A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients’ quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored t...

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Bibliographic Details
Published in:Scientific reports Vol. 14; no. 1; pp. 12963 - 9
Main Authors: Heman-Ackah, Sabrina M., Blue, Rachel, Quimby, Alexandra E., Abdallah, Hussein, Sweeney, Elizabeth M., Chauhan, Daksh, Hwa, Tiffany, Brant, Jason, Ruckenstein, Michael J., Bigelow, Douglas C., Jackson, Christina, Zenonos, Georgios, Gardner, Paul, Briggs, Selena E., Cohen, Yale, Lee, John Y. K.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 05.06.2024
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/1305
692/308/409
692/499
Adult
Aged
Algorithms
Body mass index
Brain stem
Facial nerve
Facial Nerve Injuries - etiology
Female
Health risks
Humanities and Social Sciences
Humans
Learning algorithms
Machine Learning
Male
Microsurgery - adverse effects
Microsurgery - methods
Middle Aged
multidisciplinary
Neuroma, Acoustic - surgery
Patients
Prognosis
Quality of life
Random variables
Retrospective Studies
Schwann cells
Science
Science (multidisciplinary)
Tumors
Vestibular system
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary:Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the facial nerve, which can significantly impact patients’ quality of life. As facial nerve outcomes remain challenging to prognosticate, we endeavored to utilize machine learning to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS. A database of patient-, tumor- and surgery-specific features was constructed via retrospective chart review of 242 consecutive patients who underwent microsurgical resection of VS over a 7-year study period. This database was then used to train non-linear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I vs. facial nerve injury, defined as HB II–VI, as determined at 6-month outpatient follow-up. A random forest algorithm demonstrated 90.5% accuracy, 90% sensitivity and 90% specificity in facial nerve injury prognostication. A random variable (rv) was generated by randomly sampling a Gaussian distribution and used as a benchmark to compare the predictiveness of other features. This analysis revealed age, body mass index (BMI), case length and the tumor dimension representing tumor growth towards the brainstem as prognosticators of facial nerve injury. When validated via prospective assessment of facial nerve injury risk, this model demonstrated 84% accuracy. Here, we describe the development of a machine learning algorithm to predict the likelihood of facial nerve injury following microsurgical resection of VS. In addition to serving as a clinically applicable tool, this highlights the potential of machine learning to reveal non-linear relationships between variables which may have clinical value in prognostication of outcomes for high-risk surgical procedures.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-024-63161-1