Development of an Imaging Prediction Model for Pelvic Lymph Node Metastasis of Cervical Cancer Using Artificial Intelligence Techniques.
Study Overview
Status
Status
Conditions
Conditions
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Xin Wu
- Phone Number: (021)33189900
- Email: wuxin_fc@fudan.edu.cn
Study Locations
-
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Shanghai
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Shanghai, Shanghai, China, 200090
- Recruiting
- The Obstetrics and Gynecology Hospital of Fudan University
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Contact:
- Xin Wu
- Phone Number: 8613764046908
- Email: wuxin_fc@fudan.edu.cn
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Principal Investigator:
- Xin Wu
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Sub-Investigator:
- Hua Jiang
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Sub-Investigator:
- Xuan Yin
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Sub-Investigator:
- Ling Qiu
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Sub-Investigator:
- Hui Wang
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Sub-Investigator:
- Yang Liu
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Sub-Investigator:
- Shen Luo
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Sub-Investigator:
- Xiaomei Sun
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-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
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.
- Age ≥18 years old and ≤80 years old
- patients with complete preoperative pelvic MRI images and postoperative pathology and clinical data in our hospital
Exclusion criteria:
- 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
- Imaging report more than 1 month prior to surgery
- Poor image quality and unrecognizable
Study Plan
How is the study designed?
Design Details
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
A model for identifying pelvic lymph node metastases on preoperative imaging
Time Frame: From enrollment to the end of development of model at 24 months
|
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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From enrollment to the end of development of model at 24 months
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Pathologic Processes
- Neoplasms
- Urogenital Neoplasms
- Neoplasms by Site
- Uterine Neoplasms
- Genital Neoplasms, Female
- Uterine Cervical Diseases
- Uterine Diseases
- Neoplastic Processes
- Female Urogenital Diseases
- Female Urogenital Diseases and Pregnancy Complications
- Urogenital Diseases
- Genital Diseases
- Genital Diseases, Female
- Uterine Cervical Neoplasms
- Neoplasm Metastasis
- Lymphatic Metastasis
Other Study ID Numbers
Other Study ID Numbers
- FUOBGY2024-33
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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