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.
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.
Age ≥18 years old and ≤80 years old
patients with complete preoperative pelvic MRI images and postoperative pathology and clinical data in our hospital
Patients during pregnancy or breastfeeding, patients within 42 days of abortion
Patients who have received neoadjuvant chemotherapy or radiotherapy before surgery for this previous cervical cancer
Patients with other malignant tumors within 5 years
Combination of other underlying diseases that may lead to enlarged pelvic lymph nodes
All patient information and imaging data were collected from the Obstetrics and Gynecology Hospital of Fudan University. This hospital is a specialized tertiary care facility and is recognized as the leading Obstetrics and Gynecology hospital in China.
Other
Retrospective
Not listed
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.
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