J Neurol Surg B Skull Base 2023; 84(S 01): S1-S344
DOI: 10.1055/s-0043-1762122
Presentation Abstracts
Oral Abstracts

A Machine Learning Algorithm for Prediction of Facial Nerve Injury Following Microsurgical Resection of Vestibular Schwannoma

Authors

  • Sabrina M. Heman-Ackah

    1   Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States

  • Rachel Blue

    1   Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States

  • Alexandra E. Quimby

    2   Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, United States

  • Tiffany Hwa

    3   Department of Otolaryngology - Head and Neck Surgery, Temple University, Philadelphia, Pennsylvania, United States

  • Jason Brant

    2   Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, United States

  • Michael J. Ruckenstein

    2   Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, United States

  • Douglas C. Bigelow

    2   Department of Otorhinolaryngology, University of Pennsylvania, Philadelphia, Pennsylvania, United States

  • Selena E. Briggs

    4   Department of Otolaryngology, MedStar Washington Hospital Center, Washington, Dist. of Columbia, United States

  • Yale Cohen

    5   Department of Bioengineering, University of Pennsylvania, Philadelphia, Pennsylvania, United States

  • John Y. Lee

    1   Department of Neurosurgery, University of Pennsylvania, Philadelphia, Pennsylvania, United States
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    Introduction: Microsurgical resection of vestibular schwannomas (VS) poses a significant risk of injury to cranial nerve VII (facial nerve), which can have substantial negative impact on patients’ quality of life. Facial nerve preservation is therefore an important goal of surgery. However, facial nerve outcomes remain challenging to prognosticate, largely due to a paucity of reported relevant predictive factors.

    Objective: We endeavored to design a machine learning algorithm to decipher predictive factors relevant to facial nerve outcomes following microsurgical resection of VS.

    Methods: We constructed a database of patient-, tumor- and surgery-specific features via retrospective chart review of 138 consecutive patients who underwent microsurgical resection of VS between 2018 and 2021. This database was then used to train nonlinear supervised machine learning classifiers to predict facial nerve preservation, defined as House-Brackmann (HB) I versus facial nerve injury, defined as HB II–VI, as determined at 6-month outpatient follow-up.

    Results: Among the factors evaluated, only age demonstrated a statistically significant association with facial nerve outcome by independent t-test (mean age: 51.46 functionally preserved vs. 57.35 injured, difference 5.89: years, 95% CI: 0.7727–11.0073; p = 0.0339). A random forest classifier demonstrated the highest accuracy (85.7%) amongst algorithms tested for prediction of facial nerve injury, with a sensitivity of 80% and specificity of 90.1%. We identified age, body mass index (BMI) and intracanalicular tumor extension as novel prognosticators of facial nerve injury. Importantly, these same factors were identified when the algorithm was retrained according to facial nerve status at most-recent follow-up.

    Conclusion: 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 itself serving as a clinical tool, the factors identified can be further developed as a clinical scoring system for prospective facial nerve prognostication in VS microsurgery.


     

    No conflict of interest has been declared by the author(s).

    Publication History

    Article published online:
    01 February 2023

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