Pediatric Brain TumorsNCT07703605
AI-Assisted MRI Molecular Subtyping in Pediatric Brain Tumors
An observational study of Pediatric Brain Tumors and Glioma, run by Huashan Hospital.
Trial 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). Read more about this study
- Who is it for?
- Ages 0 to 17 years
- Conditions the study is for
- Pediatric Brain Tumors, Glioma, Medulloblastoma
- How many people
- 1,400 participants
- Where
- No sites listed yet
- Study dates
- 15 Jul 2026to 30 Dec 2029
- Phase
- Observational
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 0 to 17 years
- All sexes
- No healthy volunteers
- Child
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.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: AI-Assisted Presurgical MRI Molecular Subtyping for Pediatric Brain Tumors: A Single-Center Ambispective Clinical Cohort Study
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.
Read the detailed description
Pediatric brain tumors are the most common solid tumors in children and represent a highly heterogeneous group of diseases with marked variation in histology, molecular alterations, anatomic location, treatment response, and prognosis. Several molecular features, including H3K27M mutation, BRAF V600E mutation, ZFTA fusion, SMARCB1 loss, CTNNB1 mutation, and TP53 alteration, are clinically important for diagnostic classification, risk stratification, prognosis assessment, and treatment planning. However, most molecular characterization currently depends on postoperative tissue-based testing, and noninvasive preoperative prediction remains limited.
This study is designed to evaluate an AI-assisted MRI-based diagnostic system for pediatric brain tumors in a real-world multicenter observational setting. The study will include seven target tumor categories: glioma, medulloblastoma, ependymoma, atypical teratoid/rhabdoid tumor, intracranial germ cell tumors, craniopharyngioma, and choroid plexus tumors. The retrospective component will collect multimodal data, including clinical variables, preoperative MRI, pathology reports, molecular testing results, treatment information, and follow-up data, from eligible pediatric patients treated from January 1, 2020 through December 31, 2025. The prospective component will consecutively enroll eligible patients from July 15, 2026 onward for additional validation of model performance.
Preoperative MRI data will be preprocessed using standardized procedures, including bias field correction, skull stripping, isotropic resampling, and intensity normalization. The AI model will be developed to support classification of tumor type and prediction of key molecular subtypes/markers from presurgical MRI. Model performance will be evaluated using postoperative pathology and molecular testing as the reference standard. The primary endpoint is the area under the receiver operating characteristic curve (AUC) for prespecified molecular prediction tasks. Secondary analyses will evaluate agreement between AI output and integrated final diagnosis, comparative performance against blinded radiologists on independent test sets, multiclass tumor classification performance, biomarker-specific sensitivity and specificity, and progression-free survival stratified by AI-predicted subgroup.
This study is observational and is not intended for medical device registration. Biospecimen banking is not a registration objective of this study.
- Conditions
- Pediatric Brain Tumors, Glioma, Medulloblastoma, Ependymoma, Craniopharyngioma, Choroid Plexus Tumors
- Keywords
- Artificial Intelligence, Molecular Subtyping, Diagnostic Performance, Deep Learning, Pediatric Neuro-Oncology
- Medical subject headings
- Glioma, Medulloblastoma, Ependymoma, Craniopharyngioma, Choroid Plexus Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| 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. |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 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
Agreement between AI-predicted diagnostic output based on preoperative MRI and the integrated final diagnosis established from imaging, postoperative histopathology, and molecular testing. Agreement will be quantified using Cohen's kappa coefficient.
Measured: 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
Comparison of sensitivity, specificity, accuracy, and AUC between the AI system and radiologists blinded to pathology and molecular results on a predefined independent test dataset.
Measured: 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
Macro-average area under the receiver operating characteristic curve for classification of the seven target pediatric brain tumor categories.
Measured: 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
Weighted F1 score of the AI system for multiclass classification across glioma, medulloblastoma, ependymoma, atypical teratoid/rhabdoid tumor, intracranial germ cell tumors, craniopharyngioma, and choroid plexus tumors.
Measured: 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
Sensitivity and specificity of the AI system for prediction of tumor-specific biomarkers, including but not limited to H3K27M mutation, BRAF V600E mutation, ZFTA fusion, SMARCB1 loss, CTNNB1 mutation, and TP53 alteration, depending on tumor type and data availability.
Measured: Assessed at final model evaluation using cases with complete biomarker reference-standard results through December 30, 2029.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted14 Jul 2026
- Study start15 Jul 2026 (estimated)
- Primary completion30 Dec 2029 (estimated)
- Study completion30 Dec 2029 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Pediatric Brain TumorsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Other
- Enrolment
- 1,400 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Huashan Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Xinhua Hospital, Shanghai Jiao Tong University School of Medicine, Quzhou Hospital Affiliated to Wenzhou Medical University, Ruitong Intelligent Medical (Shanghai) Technology Co., Ltd.
- Responsible party
- Principal investigator, Jinsong Wu, Huashan Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07703605
- Sponsor's protocol ID
- KY2026-985
- Status last verified
- Jul 2026
- First submitted
- 8 Jul 2026
- Last updated
- 23 Jul 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided