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Non-invasive prediction of molecular subtypes and clinical prognosis in pediatric gliomas based on multi-scale radiomics and deep learning

Development of an Integrated System for Intelligent Diagnosis Decision-Making and Personalized Intervention in Brain Tumors

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2600119278
Enrollment
Unknown
Registered
2026-02-25
Start date
2026-03-13
Completion date
Unknown
Last updated
2026-03-02

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Pediatric gliomas

Interventions

Gold Standard:The diagnosis of pediatric glioma was confirmed by morphology combined with molecular pathology
Index test:Pathological grade, Ki-67 index

Sponsors

The First Affiliated Hospital, Fujian Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 19 Years

Inclusion criteria

Inclusion criteria: 1) Age < 19 years old; 2) Histological classification and diagnosis based on the WHO grading system; 3) Cranial MR plain scan + contrast - enhanced scan within 2 weeks prior to surgery; 4) Postoperative follow-up time of at least 2 years or follow-up time of less than 2 years with recurrence endpoint event with complete follow-up data; 5) Histological identification of Ki-67 index

Exclusion criteria

Exclusion criteria: 1) Poor preoperative MR image quality; 2) Incomplete clinical and pathological data; 3) History of prior treatment before surgery (resection, radiotherapy, or chemotherapy).

Design outcomes

Primary

MeasureTime frame
Accuracy;Sensitivity;Specificity;The area under the AUC curve, AUC;Positvie predictive value;Negative predictive value;

Countries

China

Contacts

Public ContactXiaorong Yan

The First Affiliated Hospital, Fujian Medical University

178603351@qq.com+86 186 0606 2268

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Mar 14, 2026