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Preoperative prediction of cervical cancer lymph node metastasis using deep learning model based multi omics technology

Preoperative prediction and influencing factor analysis of cervical cancer lymph node metastasis using deep learning model based multi omics technology - Multiomics techniques for predicting lymph node metastasis in cervical cancer

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400081731
Enrollment
Unknown
Registered
2024-03-11
Start date
2023-04-01
Completion date
Unknown
Last updated
2024-03-18

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

Conditions

cervical cancer

Interventions

Gold Standard:The gold standard for diagnosing cervical cancer lymph node metastasis is pathology diagnosis, which is based on postoperative pathology reports.
Index test:Radiomics features and pathological features selected through training test and validationt test, and establish a clinical prediction model based on clinical staging and tumor marker therap

Sponsors

the first affilitated hospital, the Air Force Medical University
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Women aged 18-80; 2. Confirmed as cervical cancer through surgery and pathology, and evaluated the status of pelvic lymph nodes; 3. Conduct imaging examinations within two weeks before surgery; 4. The clinical data is complete.

Exclusion criteria

Exclusion criteria: 1. Patients who have undergone neoadjuvant radiotherapy and chemotherapy before surgery; 2. Patients who have already experienced distant metastasis; 3. Merge with other malignant tumors.

Design outcomes

Primary

MeasureTime frame
Radiomics;pathologic features;AUC;accuracy;negative predictive value;positive predictive value;

Secondary

MeasureTime frame
Tumor markers;clinical stage;sensitivity;specificity;

Countries

China

Contacts

Public ContactJia Li

the first affilitated hospital, the Air Force Medical University

lijia219@yeah.net+86 188 2172 9828

Outcome results

None listed

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