Sidpra Jai, Marcus Adam P, Löbel Ulrike, Toescu Sebastian M, Yecies Derek, Grant Gerald, Yeom Kristen, Mirsky David M, Marcus Hani J, Aquilina Kristian, Mankad Kshitij
University College London Medical School, London, UK.
Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, UK.
Neurooncol Adv. 2022 Jan 10;4(1):vdac003. doi: 10.1093/noajnl/vdac003. eCollection 2022 Jan-Dec.
Postoperative pediatric cerebellar mutism syndrome (pCMS) is a common but severe complication that may arise following the resection of posterior fossa tumors in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalization of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN).
An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset was collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumors. The ANN was trained on this dataset and its performance was evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. The area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed-rank test, with < .05 considered statistically significant.
Two hundred and four children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression ( < .05) and both external models ( < .001).
Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.
小儿术后小脑缄默综合征（pCMS）是儿童后颅窝肿瘤切除术后可能出现的一种常见但严重的并发症。此前有两项研究旨在术前预测pCMS，但结果各异。在本研究中，我们检验了这些模型的通用性，并确定是否可以使用人工神经网络（ANN）更准确地预测pCMS。
进行综述以确定pCMS的危险因素，并根据这些定义的危险因素收集接受原发性后颅窝肿瘤切除术儿童的回顾性数据集。在该数据集上对ANN进行训练，并通过分析受试者工作特征曲线，将其性能与逻辑回归和其他预测指标进行比较。计算曲线下面积（AUC）和准确率，并使用Wilcoxon符号秩检验进行比较，P<0.05被认为具有统计学意义。
纳入204名儿童，其中80名发生pCMS。ANN的性能（AUC 0.949；准确率90.9%）超过了逻辑回归（P<0.05）和两个外部模型（P<0.001）。
与之前的模型和传统方法相比，使用ANN可提高对pCMS的预测能力。
