Cargando…
A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus
To develop and validate a mathematical model for predicting intracranial pressure (ICP) noninvasively using phase-contrast cine MRI (PC-MRI). We performed a retrospective analysis of PC-MRI from patients with communicating hydrocephalus (n = 138). The patients were recruited from Shenzhen Second Peo...
Autores principales: | , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Netherlands
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528454/ https://www.ncbi.nlm.nih.gov/pubmed/33001400 http://dx.doi.org/10.1007/s10877-020-00598-5 |
_version_ | 1783589264346316800 |
---|---|
author | Long, Jia Sun, Deshun Zhou, Xi Huang, Xianjian Hu, Jiani Xia, Jun Yang, Guang |
author_facet | Long, Jia Sun, Deshun Zhou, Xi Huang, Xianjian Hu, Jiani Xia, Jun Yang, Guang |
author_sort | Long, Jia |
collection | PubMed |
description | To develop and validate a mathematical model for predicting intracranial pressure (ICP) noninvasively using phase-contrast cine MRI (PC-MRI). We performed a retrospective analysis of PC-MRI from patients with communicating hydrocephalus (n = 138). The patients were recruited from Shenzhen Second People’s Hospital between November 2017 and April 2020, and randomly allocated into training (n = 97) and independent validation (n = 41) groups. All participants underwent lumbar puncture and PC-MRI in order to evaluate ICP and cerebrospinal fluid (CSF) parameters (i.e., aqueduct diameter and flow velocity), respectively. A novel ICP-predicting model was then developed based on the nonlinear relationships between the CSF parameters, using the Levenberg–Marquardt and general global optimisation methods. There was no significant difference in baseline demographic characteristics between the training and independent validation groups. The accuracy of the model for predicting ICP was 0.899 in the training cohort (n = 97) and 0.861 in the independent validation cohort (n = 41). We obtained an ICP-predicting model that showed excellent performance in the noninvasive diagnosis of clinically significant communicating hydrocephalus. |
format | Online Article Text |
id | pubmed-7528454 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Springer Netherlands |
record_format | MEDLINE/PubMed |
spelling | pubmed-75284542020-10-01 A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus Long, Jia Sun, Deshun Zhou, Xi Huang, Xianjian Hu, Jiani Xia, Jun Yang, Guang J Clin Monit Comput Original Research To develop and validate a mathematical model for predicting intracranial pressure (ICP) noninvasively using phase-contrast cine MRI (PC-MRI). We performed a retrospective analysis of PC-MRI from patients with communicating hydrocephalus (n = 138). The patients were recruited from Shenzhen Second People’s Hospital between November 2017 and April 2020, and randomly allocated into training (n = 97) and independent validation (n = 41) groups. All participants underwent lumbar puncture and PC-MRI in order to evaluate ICP and cerebrospinal fluid (CSF) parameters (i.e., aqueduct diameter and flow velocity), respectively. A novel ICP-predicting model was then developed based on the nonlinear relationships between the CSF parameters, using the Levenberg–Marquardt and general global optimisation methods. There was no significant difference in baseline demographic characteristics between the training and independent validation groups. The accuracy of the model for predicting ICP was 0.899 in the training cohort (n = 97) and 0.861 in the independent validation cohort (n = 41). We obtained an ICP-predicting model that showed excellent performance in the noninvasive diagnosis of clinically significant communicating hydrocephalus. Springer Netherlands 2020-10-01 2021 /pmc/articles/PMC7528454/ /pubmed/33001400 http://dx.doi.org/10.1007/s10877-020-00598-5 Text en © Springer Nature B.V. 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic. |
spellingShingle | Original Research Long, Jia Sun, Deshun Zhou, Xi Huang, Xianjian Hu, Jiani Xia, Jun Yang, Guang A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus |
title | A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus |
title_full | A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus |
title_fullStr | A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus |
title_full_unstemmed | A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus |
title_short | A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communicating hydrocephalus |
title_sort | mathematical model for predicting intracranial pressure based on noninvasively acquired pc-mri parameters in communicating hydrocephalus |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7528454/ https://www.ncbi.nlm.nih.gov/pubmed/33001400 http://dx.doi.org/10.1007/s10877-020-00598-5 |
work_keys_str_mv | AT longjia amathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT sundeshun amathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT zhouxi amathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT huangxianjian amathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT hujiani amathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT xiajun amathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT yangguang amathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT longjia mathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT sundeshun mathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT zhouxi mathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT huangxianjian mathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT hujiani mathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT xiajun mathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus AT yangguang mathematicalmodelforpredictingintracranialpressurebasedonnoninvasivelyacquiredpcmriparametersincommunicatinghydrocephalus |