Endometrial CancerNCT05783986
Developing a MRI-based Deep Learning Model to Predict MMR Status
An observational study of Endometrial Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
Trial summary
In order to develop a convenient, cheap and comprehensive method to preoperatively predict dMMR and reduce the number of people requiring dMMR-related immunohistochemical or genetic testing after surgery, this study aims to establish a deep learning model based on MRI to predict the MMR status of endometrial cancer. Read more about this study
- Who is it for?
- Any age, women only
- Conditions the study is for
- Endometrial Cancer
- What is being tested?
- randomly divided
- How many people
- 600 participants
- Where
- No sites listed yet
- Study dates
- 17 Apr 2023to 31 Dec 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
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 Any age
- Women only
- No healthy volunteers
- Child, Adult, Older adult
Study population: Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery
Who may be able to join
- Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery from 2017 to 2022
Who cannot join
- (1) There was no immunohistochemical detection result of MMR-related protein; (2) Radiotherapy and chemotherapy before MRI; (3) small tumors that are difficult to identify on the image (<5mm) ; (4) The T2-weighted imaging quality is insufficient to plot ROI, such as obvious motion artifacts; (5) There are other gynecological malignancies
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Developing a MRI-based Deep Learning Model to Predict MMR Status of Endometrial Carcinoma
In order to develop a convenient, cheap and comprehensive method to preoperatively predict dMMR and reduce the number of people requiring dMMR-related immunohistochemical or genetic testing after surgery, this study aims to establish a deep learning model based on MRI to predict the MMR status of endometrial cancer. Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery were collected. Deep learning was used to combine the clinical model with MR Image data to build the model. ROC curves were constructed for the testing group, internal verification group and external verification group, and the area under ROC curves were calculated to evaluate the diagnostic effect and stability of the model.
The dual threshold triage strategy was used to screen out the pMMR population (below the lower threshold), dMMR population (above the upper threshold) and the uncertain part of the population (between the thresholds).
Read the detailed description
In this study, patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery were collected from 2017 to 2022. It is expected to collect 500 cases in our hospital, which are divided into 375 cases (experimental group) and 125 cases (internal verification group).
100 cases of Sun Yat-sen University Cancer Center for external verification. Clinical data (age, gender, BMI, CA125, CA19-9, MR-T staging, immunohistochemical results of MMR-related proteins) of the study population were collected and logistics regression analysis was conducted to establish clinical models. Extract, segment, integrate and enhance MR Image data.
Deep learning was used to combine the clinical model with MR Image data to build the model. ROC curves were constructed for the testing group, internal verification group and external verification group, and the area under ROC curves were calculated to evaluate the diagnostic effect and stability of the model.
The dual threshold triage strategy was used to screen out the pMMR population (below the lower threshold), dMMR population (above the upper threshold) and the uncertain part of the population (between the thresholds). If the predictive score is above the lower threshold, the patient is advised to undergo further immunohistochemical or genetic testing to confirm MMR status or dMMR type
- Conditions
- Endometrial Cancer
- Being tested
- randomly divided
- Keywords
- deep learning, MMR, MRI
- Medical subject headings
- Endometrial Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
randomly dividedOther
500 patients of our hospital were randomly divided into testing group and internal validation group, and 100 patients in collabrative hospital were external validation group.
| Study group | Role | What they receive |
|---|---|---|
| Testing group | 375 patients of our hosipital,randomly divided. Receives: Other: randomly divided | |
| Internal validation group | 125 patients of our hosipital,randomly divided. Receives: Other: randomly divided | |
| External validation group | 100 patients of Sun Yat-sen University Cancer Center |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under receiver operating characteristic curve (AUROC)
The area under receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models
Measured: one year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2023
- Study start17 Apr 2023 (estimated)
- Primary completion30 Jun 2024 (estimated)
- Study completion31 Dec 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Endometrial CancerStudies 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
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sponsor type
- University, hospital or other
- Collaborators
- Sun Yat-sen University
- Responsible party
- Sponsor
- Investigators
- Jing Li, Principal investigator, Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05783986
- Sponsor's protocol ID
- SYSKY-2023-084-01
- Status last verified
- Mar 2023
- First submitted
- 13 Mar 2023
- Last updated
- 24 Mar 2023
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No: there is not a plan to make individual participant data (IPD) available to other researchers.
