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An integrated deep learning model based on multimodal magnetic resonance data for the diagnosis of postoperative recurrence of high-grade glioma

An integrated deep learning model based on multimodal magnetic resonance data for the diagnosis of postoperative recurrence of high-grade glioma

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200066580
Enrollment
Unknown
Registered
2022-12-09
Start date
2023-01-01
Completion date
Unknown
Last updated
2023-05-15

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

Conditions

High-level glioma

Interventions

Index test:Deep learning model
Gold Standard:pathology

Sponsors

Shenzhen No.2 People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: ? Patients with complete imaging data and confirmed tumor recurrence or pseudoprogression by pathological results. ? Complete imaging data are available, and the pseudoprogression determined according to RANO criteria.

Exclusion criteria

Exclusion criteria: ? The magnetic resonance images have obvious artifacts, and it is difficult to identify the lesions. ? Patients receiving adjuvant therapy other than STUPP.

Design outcomes

Primary

MeasureTime frame
Accuracy;Sensitivity;Specificity;Positive predictive value;Negative predictive value;Area under ROC curve (AUC);—

Countries

CHHINA

Contacts

Public Contactliu xiaolei

Shenzhen No.2 People's Hospital

435528252@qq.com13530653254

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026