| Autor: |
Marcus, A, Marcus, HJ, Camp, SJ, Nandi, D, Kitchen, N, Thorne, L |
| Zdroj: |
Journal of Neurology, Neurosurgery, & Psychiatry (JNNP); 2019, Vol. 90 Issue: 3 pe9-e9, 1p |
| Abstrakt: |
ObjectivesIn managing a patient with glioblastoma multiforme (GBM), a surgeon must weigh up whether sufficient tumour can be removed so that the patient can enjoy the benefits of decompression and cytoreduction, without impacting on the patient’s neurological status. In a previous study we identified the five most important anatomical features on a pre-operative MRI that are predictive of surgical resectability and used them to develop a grading system. The aim of this study was to apply an artificial neural network (ANN) to improve the prediction of surgical resectability.MethodsA prospectively maintained database was searched between February and August 2017 to identify all adult patients with supratentorial GBM that underwent resection. Pre-operative MRI scans were scored using the aforementioned grading system and post-operative scans assessed to determine the extent of resection. Performance of the standard grading system and ANN were then evaluated by analysing their Receiver Operator Characteristic curves; Area Under Curve (AUC) and accuracy were calculated and compared using the t-test with a value of p<0.05 considered significant.ResultsIn all, 47 patients were included, of which 18 (38.3%) were found to have complete excision. The AUC and accuracy were significantly greater using the ANN compared to the standard grading system (0.87 vs. 0.79 and 0.81 vs. 0.77 respectively; p<0.01 in both cases).ConclusionsAn ANN allows for improved prediction of surgical resectability in patients with GBM. |
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