A deep learning model to predict the molecular classification of endometrial cancer from haematoxylin and eosin-stained whole-slide images

Status

Recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2300079202

Enrollment

Unknown

Registered

2023-12-27

Start date

2024-01-01

Completion date

Unknown

Last updated

2024-01-14

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

Conditions

Endometrial cancer

Interventions

Deep learning models:Predictive models for molecular typing predictions

Eligibility

Sex/Gender

Female

Age

18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Patients with endometrial cancer whose initial treatment is surgery; 2. Any histological type; 3. Patients with preoperative hysteroscopic biopsy tissue examination; 4. Patients with endometrial cancer who have undergone molecular typing.

Exclusion criteria

Exclusion criteria: 1. Combined with other malignant tumors; 2. Patients whose pathological tissues are not available.

Design outcomes

Primary

Measure	Time frame
Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) Curve of the Predictive Model;	—

Secondary

Measure	Time frame
Positive predictive value;Negative predictive value;sensitivity;specificity;	—

Countries

China

Contacts

Public ContactCui Baoxia

Qilu Hospital of Shandong University

cuibaoxia@sdu.edu.cn+86 185 6008 1862

Outcome results

None listed

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