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.
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.
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.
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.
Cohort
Other
Not listed
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.
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.
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.
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.
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.
Click on the lead sponsor to view all of their trials.
Huashan Hospital
Lead sponsor
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
This trial is not recruiting at the moment. You can still explore other options:
