Development and validation of a conventional MRI-based model to predict cerebral palsy in infants (aged 6–24 months) with periventricular white matter injury: a multicentre, retrospective cohort studyResearch in context

Tingting Huang; Jie Zheng; Heng Liu; Haoxiang Jiang; Chao Jin; Xianjun Li; Liang Wu; Lei Zhang; Congcong Liu; Yitong Bian; Miaomiao Wang; Fan Wu; Xin Zhao; Shengli Shi; Fei Wang; Mengxuan Li; Linlin Zhu; Yuying Feng; Gang Zhang; Jian Yang

doi:10.1016/j.eclinm.2025.103364

EClinicalMedicine (Aug 2025)

Development and validation of a conventional MRI-based model to predict cerebral palsy in infants (aged 6–24 months) with periventricular white matter injury: a multicentre, retrospective cohort studyResearch in context

Tingting Huang,
Jie Zheng,
Heng Liu,
Haoxiang Jiang,
Chao Jin,
Xianjun Li,
Liang Wu,
Lei Zhang,
Congcong Liu,
Yitong Bian,
Miaomiao Wang,
Fan Wu,
Xin Zhao,
Shengli Shi,
Fei Wang,
Mengxuan Li,
Linlin Zhu,
Yuying Feng,
Gang Zhang,
Jian Yang

Affiliations

Tingting Huang: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Department of MRI, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Jie Zheng: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Heng Liu: Department of Radiology, The Affiliated Hospital of Zunyi Medical University, Zunyi, China
Haoxiang Jiang: Department of Radiology, Wuxi Children’s Hospital, Wuxi, China
Chao Jin: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Xianjun Li: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Liang Wu: Department of Children's Rehabilitation, Shanxi Rehabilitation Hospital, Xi’an, China
Lei Zhang: Department of Radiology, Baoji High-Hech Hospital, Baoji, China
Congcong Liu: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Yitong Bian: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Miaomiao Wang: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Fan Wu: Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China
Xin Zhao: Department of Medical Imaging, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, China
Shengli Shi: Department of Radiology, Henan Children’s Hospital Zhengzhou Children’s Hospital, Zhengzhou, China
Fei Wang: Department of Neurosurgery, Zhengzhou Second Hospital, Zhengzhou, China
Mengxuan Li: Department of Radiology, Tianjin First Central Hospital, Tianjin, China
Linlin Zhu: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Yuying Feng: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China
Gang Zhang: Department of MRI, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, China
Jian Yang: Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China; Xi’an Key Laboratory of Medical Computational Imaging, Xi’an, China; Shanxi Engineering Research Center of Computational Imaging and Medical Intelligence, Xi’an, China; Corresponding author. Department of Radiology, The First Affiliated Hospital of Xi’an Jiaotong University, No. 277 Yanta West Road, Xi’an 710061, China.

DOI: https://doi.org/10.1016/j.eclinm.2025.103364
Journal volume & issue: Vol. 86
p. 103364

Abstract

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Summary: Background: Periventricular white matter injury (PVWMI) is the most common form of brain injury and the leading cause of cerebral palsy (CP). Early prediction of CP within the first 2 years of life is crucial for timely and effective intervention. Early CP prediction tools for infants with PVWMI are lacking. This study aimed to develop and validate a conventional Magnetic Resonance Imaging (MRI)-based model to predict CP in infants with PVWMI. Methods: In this multicentre retrospective cohort study in China, infants with PVWMI who underwent conventional MRI between 6 and 24 months of corrected age (CA) were included from five hospitals and confirmed to have CP or non-CP by 5 years of age. Between April 2013 and September 2018, a multivariable regression logistic model was developed and internally validated using data from one hospital to identify significant independent MRI features associated with CP, followed by external validation across four other hospitals. A visual nomogram was constructed based on these factors. Predictive performance was evaluated via the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curves. Between October 2018 and January 2021, data from one hospital was included in a multiple readers test cohort (nine radiologists and two paediatric neurologists with varying experience) to assess the diagnostic performance and generalisability of the model. Subgroup analyses were conducted by age and sex. Findings: Across the two recruitment periods, 383 infants (65% male) with MRI-diagnosed PVWMI were included: 191 infants (122 with CP) in the derivation cohort, 115 (75 with CP) in the external validation cohort, and 77 (46 with CP) in the multiple readers test cohort. Five MRI features were associated with CP: abnormal signals in the posterior limb of the internal capsule (odds ratio [OR] 16.52; 95% confidence interval (CI) 5.78–52.67; P < 0.001), corticospinal tract in centrum semiovale (13.01; 3.49–62.30; P < 0.001), and cerebral peduncle (5.54; 1.20–32.15; P = 0.04), abnormal signals or atrophy in the thalamus (4.76; 1.41–19.32; P = 0.02) and lenticular nucleus (4.58; 1.24–21.35; P = 0.03). The model yielded an AUC of 0.94 (95% CI 0.91–0.98) in the derivation cohort. Similar AUCs were achieved in the internal (0.96 [0.93–0.99]) and external (0.92 [0.86–0.97]) validation cohorts. In the multiple readers test cohort, the average AUC, average sensitivity, and average specificity of 11 readers were 0.96 (95% CI 0.93–0.99), 0.90 (0.84–0.96), and 0.88 (0.77–0.98), respectively. Subgroup analyses were robust, yielding similar AUCs. Interpretation: The conventional MRI-based model showed good performance for predicting CP in infants aged 6–24 months with PVWMI and also had good diagnostic performance and generalisability, which may assist in identifying high-risk infants of CP and facilitating timely interventions. Future work with external validation in diverse countries and socioeconomic contexts are needed. Funding: National Natural Science Foundation of China, Key R&D Program of Shanxi Province, National Medical Centre Project of the First Affiliated Hospital of Xi’an Jiaotong University, Henan Provincial Health Commission National Traditional Chinese Medicine Clinical Research Base Scientific Research Special Fund, and Clinical Research Award of the First Affiliated Hospital of Xi'an Jiaotong University.

Published in EClinicalMedicine

ISSN: 2589-5370 (Online)
Publisher: Elsevier
Country of publisher: United Kingdom
LCC subjects: Medicine: Medicine (General)
Website: https://www.thelancet.com/journals/eclinm/home

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