Queen Elizabeth Hospital, Lewisham and Greenwich NHS Trust, London, UK.
Wellcome EPSRC centre for Interventional and Surgical Sciences, University College London, London, UK.
Sci Rep. 2020 Mar 20;10(1):5143. doi: 10.1038/s41598-020-62160-2.
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.
在管理胶质母细胞瘤（GBM）患者时，外科医生必须仔细考虑是否可以切除足够的肿瘤，以使患者能够受益于减压和细胞减少，而不会影响患者的神经状态。在之前的一项研究中，我们确定了术前 MRI 上预测手术可切除性的五个最重要的解剖学特征，并使用它们开发了一种简单、客观和可重复的分级系统。本研究的目的是应用人工神经网络（ANN）来提高对 GBM 患者手术可切除性的预测。通过分析受试者工作特征（ROC）曲线、曲线下面积（AUC）和准确性，前瞻性地从数据库中搜索发现接受开颅术和切除术的幕上 GBM 成年患者。使用 ANN 对逻辑回归和标准分级系统进行了评估，并使用 Wilcoxon 符号秩检验进行了比较，p 值<0.05 被认为具有统计学意义。共纳入 135 例患者，其中 33 例（24.4%）发现所有增强肿瘤均完全切除。与标准分级系统相比，ANN 的 AUC 和准确性明显更高（分别为 0.87 与 0.79 和 83%与 80%；p 值均<0.01）。总之，ANN 可提高 GBM 患者手术可切除性的预测。
