Predicting Cerebral Palsy in Infants With White Matter Injury Using MRI

Early Prediction of Cerebral Palsy by MRI in Infants With White Matter Injury: a Multicenter Study

The goal of this study is to determin the MRI features associated with cerebral palsy and to develop prediction models of pediatric disorders by combining MRI with artificial intelligence.

The main questions it aims to answer are:

• How to achieve features on conventional MRI associated with cerebral palsy?
• How to predict the risk of cerebral palsy in infants aged 6 to 2 years based on conventional MRI and deep learning? Researchers will compare characteristics of periventricular white matter injury with cerebral palsy to those without cerebral palsy.

Participants will be asked to provide MRI data, clinical diagnoses information, and follow-up outcomes.

Study Overview

• Status: Not yet recruiting
• Conditions: Cerebral Palsy; Periventricular White Matter Abnormalities
• Intervention / Treatment: Other: No intervention will be performed in this cohort study

Detailed Description

Cerebral palsy (CP) is a common group of movement disorders that often results in disability in children. In the context of CP, the importance of early diagnosis is crucial, but current diagnostic modalities often identify cases after the age of 2 years. After initial screening of infants at high risk for CP by behavioral scoring, magnetic resonance imaging (MRI) forms an integral part of the comprehensive evaluation. The training of conventional model of CP risk prediction requires a large investment of time and financial resources. The average sensitivity rate drops to 90%. Up to now, deep learning technology has been widely used in tasks related to image-based disease classification and has shown excellent performance.

Periventricular white matter injury (PVWMI) accounts for the largest proportion of various types of brain injuries in cerebral palsy, and the types of brain injuries in cerebral palsy are rich and complex, posing difficulties and challenges to deep learning models. Therefore, this study focuses on PVWMI, the most common type of cerebral palsy, and uses conventional MRI to develop a deep learning prediction model for CP in infants aged 6 months to 2 years old.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

• Study Contact: Name: Yitong Bian, MD; Phone Number: 15209220323; Email: bianyt0323@163.com

Study Locations

• China
  • Shaanxi
    • Xi'an, Shaanxi, China
      • The First Affiliated Hospital Of Xi'an Jiaotong University
      • Contact: Jian Yang, Ph.D.,M.D.; Phone Number: 0086-18991232396; Email: cjr.yangjian@vip.163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

This study will follow up infants from multiple regions and different hospitals who underwent MRI examinations between 6 months and 2 years old. Each infant and young child included T1-weighted imaging (T1WI) and T2-weighted imaging (T2WI). According to the follow-up outcomes, these infants will be divided into the following groups: cerebral palsy, other diagnoses that did not develop into cerebral palsy, or inability to confirm the diagnosis.

Description

Inclusion Criteria:

1. Infants and children at high risk of periventricular white matter injury (PVWMI) (gestational age <35 weeks, birth weight <2.6 kg, forceps-assisted delivery/fetal head attraction, Apgar score <7, hypoglycaemia, sepsis, electrolyte disturbances, premature rupture of membranes);
2. Those who underwent MRI at 6 months of age-2 years, including at least T1WI and T2WI sequences;
3. Upon follow-up, the patient's clinical diagnosis: cerebral palsy, other diagnoses that did not develop into cerebral palsy, or inability to confirm the diagnosis).

Exclusion Criteria:

1. Incomplete MRI images or unreadable images due to motion artefacts;
2. Incomplete neurobehavioural assessment data (including: gross motor function).

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
PVWMI Infants aged 6 months to 2 years; Infants will be scanned by MRI at the age of 6 months to 2 years. The infants of periventricular white matter injury (PVWMI) will be enrolled.	Other: No intervention will be performed in this cohort study; Deep learning classification models will be used for automatic prediction of cerebral palsy. Machines will be used to assist doctors in cerebral palsy risk evaluation.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy of the model predicting cerebral palsy	Determine the accuracy of PVWMI classification and cerebral palsy prediction. The higher the value, the better the model performance.	From September 2024 to December 2025

Collaborators and Investigators

• Sponsor: First Affiliated Hospital Xi'an Jiaotong University
• Collaborators: The First Affiliated Hospital of Henan University of Traditional Chinese Medicine; Shenzhen Children's Hospital; Zunyi Medical College; Wuxi Women's & Children's Hospital
• Investigators: Study Director: Jian Yang, Ph.D.,M.D, First Affiliated Hospital Xi'an Jiaotong University

Study record dates

Study Major Dates

• Study Start (Estimated): September 1, 2024
• Primary Completion (Estimated): December 31, 2025
• Study Completion (Estimated): December 31, 2025

Study Registration Dates

• First Submitted: August 26, 2024
• First Submitted That Met QC Criteria: August 26, 2024
• First Posted (Actual): August 28, 2024

Study Record Updates

• Last Update Posted (Actual): August 28, 2024
• Last Update Submitted That Met QC Criteria: August 26, 2024
• Last Verified: August 1, 2024

More Information

Terms related to this study

• Keywords: MRI; Cerebral palsy; Deep learning; Periventricular white matter injury
• Additional Relevant MeSH Terms: Pathologic Processes; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Neurologic Manifestations; Brain Damage, Chronic; Cerebral Palsy; Paralysis; Leukoaraiosis
• Other Study ID Numbers: XJTU1AF2024LSYY-154

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: The data will be available from the corresponding author upon reasonable request.
• IPD Sharing Time Frame: The data will be available after the paper published.
• IPD Sharing Access Criteria: The data can be used just for research purpose. Users can contact to the corresponding author through e-mail.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No