AI-Assisted MRI Molecular Subtyping in Pediatric Brain Tumors

NCT07703605 | Not Yet Recruiting DMC

• 1 other identifier: KY2026-985
• Study Type: observational
• Target: 1,400
• Locations: 0 countries
• Sites: N/A

Brief Summary

This multicenter observational cohort study aims to develop and validate an artificial intelligence (AI)-assisted diagnostic system for preoperative molecular subtyping of pediatric brain tumors using routine magnetic resonance imaging (MRI). The study will include seven major pediatric brain tumor categories: glioma, medulloblastoma, ependymoma, atypical teratoid/rhabdoid tumor (AT/RT), intracranial germ cell tumors, craniopharyngioma, and choroid plexus tumors. The study includes a retrospective cohort for model development and internal/external validation, and a prospective cohort for further validation. Retrospective data will be collected from pediatric patients who underwent first surgical treatment between January 1, 2020 and December 31, 2025. Prospective enrollment will begin on July 15, 2026, with an anticipated sample size of 150 participants. The AI system will analyze preoperative MRI sequences, including T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR images, to predict key molecular markers and integrated diagnostic categories. The primary objective is to evaluate the diagnostic performance of the AI system for prespecified molecular prediction tasks using postoperative histopathology and molecular testing as the reference standard. Secondary objectives include assessing agreement with integrated diagnosis, comparing performance against blinded radiologists, and exploring prognostic associations of AI-predicted subgroups.

Conditions

Pediatric Brain Tumors; Glioma; Medulloblastoma; Ependymoma; Craniopharyngioma; Choroid Plexus Tumors

Keywords

Artificial Intelligence; Molecular Subtyping; Diagnostic Performance; Deep Learning; Pediatric Neuro-Oncology

Outcome Measures

Primary Outcomes (1)

• Area Under the Receiver Operating Characteristic Curve (AUC) for MRI-Based Prediction of Prespecified Molecular Markers

  Diagnostic discrimination of the AI-assisted system for binary prediction of prespecified key molecular markers or molecular subtypes from preoperative MRI, using postoperative histopathology and molecular testing as the reference standard. AUC values and 95% confidence intervals will be calculated for each prespecified molecular prediction task.

  Assessed at final model evaluation using all eligible retrospective cases collected from January 1, 2020 through December 31, 2025 and all eligible prospectively enrolled cases with available reference-standard data collected from July 15, 2026 through D

Secondary Outcomes (5)

• Agreement Between AI-Based Diagnosis and Integrated Final Diagnosis

  Assessed at final diagnostic adjudication for each eligible participant from study start on July 15, 2026 through study completion on December 30, 2029, including retrospective cases with complete reference-standard data.

• Comparative Diagnostic Performance of the AI System Versus Blinded Radiologists

  Assessed at blinded reader evaluation after completion of dataset curation and test set locking, anticipated by December 30, 2029.

• Macro-Average AUC for Seven-Class Tumor Classification

  Assessed at final model evaluation using eligible cases with complete imaging and reference-standard diagnostic data through December 30, 2029.

• Weighted F1 Score for Seven-Class Tumor Classification

  Assessed at final model evaluation using eligible cases with complete imaging and reference-standard diagnostic data through December 30, 2029.

• Sensitivity and Specificity for Prediction of Key Molecular Biomarkers

  Assessed at final model evaluation using cases with complete biomarker reference-standard results through December 30, 2029.

Study Arms (2)

Retrospective Cohort

Pediatric patients younger than 18 years who underwent first surgical treatment for one of the target brain tumors between January 1, 2020 and December 31, 2025, with available preoperative MRI and postoperative pathological confirmation. Data from this cohort will be used for model development and validation.

Prospective Cohort

Consecutively enrolled pediatric patients younger than 18 years meeting eligibility criteria from July 15, 2026 onward. Data from this cohort will be used for prospective validation of AI diagnostic performance.

Eligibility Criteria

• Age: 0 Years - 17 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17)
• Sampling Method: Non-Probability Sample

Study Population

Pediatric patients with suspected primary brain tumors who undergo first surgical treatment at participating centers and whose postoperative pathology confirms one of seven predefined tumor categories.

You may qualify if:

• Age younger than 18 years at the time of index surgery.
• Evaluated at a participating study center and scheduled for first surgical treatment of a suspected target pediatric brain tumor.
• Preoperative brain MRI available before surgery, including at minimum T1-weighted, contrast-enhanced T1-weighted, T2-weighted, and FLAIR sequences in DICOM format; MRI preferably performed within 7 days before surgery and before biopsy or tumor-directed therapy.
• Postoperative histopathology confirming one of the following target tumor categories: glioma, medulloblastoma, ependymoma, atypical teratoid/rhabdoid tumor, intracranial germ cell tumors, craniopharyngioma, or choroid plexus tumors.
• For the prospective cohort, written informed consent provided by a parent or legal guardian, with child assent obtained when appropriate according to age, understanding, and local ethics requirements.

You may not qualify if:

• Postoperative pathology confirming a non-target tumor type.
• Recurrent tumor, repeat surgery, or prior tumor-directed surgery before the index surgery.
• Preoperative MRI of inadequate quality for analysis, including severe motion artifact, severe susceptibility/metal artifact, or incomplete field of view.
• Prior biopsy, radiotherapy, chemotherapy, or other tumor-directed treatment before the index preoperative MRI that is judged to substantially affect imaging interpretation.
• Concurrent malignant disease other than the target brain tumor.
• Inability to comply with follow-up requirements in the prospective cohort, in the investigator's judgment, because of severe comorbidity or other practical limitations.

Sponsors & Collaborators

• Huashan Hospital: lead
• Xinhua Hospital, Shanghai Jiao Tong University School of Medicine: collaborator
• Quzhou Hospital Affiliated to Wenzhou Medical University: collaborator
• Ruitong Intelligent Medical (Shanghai) Technology Co., Ltd.: collaborator

MeSH Terms

Conditions

Glioma; Medulloblastoma; Ependymoma; Craniopharyngioma; Choroid Plexus Neoplasms

Condition Hierarchy (Ancestors)

Neoplasms, Neuroepithelial; Neuroectodermal Tumors; Neoplasms, Germ Cell and Embryonal; Neoplasms by Histologic Type; Neoplasms; Neoplasms, Glandular and Epithelial; Neoplasms, Nerve Tissue; Neuroectodermal Tumors, Primitive; Cerebral Ventricle Neoplasms; Brain Neoplasms; Central Nervous System Neoplasms; Nervous System Neoplasms; Neoplasms by Site; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases

Central Study Contacts

Lei Jin

CONTACT

86-21-52887200; ozlei91@126.com

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: OTHER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Prof

Study Record Dates

• First Submitted: July 8, 2026
• First Posted: July 14, 2026
• Study Start: July 15, 2026
• Primary Completion (Estimated): December 30, 2029
• Study Completion (Estimated): December 30, 2029
• Last Updated: July 23, 2026

Record last verified: 2026-07