Abstract
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 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 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 p < 0.05 considered statistically significant. In all, 135 patients were included, of which 33 (24.4%) were found to have complete excision of all contrast-enhancing tumour. The AUC and accuracy were significantly greater using the ANN compared to the standard grading system (0.87 vs. 0.79 and 83% vs. 80% respectively; p < 0.01 in both cases). In conclusion, an ANN allows for the improved prediction of surgical resectability in patients with GBM.
Highlights
Defining the surgical resectability of GBM is inherently challenging
10 mm of the ventricles; bilateral location if the contrast-enhancing tumour extended into the corpus callosum; eloquent location if the contrast-enhancing tumour extended into motor or sensory cortex, language cortex, insula, or basal ganglia; large size if the diameter of the contrast-enhancing tumour exceeded 40 mm; and associated oedema if hypointensity extended more than 10 mm from the contrast-enhancing tumour[7]
We have demonstrated that use of the aforementioned artificial neural network (ANN) does improve the prediction of surgical resectability in patients with GBM
Summary
Defining the surgical resectability of GBM is inherently challenging. As early as 1928 Walter Dandy demonstrated that tumour cells infiltrate far beyond the clinically evident tumour mass[1]. Surgical resection of GBM continues to carry a significant risk of complications, with new neurological deficits occurring post-operatively in approximately one in ten patients[5,6]. In a previous study we performed a systematic review of the literature to identify the five most frequently cited anatomical features on a standard pre-operative contrast-enhanced T1-weighted MRI that are predictive of surgical resectability. We used these features to develop a simple, objective, and reproducible grading system (Table 1)[7]. The rate of complete of contrast-enhancing tumour varied widely from 3.4% in high complexity lesions to 50.0% in low complexity lesions[7]
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