IMG-02. Improved prediction of postoperative paediatric cerebellar mutism syndrome using an artificial neural network. (3rd June 2022) - Sidpra, Jai; Marcus, Adam P; Löbel, Ulrike; Toescu, Sebastian; Yecies, Derek; Grant, Gerald; Yeom, Kristen; Mirsky, David M; Marcus, Hani J; Aquilina, Kristian; Mankad, Kshitij
Abstract: BACKGROUND: Postoperative paediatric cerebellar mutism syndrome (pCMS) is a common but severe complication which may arise following the resection of posterior fossa tumours in children. Two previous studies have aimed to preoperatively predict pCMS, with varying results. In this work, we examine the generalisation of these models and determine if pCMS can be predicted more accurately using an artificial neural network (ANN). METHODS: An overview of reviews was performed to identify risk factors for pCMS, and a retrospective dataset collected as per these defined risk factors from children undergoing resection of primary posterior fossa tumours. The ANN was trained on this dataset and its performance evaluated in comparison to logistic regression and other predictive indices via analysis of receiver operator characteristic curves. Area under the curve (AUC) and accuracy were calculated and compared using a Wilcoxon signed rank test, with p<0.05 considered statistically significant. RESULTS: 204 children were included, of whom 80 developed pCMS. The performance of the ANN (AUC 0.949; accuracy 90.9%) exceeded that of logistic regression (p<0.05) and both external models (p<0.001). CONCLUSION: Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods.