Improved prediction of postoperative pediatric cerebellar mutism syndrome using an artificial neural network.

Autor:	Sidpra J; University College London Medical School, London, UK.; Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, UK.; Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK., Marcus AP; Department of Brain Sciences and Computing, Imperial College London, London, UK., Löbel U; Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK., Toescu SM; Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.; Developmental Imaging and Biophysics Section, University College London Great Ormond Street Institute of Child Health, London, UK., Yecies D; Department of Neurosurgery, Lucile Packard Children's Hospital, Stanford, California, USA., Grant G; Department of Neurosurgery, Lucile Packard Children's Hospital, Stanford, California, USA., Yeom K; Department of Neuroradiology, Lucile Packard Children's Hospital, Stanford, California, USA., Mirsky DM; Department of Radiology, Children's Hospital Colorado, University of Colorado School of Medicine, Aurora, Colorado, USA., Marcus HJ; Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, UK.; Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK., Aquilina K; Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, UK.; Department of Neurosurgery, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK., Mankad K; Developmental Biology and Cancer Section, University College London Great Ormond Street Institute of Child Health, London, UK.; Department of Neuroradiology, Great Ormond Street Hospital for Children NHS Foundation Trust, London, UK.
Jazyk:	angličtina
Zdroj:	Neuro-oncology advances [Neurooncol Adv] 2022 Jan 10; Vol. 4 (1), pp. vdac003. Date of Electronic Publication: 2022 Jan 10 (Print Publication: 2022).
DOI:	10.1093/noajnl/vdac003
Abstrakt:	Background: 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). Methods: 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 P  < .05 considered statistically significant. Results: 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 ( P  < .05) and both external models ( P  < .001). Conclusion: Using an ANN, we show improved prediction of pCMS in comparison to previous models and conventional methods. (© The Author(s) 2022. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology.)
Databáze:	MEDLINE
Externí odkaz:	Zobrazit plný text záznamu