Uterine Cervical NeoplasmsNCT06541288
A Prospective Cohort Study Comparing AI Prediction Model With Imaging Assessment to Diagnose Lymph Node Metastasis in Cervical Cancer
A study testing AI Prediction Model and Conventional Imageing Assessment in people with Uterine Cervical Neoplasms, run by Obstetrics & Gynecology Hospital of Fudan University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 to 80 years, women only
- Conditions the study is for
- Uterine Cervical Neoplasms
- What is being tested?
- AI Prediction Model and Conventional Imageing Assessment
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 230 participants
- Where
- 1 siteChina
- Study dates
- Aug 2024to Dec 2027
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- Women only
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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;
- Age ≥18 years and ≤80 years;
- patients who underwent preoperative pelvic MRI (plain/enhanced) imaging in our hospital or sub-centers.
Who cannot join
- patients during pregnancy or lactation, patients with abortion within 42 days;
- patients who are undergoing or have undergone preoperative neoadjuvant chemotherapy or radiotherapy for this cervical cancer;
- Patients with other malignant tumors within 5 years;
- Combination of other underlying diseases that may lead to enlarged pelvic lymph nodes;
- patients whose preoperative pelvic MRI date is more than 1 month from the day of surgery;
- poor quality imaging images that are unrecognizable.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site
The Obstetrics and Gynecology Hospital of Fudan University
Shanghai, Shanghai Municipality, 200090
About this study
Official title: A Prospective Cohort Study Comparing Artificial Intelligence Multimodal Fusion Prediction Models With Conventional Imaging Assessment for the Diagnosis of Pelvic Lymph Node Metastasis in Cervical Cancer
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.
- Conditions
- Uterine Cervical Neoplasms
- Being tested
- AI Prediction Model, Conventional Imageing Assessment
- Medical subject headings
- Uterine Cervical Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI Prediction ModelDiagnostic test
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
Conventional Imageing AssessmentDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| AI Prediction Model | Experimental | Not described Receives: Diagnostic Test: AI Prediction Model |
| Conventional Imageing Assessment | Active comparator | Not described Receives: Diagnostic Test: Conventional Imageing Assessment |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy in determining pelvic lymph node metastasis
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.
Measured: 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.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Aug 2024
- Study startAug 2024 (estimated)
- Primary completionDec 2027 (estimated)
- Study completionDec 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Uterine Cervical NeoplasmsStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Factorial
- Masking
- None (open label)
- Enrolment
- 230 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Obstetrics & Gynecology Hospital of Fudan University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xin Wu, Obstetrics & Gynecology Hospital of Fudan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06541288
- Sponsor's protocol ID
- FUOBGY-2024-64
- Status last verified
- Aug 2024
- First submitted
- 1 Aug 2024
- Last updated
- 7 Aug 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 7 Aug 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- A Prospective Cohort Study Comparing Artificial Intelligence Multimodal Fusion Prediction Models With Conventional Imaging Assessment for the Diagnosis of Pelvic Lymph Node Metastasis in Cervical Cancer
- ClinicalTrials.gov ID
- NCT06541288
- Acronym / sponsor ID
- FUOBGY-2024-64
- Conditions
- Uterine Cervical Neoplasms
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 230 participants (estimated)
- Lead sponsor
- Obstetrics & Gynecology Hospital of Fudan UniversityUniversity, hospital or other
- Study start
- Aug 2024 (estimated)
- Primary completion
- Dec 2027 (estimated)
- Study completion
- Dec 2027 (estimated)
- First posted
- 7 Aug 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Non-randomised
- Intervention model
- Factorial
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AI Prediction Model | Experimental | Diagnostic Test: AI Prediction Model |
| Conventional Imageing Assessment | Active comparator | Diagnostic Test: Conventional Imageing Assessment |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- 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;
- Age ≥18 years and ≤80 years;
- patients who underwent preoperative pelvic MRI (plain/enhanced) imaging in our hospital or sub-centers.
Key exclusion criteria (6 listed)
- patients during pregnancy or lactation, patients with abortion within 42 days;
- patients who are undergoing or have undergone preoperative neoadjuvant chemotherapy or radiotherapy for this cervical cancer;
- Patients with other malignant tumors within 5 years;
- Combination of other underlying diseases that may lead to enlarged pelvic lymph nodes;
- patients whose preoperative pelvic MRI date is more than 1 month from the day of surgery;
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI Prediction ModelPelvic 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 test | AI Prediction Model |
| Conventional Imageing AssessmentPelvic 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. | Diagnostic test | Conventional Imageing Assessment |
Primary outcome
Accuracy in determining pelvic lymph node metastasis
Time frame: 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.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 7 Aug 2024.
- Individual participant dataSharing planned: No
Related trials for Uterine Cervical Neoplasms
Active, not recruitingObservational study
Vaginal Microbiome and HPV Pre-malignant and Cervical Dysplasia
Human Papilloma Virus, Vaginal Flora Imbalance
Recruiting
Hydrogel Spacer During Definitive Radiotherapy for Cervical Cancer
Cervical Cancer
Active, not recruiting
Self-collection for HPV Testing to Improve Cervical Cancer Prevention (SHIP) Trial (LMI-001-A-S03)
Cervical Carcinoma, Human Papillomavirus Infection
Active, not recruiting
Self-collection for HPV Testing to Improve Cervical Cancer Prevention (SHIP) Trial (LMI-001-A-S02)
Cervical Carcinoma, Human Papillomavirus Infection
RecruitingPhase 3
A Clinical Study of Sacituzumab Tirumotecan (MK-2870) in Combination With Pembrolizumab (MK-3475) as First-line Maintenance Treatment of Cervical Cancer (MK-2870-036/TroFuse-036/GOG-3123/ENGOT-cx22)
Cervical Cancer
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI Prediction Model and Conventional Imageing Assessment in people with Uterine Cervical Neoplasms, run by Obstetrics & Gynecology Hospital of Fudan University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 6 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 7 Aug 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy in determining pelvic lymph node metastasis (time frame: 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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 7 Aug 2024.