Development of an Imaging Prediction Model for Pelvic Lymph Node Metastasis of Cervical Cancer Using Artificial Intelligence Techniques.

Obstetrics & Gynecology Hospital of Fudan University4,000 enrolled

Overview

This study is a retrospective exploratory trial conducted at a single center, aiming to develop and validate a preoperative lymphatic metastasis model for cervical cancer using artificial intelligence deep learning. The model is trained using preoperative imaging and postoperative pathological findings of cervical cancer patients, with the goal of enhancing the accuracy of lymphatic metastasis prediction through preoperative imaging and offering insights for treatment decisions.

Study Type

OBSERVATIONAL

Enrollment

4,000

Conditions

Cervical Cancer Lymph Node Metastasis Artificial Intelligence

Eligibility

Sex: FEMALEMin age: 18 YearsMax age: 80 Years

Medical Language ↔ Plain English

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. 2. Age ≥18 years old and ≤80 years old 3. patients with complete preoperative pelvic MRI images and postoperative pathology and clinical data in our hospital Exclusion criteria: 1. Patients during pregnancy or breastfeeding, patients within 42 days of abortion 2. Patients who have received neoadjuvant chemotherapy or radiotherapy before surgery for this previous 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. Imaging report more than 1 month prior to surgery 6. Poor image quality and unrecognizable

Outcomes

Primary Outcomes

A model for identifying pelvic lymph node metastases on preoperative imaging

Artificial intelligence (AI) technology was utilized to develop a model for identifying pelvic lymph node metastasis from preoperative images in order to enhance the accuracy of preoperative lymph node metastasis detection in cervical cancer. The model was validated with the main diagnostic focus on determining the status of lymph node metastasis.

Time frame: From enrollment to the end of development of model at 24 months