improved prediction of surgical resectability in patients with glioblastoma using an artificial neural network in managing a patient with glioblastoma gbm a surgeon must carefully consider whether sufficient tumour can be removed so that the patient can enjoy the benefits of decompression and cytoreduction without impacting on the patients neurological status in a previous study we identified the five most important anatomical features on a preoperative mri that are predictive of surgical resectability and used them to develop a simple objective and reproducible grading system the objective of this study was to apply an artificial neural network ann to improve the prediction of surgical resectability in patients with gbm prospectively maintained databases were searched to identify adult patients with supratentorial gbm that underwent craniotomy and resection performance of the ann was evaluated against logistic regression and the standard grading system by analysing their receiver operator characteristic roc curves area under curve auc and accuracy were calculated and compared using wilcoxon signed rank test with a value of p005 considered statistically significant in all 135 patients were included of which 33 244 were found to have complete excision of all contrastenhancing tumour the auc and accuracy were significantly greater using the ann compared to the standard grading system 087 vs 079 and 83 vs 80 respectively p001 in both cases in conclusion an ann allows for the improved prediction of surgical resectability in patients with gbm