A mathematical model for predicting intracranial pressure based on noninvasively acquired PC-MRI parameters in communica
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ORIGINAL RESEARCH
A mathematical model for predicting intracranial pressure based on noninvasively acquired PC‑MRI parameters in communicating hydrocephalus Jia Long1,2 · Deshun Sun3 · Xi Zhou2 · Xianjian Huang4 · Jiani Hu5 · Jun Xia2 · Guang Yang6,7 Received: 10 June 2020 / Accepted: 22 September 2020 © Springer Nature B.V. 2020
Abstract 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. Keywords Intracranial pressure · Phase-contrast cine MRI · Cerebrospinal fluid parameters · Communicating hydrocephalus · Levenberg–marquardt optimisation
Jia Long and Deshun Sun have contributed equally to this work. 3
Shenzhen Key Laboratory of Tissue Engineering, Shenzhen Laboratory of Digital Orthopedic Engineering, Guangdong Provincial Research Center for Artificial Intelligence and Digital Orthopedic Technology, Shenzhen Second People’s Hospital, Health Science Center, The First Hospital Affiliated To Shenzhen University, Shenzhen 518035, China
4
Department of Neurosurgery, Shenzhen Second People’s Hospital, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen 518035, China
Xianjian Huang [email protected]
5
Department of Radiology, School of Medicine, Wayne State University, Detroit, MI 48201, USA
Jiani Hu [email protected]
6
Cardiovascular Research Centre, Royal Brompton Hospital, London SW3 6NP, UK
Guang Yang [email protected]
7
National Heart and Lung Institute, Imperial College London, London SW7 2AZ, UK
* Jun Xia [email protected] Jia Long [email protected] Deshun Sun [email protected] Xi Zhou [email protected]
1
Department of Radiology, Pinghu Hospital Shenzhen University, Shenzhen, China
Department of Radiology, Shenzhen Second People’s Hospital, Health Science Center, The First Affiliated Hospital of Shenzhen University, Shenzhen, China
2
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