1School of Medicine.
2Department of Surgery, and.
Neurosurg Focus. 2018 Nov 1;45(5):E5. doi: 10.3171/2018.8.FOCUS18325.
OBJECTIVEGlioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) are common intracranial pathologies encountered by neurosurgeons. They often may have similar radiological findings, making diagnosis difficult without surgical biopsy; however, management is quite different between these two entities. Recently, predictive analytics, including machine learning (ML), have garnered attention for their potential to aid in the diagnostic assessment of a variety of pathologies. Several ML algorithms have recently been designed to differentiate GBM from PCNSL radiologically with a high sensitivity and specificity. The objective of this systematic review and meta-analysis was to evaluate the implementation of ML algorithms in differentiating GBM and PCNSL.METHODSThe authors performed a systematic review of the literature using PubMed in accordance with PRISMA guidelines to select and evaluate studies that included themes of ML and brain tumors. These studies were further narrowed down to focus on works published between January 2008 and May 2018 addressing the use of ML in training models to distinguish between GBM and PCNSL on radiological imaging. Outcomes assessed were test characteristics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).RESULTSEight studies were identified addressing use of ML in training classifiers to distinguish between GBM and PCNSL on radiological imaging. ML performed well with the lowest reported AUC being 0.878. In studies in which ML was directly compared with radiologists, ML performed better than or as well as the radiologists. However, when ML was applied to an external data set, it performed more poorly.CONCLUSIONSFew studies have applied ML to solve the problem of differentiating GBM from PCNSL using imaging alone. Of the currently published studies, ML algorithms have demonstrated promising results and certainly have the potential to aid radiologists with difficult cases, which could expedite the neurosurgical decision-making process. It is likely that ML algorithms will help to optimize neurosurgical patient outcomes as well as the cost-effectiveness of neurosurgical care if the problem of overfitting can be overcome.
目的
胶质母细胞瘤（GBM）和原发性中枢神经系统淋巴瘤（PCNSL）是神经外科医生常见的颅内病变。它们通常可能具有相似的影像学表现，如果没有手术活检，诊断会很困难；然而，这两种病变的治疗方法却大不相同。最近，预测分析，包括机器学习（ML），因其在帮助诊断各种病变方面的潜力而受到关注。最近已经设计了几种 ML 算法来从影像学上区分 GBM 和 PCNSL，具有很高的敏感性和特异性。本系统回顾和荟萃分析的目的是评估 ML 算法在区分 GBM 和 PCNSL 中的应用。
方法
作者按照 PRISMA 指南进行了系统的文献综述，使用 PubMed 选择和评估了包括 ML 和脑瘤主题的研究。这些研究进一步缩小范围，重点关注 2008 年 1 月至 2018 年 5 月期间发表的关于在影像学上使用 ML 训练模型来区分 GBM 和 PCNSL 的工作。评估的结果是测试特征，如准确性、敏感性、特异性和接受者操作特征曲线下的面积（AUC）。
结果
确定了 8 项研究，涉及使用 ML 在训练分类器中区分 GBM 和 PCNSL 的影像学表现。ML 的表现非常好，报告的最低 AUC 为 0.878。在将 ML 与放射科医生进行直接比较的研究中，ML 的表现优于或与放射科医生一样好。然而，当 ML 应用于外部数据集时，它的表现就差了。
结论
很少有研究应用 ML 来解决仅通过影像学区分 GBM 和 PCNSL 的问题。在已发表的研究中，ML 算法已经显示出了有希望的结果，并且肯定有潜力帮助放射科医生处理困难病例，从而加快神经外科决策过程。如果能够克服过拟合问题，ML 算法很可能有助于优化神经外科患者的治疗效果，提高神经外科护理的成本效益。