Study at a glance
From the registry record, last updated by the sponsor on 24 Mar 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Developing a MRI-based Deep Learning Model to Predict MMR Status of Endometrial Carcinoma
- ClinicalTrials.gov ID
- NCT05783986
- Acronym / sponsor ID
- SYSKY-2023-084-01
- Conditions
- Endometrial Cancer
- Keywords
- deep learning, MMR, MRI
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen UniversityUniversity, hospital or other
- Collaborators
- Sun Yat-sen University
- Study start
- 17 Apr 2023 (estimated)
- Primary completion
- 30 Jun 2024 (estimated)
- Study completion
- 31 Dec 2024 (estimated)
- First posted
- 24 Mar 2023 (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.
In order to develop a convenient, cheap and comprehensive method to preoperatively predict dMMR and reduce the number of people requiring dMMR-related immunohistochemical or genetic testing after surgery, this study aims to establish a deep learning model based on MRI to predict the MMR status of endometrial cancer. Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery were collected. Deep learning was used to combine the clinical model with MR Image data to build the model. ROC curves were constructed for the testing group, internal verification group and external verification group, and the area under ROC curves were calculated to evaluate the diagnostic effect and stability of the model.
The dual threshold triage strategy was used to screen out the pMMR population (below the lower threshold), dMMR population (above the upper threshold) and the uncertain part of the population (between the thresholds).
Detailed description
In this study, patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery were collected from 2017 to 2022. It is expected to collect 500 cases in our hospital, which are divided into 375 cases (experimental group) and 125 cases (internal verification group).
100 cases of Sun Yat-sen University Cancer Center for external verification. Clinical data (age, gender, BMI, CA125, CA19-9, MR-T staging, immunohistochemical results of MMR-related proteins) of the study population were collected and logistics regression analysis was conducted to establish clinical models. Extract, segment, integrate and enhance MR Image data.
Deep learning was used to combine the clinical model with MR Image data to build the model. ROC curves were constructed for the testing group, internal verification group and external verification group, and the area under ROC curves were calculated to evaluate the diagnostic effect and stability of the model.
The dual threshold triage strategy was used to screen out the pMMR population (below the lower threshold), dMMR population (above the upper threshold) and the uncertain part of the population (between the thresholds). If the predictive score is above the lower threshold, the patient is advised to undergo further immunohistochemical or genetic testing to confirm MMR status or dMMR type
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| Testing group | – | Other: randomly divided375 patients of our hosipital,randomly divided. |
| Internal validation group | – | Other: randomly divided125 patients of our hosipital,randomly divided. |
| External validation group | – | –100 patients of Sun Yat-sen University Cancer Center |
Who may qualify
- Sex
- Female only
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery
Key inclusion criteria (1 listed)
- Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery from 2017 to 2022
Key exclusion criteria (1 listed)
- (1) There was no immunohistochemical detection result of MMR-related protein; (2) Radiotherapy and chemotherapy before MRI; (3) small tumors that are difficult to identify on the image (<5mm) ; (4) The T2-weighted imaging quality is insufficient to plot ROI, such as obvious motion artifacts; (5) There are other gynecological malignancies
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 |
|---|---|---|
| randomly divided500 patients of our hospital were randomly divided into testing group and internal validation group, and 100 patients in collabrative hospital were external validation group. | Other | Internal validation group, Testing group |
Primary outcome
Area under receiver operating characteristic curve (AUROC)
Time frame: one year
The area under receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Mar 2023.
- Individual participant dataSharing planned: No. there is not a plan to make individual participant data (IPD) available to other researchers.
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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. This study is not recruiting at the moment.
Jing Li principal investigator
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Questions about this trial
What is this clinical trial studying?
An observational study of Endometrial Cancer, run by Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University.
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 1 inclusion and 1 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: status not confirmed, as of the sponsor's last update on 24 Mar 2023. Status can change between updates, so confirm with the study contact before making plans.
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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Jing Li (15915893493, lijing228@mail.sysu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under receiver operating characteristic curve (AUROC) (time frame: one year). 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 24 Mar 2023.