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

NCT06541288NOT_YET_RECRUITINGNAINTERVENTIONAL

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.

Key Facts

Lead Sponsor

Obstetrics & Gynecology Hospital of Fudan University

Enrollment

230

Start Date

2024-08-01

Completion Date

2027-12-01

Study Type

INTERVENTIONAL

Official Title

A Prospective Cohort Study Comparing Artificial Intelligence Multimodal Fusion Prediction Models With Conventional Imaging Assessment for the Diagnosis of Pelvic Lymph Node Metastasis in Cervical Cancer

Interventions

AI Prediction ModelConventional Imageing Assessment

Conditions

Uterine Cervical Neoplasms

Eligibility

Age Range

18 Years – 80 Years

Sex

FEMALE

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.

Outcome Measures

Primary Outcomes

Accuracy in determining pelvic lymph node metastasis

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.

Time frame: 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.

Locations

The Obstetrics and Gynecology Hospital of Fudan University, Shanghai, China

Linked Diseases

Uterine Cervical Neoplasms

Uterine Cervical Neoplasms — a type of gynecological cancer.