WHEN TO RESECT OR BIOPSY FOR PATIENTS WITH SUPRATENTORIAL GLIOBLASTOMA: A MULTIVARIABLE PREDICTION MODEL

Abstract Background The prospects of a patient with suspected glioblastoma may rely heavily on the indication for surgical resection versus biopsy only. Biopsy percentages vary considerably across hospitals and guidelines for treatment of glioblastoma lack criteria for surgical decision-making. To identify patient and tumor characteristics associated with the decision to resect or biopsy a glioblastoma and to develop and validate a prediction model for decision support. Material and Methods Clinical data and pre-operative MRI scans were collected for adults who underwent first-time surgery for supratentorial glioblastoma from a registry-based cohort study of 12 hospitals from the Netherlands, Germany, France, Italy, and the United States between 1st of January 2007 and 31st of December 2011. The main outcome was the type of surgical procedure: surgical resection or biopsy only. Predictors were patient- and tumor-related characteristics. Radiological factors were extracted from MRI using an automated tumor segmentation method. A prediction model was constructed using multivariable logistic regression analysis. The model was cross-validated and externally validated with a leave-one-hospital-out approach. Results Out of 1053 patients treated for glioblastoma, 28% underwent biopsy only. Biopsy rates varied from 15-40% across hospitals. The prediction model showed excellent discrimination with an average area under the curve of 0.86. Of the patient-related characteristics, younger age was associated more with resection and Karnofsky Performance Score of 60 or less with biopsy. Of the tumor-related characteristics, a location in the right hemisphere, unifocality, no tumor midline crossing, and no involvement of the cortical spinal tract, were associated with resection, as well as a high expected resectability index, a location in the right occipital lobe, and a higher percentage of tumor in Schaefer’s dorsal or ventral attention, limbic, and default networks. External validation proved acceptable to outstanding discrimination with areas under the curve ranging between 0.79 and 0.92 for hospitals. Conclusion A prediction model is presented and validated to support the decision to resect or to biopsy a patient with a suspected supratentorial glioblastoma. In this prediction model, tumor-related characteristics were more informative than patient-related factors. This may support surgical decision-making for individual patients, or facilitate comparisons of patient cohorts between surgeons or institutions.