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

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.

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Key information

Conditions

Uterine Cervical Neoplasms Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Genital Diseases Genital Diseases, Female Genital Neoplasms, Female Neoplasms Neoplasms by Site Urogenital Diseases Urogenital Neoplasms Uterine Cervical Diseases Uterine Diseases Uterine Neoplasms

Age range

18 year–80 year

Sex eligibility

Female

Study type

Interventional

Phase

Not applicable

Primary location

The Obstetrics and Gynecology Hospital of Fudan University

Shanghai, Shanghai Municipality, 200090, China

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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.

Exclusion criteria

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.

Treatment and study plan

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

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.

Primary outcomes

Accuracy in determining pelvic lymph node metastasis

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.

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.

Sponsors and collaborators

Lead sponsor

Obstetrics & Gynecology Hospital of Fudan University

Other

Registry information

Official study 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

Important dates

Study start: 2024
Primary completion: 2027
Study completion: 2027
First posted: Aug 7, 2024
Registry last updated: Aug 7, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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