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

Not Applicable

Not yet recruiting

• Conditions: Uterine Cervical Neoplasms
• Interventions: Diagnostic Test: AI Prediction Model; Diagnostic Test: Conventional Imageing Assessment

• Registration Number: NCT06541288
• Lead Sponsor: Obstetrics & Gynecology Hospital of Fudan University

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.

Detailed Description

Not available

Study Timeline

• Status Verified: 2024-08
• Study Start: 2024-08
• Study First Submitted: 2024-08-01
• Study First Submitted QC: 2024-08-06
• Last Update Submitted: 2024-08-06
• Study First Posted: 2024-08-07
• Last Update Posted: 2024-08-07
• Primary Completion: 2027-12
• Study Completion: 2027-12

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: Female
• Target Recruitment: 230

Inclusion Criteria

Not provided

Exclusion Criteria

Not provided

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: FACTORIAL

Arm && Interventions

Group	Intervention	Description
AI Prediction Model	AI Prediction Model	-
Conventional Imageing Assessment	Conventional Imageing Assessment	-

Primary Outcome Measures

Name	Time	Method
Accuracy in determining pelvic lymph node metastasis	The time frame was from subject enrollment until surgical pathology results were obtained. The time between subject enrollment and the availability of surgical pathology results was approximately 1 to 1.5 months.	After the subjects underwent surgical treatment, surgical pathology served as the gold standard for evaluating the accuracy of the AI predictive model in comparison to traditional imaging diagnosis. In the statistical analysis phase, sensitivity and specificity were utilized as the primary indicators to assess the accuracy of both diagnostic modalities.

Trial Locations

Locations (1)

The Obstetrics and Gynecology Hospital of Fudan University; 🇨🇳 Shanghai, Shanghai, China