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Trial Title:
Developing a MRI-based Deep Learning Model to Predict MMR Status
NCT ID:
NCT05783986
Condition:
Endometrial Cancer
Conditions: Official terms:
Endometrial Neoplasms
Conditions: Keywords:
deep learning, MMR, MRI
Study type:
Observational
Overall status:
Not yet recruiting
Study design:
Time perspective:
Retrospective
Intervention:
Intervention type:
Other
Intervention name:
randomly divided
Description:
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.
Arm group label:
Internal validation group
Arm group label:
Testing group
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).
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
Criteria for eligibility:
Study pop:
Patients diagnosed with endometrial cancer after surgery and who had completed pelvic MRI
before surgery
Sampling method:
Non-Probability Sample
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
Gender:
Female
Minimum age:
N/A
Maximum age:
N/A
Healthy volunteers:
No
Start date:
April 17, 2023
Completion date:
December 31, 2024
Lead sponsor:
Agency:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Agency class:
Other
Collaborator:
Agency:
Sun Yat-sen University
Agency class:
Other
Source:
Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University
Record processing date:
ClinicalTrials.gov processed this data on November 12, 2024
Source: ClinicalTrials.gov page:
https://clinicaltrials.gov/ct2/show/NCT05783986
