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

NCT06541288 | Not Yet Recruiting

• 1 other identifier: FUOBGY-2024-64
• Study Type: interventional
• Target: 230
• Locations: 1 country
• Sites: 1

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

Outcome Measures

Primary Outcomes (1)

• 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.

  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.

Study Arms (2)

AI Prediction Model

EXPERIMENTAL

Diagnostic Test: AI Prediction Model

Conventional Imageing Assessment

ACTIVE COMPARATOR

Diagnostic Test: Conventional Imageing Assessment

Interventions

AI Prediction Model 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

AI Prediction Model

Conventional Imageing Assessment 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.

Conventional Imageing Assessment

Eligibility Criteria

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

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 or sub-center;
• Age ≥18 years and ≤80 years;
• patients who underwent preoperative pelvic MRI (plain/enhanced) imaging in our hospital or sub-centers.

You may not qualify if:

• patients during pregnancy or lactation, patients with abortion within 42 days;
• patients who are undergoing or have undergone preoperative neoadjuvant chemotherapy or radiotherapy for this cervical cancer;
• Patients with other malignant tumors within 5 years;
• Combination of other underlying diseases that may lead to enlarged pelvic lymph nodes;
• patients whose preoperative pelvic MRI date is more than 1 month from the day of surgery;
• poor quality imaging images that are 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

Location

MeSH Terms

Conditions

Uterine Cervical Neoplasms

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

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: NON RANDOMIZED
• Masking: NONE
• Purpose: DIAGNOSTIC
• Intervention Model: FACTORIAL
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Deputy Chief of Gynecologic Oncology

Study Record Dates

• First Submitted: August 1, 2024
• First Posted: August 7, 2024
• Study Start: August 1, 2024
• Primary Completion (Estimated): December 1, 2027
• Study Completion (Estimated): December 1, 2027
• Last Updated: August 7, 2024

Record last verified: 2024-08

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)