A prediction model for recurrence after translabyrinthine surgery for vestibular schwannoma: toward personalized postoperative surveillance.

ABSTRACT

PURPOSE: The aim of this study is to compute and validate a statistical predictive model for the risk of recurrence, defined as regrowth of tumor necessitating salvage treatment, after translabyrinthine removal of vestibular schwannomas to individualize postoperative surveillance. METHODS: The multivariable predictive model for risk of recurrence was based on retrospectively collected patient data between 1995 and 2017 at a tertiary referral center. To assess for internal validity of the prediction model tenfold cross-validation was performed. A 'low' calculated risk of recurrence in this study was set at < 1%, based on clinical criteria and expert opinion. RESULTS: A total of 596 patients with 33 recurrences (5.5%) were included for analysis. The final prediction model consisted of the predictors 'age at time of surgery', 'preoperative tumor growth' and 'first postoperative MRI outcome'. The area under the receiver operating curve of the prediction model was 89%, with a C-index of 0.686 (95% CI 0.614-0.796) after cross-validation. The predicted probability for risk of recurrence was low (< 1%) in 373 patients (63%). The earliest recurrence in these low-risk patients was detected at 46 months after surgery. CONCLUSION: This study presents a well-performing prediction model for the risk of recurrence after translabyrinthine surgery for vestibular schwannoma. The prediction model can be used to tailor the postoperative surveillance to the estimated risk of recurrence of individual patients. It seems that especially in patients with an estimated low risk of recurrence, the interval between the first and second postoperative MRI can be safely prolonged.