FindTrial
Sign In

Deep learning for intraoperative prediction of primary central nervous system lymphoma and glioma: a discovery and multicenter validation study

Deep learning for intraoperative prediction of primary central nervous system lymphoma and glioma: a discovery and multicenter validation study

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2200064614
Enrollment
Unknown
Registered
2022-10-12
Start date
2022-10-02
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

Brain tumor

Interventions

Gold Standard:Formalin-fixed paraffin-embedded (FFPE) slides
Index test:LGNet?pathologist?AI-assiting pathologist?human-machine fusion?correted human-machine fusion

Sponsors

Sun Yat-Sen University Cancer Center
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. The intraoperative diagnosis of patients with glioma and PCNSL that were confirmed by FFPE sample diagnosis; 2. Available clinical information and H&E-stained frozen sample slides; 3. Finally, it was confirmed to be lymphoma or glioma through paraffin section and immunohistochemistry.

Exclusion criteria

Exclusion criteria: 1. WSI with poor quality such as out of focus, dull staining or obvious tissue folds; 2. Patients with incomplete materials and clinical data.

Design outcomes

Primary

MeasureTime frame
The area under the ROC curve, AUROC;Sensitivity;Specificity;—

Countries

China

Contacts

Public ContactMuyan Cai

Sun Yat-Sen University Cancer Center

caimy@sysucc.org.cn+86 020-87342775

Outcome results

None listed

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