A Nomogram to Predict Recurrence-Free Survival Following Surgery for Vestibular Schwannoma

Zhang, Ze-Han; Zhang, Ding; Shi, Xudong; Tao, Bingyan; Liu, Yuyang; Zhang, Jun

doi:10.3389/fonc.2022.838112

Cited by 1 publication

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References 26 publications

(34 reference statements)

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“…Predictive models could be used to guide management decisions for VS by providing clinicians and patients with quantitative estimates of risk. For example, predictive models have been developed to predict VS growth, 18,19 VS recurrence following surgery, 20,21 and VS treatment response following radiation treatment. 22,23 In our study, the binary classification model could provide a probabilistic estimate of having vestibular dysfunction at the 3-, 6-, or 12-month marks; and the 4-way classification model would provide individual probabilities for each of these time points.…”

Section: Discussionmentioning

confidence: 99%

Development of a Predictive Model for Persistent Dizziness Following Vestibular Schwannoma Surgery

et al. 2023

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ObjectiveIn an era of vestibular schwannoma (VS) surgery where functional preservation is increasingly emphasized, persistent postoperative dizziness is a relatively understudied functional outcome. The primary objective was to develop a predictive model to identify patients at risk for developing persistent postoperative dizziness after VS resection.MethodsRetrospective review of patients who underwent VS surgery at our institution with a minimum of 12 months of postoperative follow‐up. Demographic, tumor‐specific, preoperative, and immediate postoperative features were collected as predictors. The primary outcome was self‐reported dizziness at 3‐, 6‐, and 12‐month follow‐up. Binary and multiclass machine learning classification models were developed using these features.ResultsA total of 1,137 cases were used for modeling. The median age was 67 years, and 54% were female. Median tumor size was 2 cm, and the most common approach was suboccipital (85%). Overall, 63% of patients did not report postoperative dizziness at any timepoint; 11% at 3‐month follow‐up; 9% at 6‐months; and 17% at 12‐months. Both binary and multiclass models achieved high performance with AUCs of 0.89 and 0.86 respectively. Features important to model predictions were preoperative headache, need for physical therapy on discharge, vitamin D deficiency, and systemic comorbidities.ConclusionWe demonstrate the feasibility of a machine learning approach to predict persistent dizziness following vestibular schwannoma surgery with high accuracy. These models could be used to provide quantitative estimates of risk, helping counsel patients on what to expect after surgery and manage patients proactively in the postoperative setting.Level of evidenceLevel IV Laryngoscope, 2023

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