Developing a MRI-based Deep Learning Model to Predict MMR Status
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).
Study Overview
Status
Conditions
Intervention / Treatment
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 Type
Enrollment (Anticipated)
Contacts and Locations
Study Contact
- Name: Jing Li
- Phone Number: 15915893493
- Email: lijing228@mail.sysu.edu.cn
Participation Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
Description
Inclusion Criteria:
- Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI before surgery from 2017 to 2022
Exclusion Criteria:
- (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
Study Plan
How is the study designed?
Design Details
Cohorts and Interventions
Group / Cohort |
Intervention / Treatment |
|---|---|
|
Testing group
375 patients of our hosipital,randomly divided.
|
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.
|
|
Internal validation group
125 patients of our hosipital,randomly divided.
|
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.
|
|
External validation group
100 patients of Sun Yat-sen University Cancer Center
|
What is the study measuring?
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
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
|
one year
|
Collaborators and Investigators
Collaborators
Investigators
- Principal Investigator: Jing Li, Sun Yat-sen Memorial Hospital,Sun Yat-sen University
Study record dates
Study Major Dates
Study Start (Anticipated)
Primary Completion (Anticipated)
Study Completion (Anticipated)
Study Registration Dates
First Submitted
First Submitted That Met QC Criteria
First Posted (Actual)
Study Record Updates
Last Update Posted (Actual)
Last Update Submitted That Met QC Criteria
Last Verified
More Information
Terms related to this study
Keywords
Additional Relevant MeSH Terms
Other Study ID Numbers
- SYSKY-2023-084-01
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
IPD Plan Description
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.