G., on request.


Code Availability

The source code employed to develop the herein presented machine learning model is available at the following GitHub repository: https://github.com/valerio-mc/ML-fistola-pituitary .


* These authors contributed equally to the work.


Supplementary Material



Publication History

Received: 12 March 2021

Accepted: 12 November 2021

Publication Date:
16 January 2022 (online)

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