Are We Overmonitoring Vestibular Schwannomas? Challenging the Necessity of Tracking Them All

OBJECTIVE: Increased MRI use for nonspecific symptoms has led to frequent detection of small or incidental vestibular schwannomas (VS), most of which never require treatment. This study aimed to develop and validate a stepwise model to stratify VS patients using early MRI findings and reduce unnecessary long-term surveillance. STUDY DESIGN: Retrospective multicenter cohort study with external validation. SETTING: Three tertiary referral centers in France (Strasbourg, Angers) and Switzerland (Frauenfeld). METHODS: Patients aged ≥18 years with sporadic VS and ≥3 consecutive MRIs before any intervention were included (2000-2020). Prior treatment, intralabyrinthine schwannomas, or incomplete imaging led to exclusion. A training cohort (n = 156) and an independent validation cohort (n = 32) were analyzed. Logistic regression and random forest models were used to identify predictors of treatment. A clinically applicable stepwise algorithm integrating baseline tumor volume, annual growth rate, and symptom presence was developed. RESULTS: The optimized model achieved 100% sensitivity in both cohorts, ensuring no patient requiring treatment was missed. Specificity was 74% in the training cohort and 48% in the validation cohort. After 1 follow-up MRI, surveillance could be safely discontinued in half of the cohorts (respectively 74% and 48% of patients). Larger baseline tumor volume, symptom presence, and higher growth rate were significant predictors of treatment (P < .05). CONCLUSION: A stratified, stepwise approach enables safe reduction of long-term MRI surveillance in approximately half of VS patients, supporting personalized follow-up and more efficient healthcare use.