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Developing a MRI-based Deep Learning Model to Predict MMR Status

NCT05783986 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 600

Last updated 2023-03-24

No results posted yet for this study

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).

Conditions

Interventions

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.

Sponsors & Collaborators

  • Sun Yat-sen University

    collaborator OTHER

  • Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

    lead OTHER

Principal Investigators

  • Jing Li · Sun Yat-Sen Memorial Hospital of Sun Yat-Sen University

Eligibility

Sex
FEMALE
Healthy Volunteers
No

Timeline & Regulatory

Start
2023-04-17
Primary Completion
2024-06-30
Completion
2024-12-31

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05783986 on ClinicalTrials.gov