Uterine Cervical Neoplasms
Conditions
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.
Interventions
DIAGNOSTIC_TESTAI 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_TESTConventional 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
Obstetrics & Gynecology Hospital of Fudan University
Study design
Allocation
NON_RANDOMIZED
Intervention model
FACTORIAL
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 80 Years
Healthy volunteers
No
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.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy in determining pelvic lymph node metastasis | 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. |
Countries
China
Outcome results
None listed