A Prospective Cohort Study Comparing AI Prediction Model With Imaging Assessment to Diagnose Lymph Node Metastasis in Cervical Cancer

NCT ID: NCT06541288

Last Updated: 2024-08-07

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 230 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-08-31
• Study Completion Date: 2027-12-31

Brief Summary

The goal of this prospective cohort study is to learn whether artificial intelligence multimodal fusion prediction models are effective in diagnosing pelvic lymph node metastasis in cervical cancer. The main question it aims to answer is: can artificial intelligence multimodal fusion prediction models improve the accuracy of preoperative diagnosis of pelvic lymph node metastasis in cervical cancer? The researchers compared the AI multimodal fusion prediction model with traditional imaging physician assessments to see if the prediction model could yield more accurate lymph node metastasis determinations. Participants will undergo pelvic MRI after pathologically confirming a diagnosis of cervical cancer, and the results will be used to determine pelvic lymph node metastasis status by the predictive model and the imaging physician, respectively. Subsequent pathology results after surgical lymph node clearance will be used as the gold standard to determine the accuracy of the two preoperative lymph node diagnostic modalities.

Conditions

Uterine Cervical Neoplasms

Study Design

• Allocation Method: NON_RANDOMIZED
• Intervention Model: FACTORIAL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: NONE

Study Groups

AI Prediction Model

Group Type: EXPERIMENTAL

AI Prediction Model

Intervention Type: DIAGNOSTIC_TEST

Pelvic MRI was performed after pathologic diagnosis clarified the diagnosis of cervical cancer. Further pelvic lymph node metastasis status was determined by artificial intelligence multimodal fusion prediction modeling

Conventional Imageing Assessment

Group Type: ACTIVE_COMPARATOR

Conventional Imageing Assessment

Intervention Type: DIAGNOSTIC_TEST

Pelvic MRI was performed after pathologic diagnosis clarified the diagnosis of cervical cancer.Further pelvic MRI images are read by a specialized imaging physician to determine pelvic lymph node status.

Interventions

AI Prediction Model

Pelvic MRI was performed after pathologic diagnosis clarified the diagnosis of cervical cancer. Further pelvic lymph node metastasis status was determined by artificial intelligence multimodal fusion prediction modeling

Intervention Type: DIAGNOSTIC_TEST

Conventional Imageing Assessment

Pelvic MRI was performed after pathologic diagnosis clarified the diagnosis of cervical cancer.Further pelvic MRI images are read by a specialized imaging physician to determine pelvic lymph node status.

Intervention Type: DIAGNOSTIC_TEST

Eligibility Criteria

Inclusion Criteria

1. 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 or sub-center;

2. Age ≥18 years and ≤80 years;

3. patients who underwent preoperative pelvic MRI (plain/enhanced) imaging in our hospital or sub-centers.

Exclusion Criteria

1. patients during pregnancy or lactation, patients with abortion within 42 days;

2. patients who are undergoing or have undergone preoperative neoadjuvant chemotherapy or radiotherapy for this cervical cancer;

3. Patients with other malignant tumors within 5 years;

4. Combination of other underlying diseases that may lead to enlarged pelvic lymph nodes;

5. patients whose preoperative pelvic MRI date is more than 1 month from the day of surgery;

6. poor quality imaging images that are unrecognizable.

• Minimum Eligible Age: 18 Years
• Maximum Eligible Age: 80 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: No

Sponsors

Obstetrics & Gynecology Hospital of Fudan University

OTHER

Sponsor Role: lead

Responsible Party

Xin Wu

Deputy Chief of Gynecologic Oncology

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

The Obstetrics and Gynecology Hospital of Fudan University

Shanghai, Shanghai Municipality, China

Countries

China

Other Identifiers

FUOBGY-2024-64

Identifier Type: -

Identifier Source: org_study_id