Cluster Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands.
Neurosurgical Center Amsterdam, Brain Tumor Center Amsterdam, Amsterdam University Medical Center, location VUmc, Amsterdam, The Netherlands.
PLoS One. 2019 Sep 27;14(9):e0222939. doi: 10.1371/journal.pone.0222939. eCollection 2019.
During resections of brain tumors, neurosurgeons have to weigh the risk between residual tumor and damage to brain functions. Different perspectives on these risks result in practice variation. We present statistical methods to localize differences in extent of resection between institutions which should enable to reveal brain regions affected by such practice variation.
Synthetic data were generated by simulating spheres for brain, tumors, resection cavities, and an effect region in which a likelihood of surgical avoidance could be varied between institutions. Three statistical methods were investigated: a non-parametric permutation based approach, Fisher's exact test, and a full Bayesian Markov chain Monte Carlo (MCMC) model. For all three methods the false discovery rate (FDR) was determined as a function of the cut-off value for the q-value or the highest density interval, and receiver operating characteristic and precision recall curves were created. Sensitivity to variations in the parameters of the synthetic model were investigated. Finally, all these methods were applied to retrospectively collected data of 77 brain tumor resections in two academic hospitals.
Fisher's method provided an accurate estimation of observed FDR in the synthetic data, whereas the permutation approach was too liberal and underestimated FDR. AUC values were similar for Fisher and Bayes methods, and superior to the permutation approach. Fisher's method deteriorated and became too liberal for reduced tumor size, a smaller size of the effect region, a lower overall extent of resection, fewer patients per cohort, and a smaller discrepancy in surgical avoidance probabilities between the different surgical practices. In the retrospective patient data, all three methods identified a similar effect region, with lower estimated FDR in Fisher's method than using the permutation method.
Differences in surgical practice may be detected using voxel statistics. Fisher's test provides a fast method to localize differences but could underestimate true FDR. Bayesian MCMC is more flexible and easily extendable, and leads to similar results, but at increased computational cost.
在脑肿瘤切除术中，神经外科医生必须权衡肿瘤残留和脑功能损伤的风险。对这些风险的不同看法导致了实践的差异。我们提出了统计方法来定位不同机构之间的切除范围差异，这应该能够揭示受这种实践差异影响的脑区。
通过模拟球体来生成脑、肿瘤、切除腔和一个手术回避可能性可以在机构之间变化的效应区，生成了合成数据。研究了三种统计方法：一种基于非参数置换的方法、Fisher 精确检验和完全贝叶斯马尔可夫链蒙特卡罗（MCMC）模型。对于所有三种方法，都确定了 q 值或最高密度区间的截断值作为 q 值的函数的错误发现率（FDR），并创建了接收者操作特性和精度召回曲线。研究了合成模型参数变化的敏感性。最后，将所有这些方法应用于两家学术医院的 77 例脑肿瘤切除的回顾性数据。
Fisher 方法在合成数据中提供了准确的观测 FDR 估计，而置换方法过于宽松且低估了 FDR。AUC 值对于 Fisher 和 Bayes 方法相似，并且优于置换方法。Fisher 方法对于肿瘤尺寸减小、效应区尺寸较小、整体切除范围较小、每个队列的患者较少以及不同手术实践之间手术回避概率的差异较小，表现较差且变得过于宽松。在回顾性患者数据中，所有三种方法都确定了一个类似的效应区，Fisher 方法的估计 FDR 低于置换方法。
可以使用体素统计检测手术实践的差异。Fisher 检验提供了定位差异的快速方法，但可能低估真实 FDR。贝叶斯 MCMC 更灵活且易于扩展，并且会产生相似的结果，但计算成本更高。
