Developing a MRI-based Deep Learning Model to Predict MMR Status
Study Details
Study Description
Brief 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. 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).
| Condition or Disease | Intervention/Treatment | Phase |
|---|---|---|
|
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
Arms and Interventions
| Arm | Intervention/Treatment |
|---|---|
| Testing group 375 patients of our hosipital,randomly divided. |
Other: 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. |
Other: 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 |
Outcome Measures
Primary Outcome Measures
- Area under receiver operating characteristic curve (AUROC) [one year]
The area under receiver operating characteristic curve (AUROC) was used to evaluate the performance of the models
Eligibility Criteria
Criteria
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
Contacts and Locations
Locations
No locations specified.Sponsors and Collaborators
- Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
- Sun Yat-sen University
Investigators
- Principal Investigator: Jing Li, Sun Yat-sen Memorial Hospital,Sun Yat-sen University
Study Documents (Full-Text)
None provided.More Information
Publications
None provided.- SYSKY-2023-084-01