Proposal and Validation of a Simple Grading Scale (TRANSSPHER Grade) for Predicting Gross Total Resection of Nonfunctioning Pituitary Macroadenomas After Transsphenoidal Surgery

Abstract BACKGROUND A simple, reliable grading scale to better characterize nonfunctioning pituitary adenomas (NFPAs) preoperatively has potential for research and clinical applications. OBJECTIVE To develop a grading scale from a prospective multicenter cohort of patients that accurately and reliably predicts the likelihood of gross total resection (GTR) after transsphenoidal NFPA surgery. METHODS Extent-of-resection (EOR) data from a prospective multicenter study in transsphenoidal NFPA surgery were analyzed (TRANSSPHER study; ClinicalTrials.gov NCT02357498). Sixteen preoperative radiographic magnetic resonance imaging (MRI) tumor characteristics (eg, tumor size, invasion measures, tumor signal characteristics, and parameters impacting surgical access) were evaluated to determine EOR predictors, to calculate receiver-operating characteristic curves, and to develop a grading scale. A separate validation cohort (n = 165) was examined to assess the scale's performance and inter-rater reliability. RESULTS Data for 222 patients from 7 centers treated by 15 surgeons were analyzed. Approximately one-fifth of patients (18.5%; 41 of 222) underwent subtotal resection (STR). Maximum tumor diameter > 40 mm; nodular tumor extension through the diaphragma into the frontal lobe, temporal lobe, posterior fossa, or ventricle; and Knosp grades 3 to 4 were identified as independent STR predictors. A grading scale (TRANSSPHER grade) based on a combination of these 3 features outperformed individual variables in predicting GTR (AUC, 0.732). In a validation cohort, the scale exhibited high sensitivity and specificity (AUC, 0.779) and strong inter-rater reliability (kappa coefficient, 0.617). CONCLUSION This simple, reliable grading scale based on preoperative MRI characteristics can be used to better characterize NFPAs for clinical and research purposes and to predict the likelihood of achieving GTR.