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

NCT06541288 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 230

Last updated 2024-08-07

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

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

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.

Sponsors & Collaborators

  • Obstetrics & Gynecology Hospital of Fudan University

    lead OTHER

Study Design

Allocation
NON_RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
FACTORIAL

Eligibility

Min Age
18 Years
Max Age
80 Years
Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-08-31
Primary Completion
2027-12-31
Completion
2027-12-31

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06541288 on ClinicalTrials.gov