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Trial Title:
A Prospective Cohort Study Comparing AI Prediction Model With Imaging Assessment to Diagnose Lymph Node Metastasis in Cervical Cancer
NCT ID:
NCT06541288
Condition:
Uterine Cervical Neoplasms
Conditions: Official terms:
Uterine Cervical Neoplasms
Lymphatic Metastasis
Study type:
Interventional
Study phase:
N/A
Overall status:
Not yet recruiting
Study design:
Allocation:
Non-Randomized
Intervention model:
Factorial Assignment
Primary purpose:
Diagnostic
Masking:
None (Open Label)
Intervention:
Intervention type:
Diagnostic Test
Intervention name:
AI Prediction Model
Description:
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
Arm group label:
AI Prediction Model
Intervention type:
Diagnostic Test
Intervention name:
Conventional Imageing Assessment
Description:
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.
Arm group label:
Conventional Imageing Assessment
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.
Criteria for eligibility:
Criteria:
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.
Gender:
Female
Minimum age:
18 Years
Maximum age:
80 Years
Healthy volunteers:
No
Locations:
Facility:
Name:
The Obstetrics and Gynecology Hospital of Fudan University
Address:
City:
Shanghai
Zip:
200090
Country:
China
Start date:
August 2024
Completion date:
December 2027
Lead sponsor:
Agency:
Obstetrics & Gynecology Hospital of Fudan University
Agency class:
Other
Source:
Obstetrics & Gynecology Hospital of Fudan University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT06541288
