Development of an Imaging Prediction Model for Pelvic Lymph Node Metastasis of Cervical Cancer Using Artificial Intelligence Techniques.

NCT06448897 | Recruiting DMC

• 1 other identifier: FUOBGY2024-33
• Study Type: observational
• Target: 4,000
• Locations: 1 country
• Sites: 1

Brief Summary

This study is a retrospective exploratory trial conducted at a single center, aiming to develop and validate a preoperative lymphatic metastasis model for cervical cancer using artificial intelligence deep learning. The model is trained using preoperative imaging and postoperative pathological findings of cervical cancer patients, with the goal of enhancing the accuracy of lymphatic metastasis prediction through preoperative imaging and offering insights for treatment decisions.

Conditions

Cervical Cancer; Lymph Node Metastasis; Artificial Intelligence

Keywords

Cervical Cancer; Lymph Node Metastasis; Artificial Interlligence

Outcome Measures

Primary Outcomes (1)

• A model for identifying pelvic lymph node metastases on preoperative imaging

  Artificial intelligence (AI) technology was utilized to develop a model for identifying pelvic lymph node metastasis from preoperative images in order to enhance the accuracy of preoperative lymph node metastasis detection in cervical cancer. The model was validated with the main diagnostic focus on determining the status of lymph node metastasis.

  From enrollment to the end of development of model at 24 months

Eligibility Criteria

• Age: 18 Years - 80 Years
• Sex: female
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

All patient information and imaging data were collected from the Obstetrics and Gynecology Hospital of Fudan University. This hospital is a specialized tertiary care facility and is recognized as the leading Obstetrics and Gynecology hospital in China.

You may qualify if:

• patients with preoperative diagnosis of invasive cervical cancer stage I-III, with any type of pathology, and patients who underwent radical/modified radical cervical cancer surgery + pelvic lymph node dissection in our hospital.
• Age ≥18 years old and ≤80 years old
• patients with complete preoperative pelvic MRI images and postoperative pathology and clinical data in our hospital

You may not qualify if:

• Patients during pregnancy or breastfeeding, patients within 42 days of abortion
• Patients who have received neoadjuvant chemotherapy or radiotherapy before surgery for this previous cervical cancer
• Patients with other malignant tumors within 5 years
• Combination of other underlying diseases that may lead to enlarged pelvic lymph nodes
• Imaging report more than 1 month prior to surgery
• Poor image quality and unrecognizable

Sponsors & Collaborators

• Obstetrics & Gynecology Hospital of Fudan University: lead

Study Sites (1)

The Obstetrics and Gynecology Hospital of Fudan University

Shanghai, Shanghai Municipality, 200090, China

RECRUITING

MeSH Terms

Conditions

Uterine Cervical Neoplasms; Lymphatic Metastasis

Condition Hierarchy (Ancestors)

Uterine Neoplasms; Genital Neoplasms, Female; Urogenital Neoplasms; Neoplasms by Site; Neoplasms; Uterine Cervical Diseases; Uterine Diseases; Genital Diseases, Female; Female Urogenital Diseases; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Genital Diseases; Neoplasm Metastasis; Neoplastic Processes; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Xin Wu

CONTACT

(021)33189900; wuxin_fc@fudan.edu.cn

Study Design

• Study Type: observational
• Observational Model: OTHER
• Time Perspective: RETROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: June 3, 2024
• First Posted: June 7, 2024
• Study Start: February 1, 2024
• Primary Completion: December 1, 2025
• Study Completion: December 1, 2025
• Last Updated: June 7, 2024

Record last verified: 2024-06

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)