SRS38 - Extent of resection in meningioma surgery: concordance and prognostic value of surgical, radiological, and volumetric assessments

Abstract Introduction Postoperative meningioma recurrence poses a significant clinical challenge. Extent of resection (EOR) is a key predictor, and the Simpson Grading (SG) scale has historically been utilised to estimate such. No consensus exists for which of SG, postoperative radiology report (RG), and residual tumour volume (RTV) best predicts recurrence. Aims (1) Evaluate the concordance between intraoperative and postoperative EOR and (2) determine which estimate has the greatest prognostic value for predicting tumour recurrence. Methods In this retrospective review of 270 patients, intraoperative EOR was compared to EOR from postoperative imaging. Agreement was assessed using Cohen’s Kappa and absolute agreement. Machine learning models (logistic and Cox regression) were developed to compare the prognostic performance of SG, RG, and RTV for predicting recurrence. Results Agreement between surgical and radiological EOR was substantial (κ = 0.704). Machine learning models demonstrated that imaging-based metrics consistently outperformed SG. RTV performed best for 3- and 5-year prediction (AUROC = 0.787, 0.709). RTV Cox models also performed best (C-index = 0.752). No significant recurrence risk difference was found between SG 1,2, and 3. Conclusions Objective, imaging-based metrics are superior to the subjective Simpson Grade for predicting meningioma recurrence, with quantitative residual tumour volume offering the most robust prognostic value. To our knowledge, this is the first study to directly compare these EOR estimators within a machine learning framework. These findings support a shift away from a reliance on the Simpson Grade and towards the integration of quantitative radiological data for more accurate postoperative risk stratification and patient management.