An observational study in Pediatric Brain Tumors, Glioma and Medulloblastoma, sponsored by Huashan Hospital. Not yet recruiting. Open to participants aged 0 Years to 17 Years. Per ClinicalTrials.gov, last updated 2026-07-23.
Sponsored by Huashan Hospital · Observational
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.
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.
1,452 studies on the registry are indexed under Glioma; 349 are open to participants now.
This study's planned enrollment of 1,400 is above the median of 90 across 273 observational studies indexed under Glioma.
Browse Glioma studies →Huashan Hospital is the lead sponsor of 247 studies on the registry; 104 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
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Exclusion Criteria:
Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.
Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Biopsy and archival tissue
A biopsy takes a small piece of tissue to examine. “Archival tissue” is a sample already taken, for example at diagnosis, and kept by the hospital.
Whether a stored sample will do depends on when it was taken, how much there is and what the study needs. Ask whether the study accepts your stored sample or needs a new biopsy.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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.
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.
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.
Time frame: 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
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: Assessed at final model evaluation using cases with complete biomarker reference-standard results through December 30, 2029.
No study locations are listed for this record.
Plan to share: Undecided
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Start is estimated for Jul 2026; primary completion for Dec 2029, 41 months later.
Of 113 non-industry observational studies under Glioma, half reached primary completion within 67 months of starting (the median), counting those still running; a quarter within 33 and three quarters within 104. This study's planned 41 months is below that.
Counting every sponsor, half of 123 studies reached it within 60 months; a quarter within 29 and three quarters within 104.
How these figures are worked out, and which studies are left out of them.
This study is not yet recruiting, as verified in Jul 2026. You cannot join it, but the record below documents what was studied.
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