FindTrial
Sign In

A Deep Learning-Based Staging Model in Endometrial Cancer using Conventional MRI and Clinical Features

A Deep Learning-Based Staging Model in Endometrial Cancer using Conventional MRI and Clinical Features

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000029361
Enrollment
Unknown
Registered
2020-01-26
Start date
2020-01-31
Completion date
Unknown
Last updated
2020-06-01

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

Conditions

endometrial cancer

Interventions

Gold Standard:Histopathological diagnosis.
Index test:A&#32
Deep&#32
Learning-Based&#32
Staging&#32
Model&#32
in&#32
Endometrial&#32
Cancer.

Sponsors

The First Affiliated Hospital, Xi'an Jiaotong University
Lead Sponsor

Eligibility

Sex/Gender
Female

Inclusion criteria

Inclusion criteria: 1. Primary endometrial cancer with a definitive histological diagnosis; 2. Available information regarding relevant clinical features; 3. Available information about preoperative MRI T1-weighted images (T1), T2-weighted images (T2) and DWI; 4. FIGO 2009 stage Ia and Ib; 5. All patients underwent radical resection of endometrial cancer.

Exclusion criteria

Exclusion criteria: Patients who had previously received systemic or local radiotherapy, systemic or local chemotherapy, anti-vascular therapy or surgery at a local hospital were excluded.

Design outcomes

Primary

MeasureTime frame
Depth of infiltrating muscularis;maximum tumor diameter;clinical features;SEN, SPE, ACC, AUC of ROC;—

Countries

China

Contacts

Public ContactQiling Li

The First Affiliated Hospital, Xi'an Jiaotong University

liqilinglady@163.com+86 18991232838

Outcome results

None listed

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